Identifying Engineering Students' English Sentence Reading Comprehension Errors: Applying a Data Mining Technique

ERIC Educational Resources Information Center

Tsai, Yea-Ru; Ouyang, Chen-Sen; Chang, Yukon

2016-01-01

The purpose of this study is to propose a diagnostic approach to identify engineering students' English reading comprehension errors. Student data were collected during the process of reading texts of English for science and technology on a web-based cumulative sentence analysis system. For the analysis, the association-rule, data mining technique…

Mining Tasks from the Web Anchor Text Graph: MSR Notebook Paper for the TREC 2015 Tasks Track

DTIC Science & Technology

2015-11-20

Mining Tasks from the Web Anchor Text Graph: MSR Notebook Paper for the TREC 2015 Tasks Track Paul N. Bennett Microsoft Research Redmond, USA pauben...anchor text graph has proven useful in the general realm of query reformulation [2], we sought to quantify the value of extracting key phrases from...anchor text in the broader setting of the task understanding track. Given a query, our approach considers a simple method for identifying a relevant

DrugQuest - a text mining workflow for drug association discovery.

PubMed

Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Theodosiou, Theodosios; Vizirianakis, Ioannis S; Iliopoulos, Ioannis

2016-06-06

Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases. Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank "Description", "Indication", "Pharmacodynamics" and "Mechanism of Action" text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface. DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest .

Mining Social Media and Web Searches For Disease Detection

PubMed Central

Yang, Y. Tony; Horneffer, Michael; DiLisio, Nicole

2013-01-01

Web-based social media is increasingly being used across different settings in the health care industry. The increased frequency in the use of the Internet via computer or mobile devices provides an opportunity for social media to be the medium through which people can be provided with valuable health information quickly and directly. While traditional methods of detection relied predominately on hierarchical or bureaucratic lines of communication, these often failed to yield timely and accurate epidemiological intelligence. New web-based platforms promise increased opportunities for a more timely and accurate spreading of information and analysis. This article aims to provide an overview and discussion of the availability of timely and accurate information. It is especially useful for the rapid identification of an outbreak of an infectious disease that is necessary to promptly and effectively develop public health responses. These web-based platforms include search queries, data mining of web and social media, process and analysis of blogs containing epidemic key words, text mining, and geographical information system data analyses. These new sources of analysis and information are intended to complement traditional sources of epidemic intelligence. Despite the attractiveness of these new approaches, further study is needed to determine the accuracy of blogger statements, as increases in public participation may not necessarily mean the information provided is more accurate. PMID:25170475

Mining social media and web searches for disease detection.

PubMed

Yang, Y Tony; Horneffer, Michael; DiLisio, Nicole

2013-04-28

Web-based social media is increasingly being used across different settings in the health care industry. The increased frequency in the use of the Internet via computer or mobile devices provides an opportunity for social media to be the medium through which people can be provided with valuable health information quickly and directly. While traditional methods of detection relied predominately on hierarchical or bureaucratic lines of communication, these often failed to yield timely and accurate epidemiological intelligence. New web-based platforms promise increased opportunities for a more timely and accurate spreading of information and analysis. This article aims to provide an overview and discussion of the availability of timely and accurate information. It is especially useful for the rapid identification of an outbreak of an infectious disease that is necessary to promptly and effectively develop public health responses. These web-based platforms include search queries, data mining of web and social media, process and analysis of blogs containing epidemic key words, text mining, and geographical information system data analyses. These new sources of analysis and information are intended to complement traditional sources of epidemic intelligence. Despite the attractiveness of these new approaches, further study is needed to determine the accuracy of blogger statements, as increases in public participation may not necessarily mean the information provided is more accurate.

Visual Based Retrieval Systems and Web Mining--Introduction.

ERIC Educational Resources Information Center

Iyengar, S. S.

2001-01-01

Briefly discusses Web mining and image retrieval techniques, and then presents a summary of articles in this special issue. Articles focus on Web content mining, artificial neural networks as tools for image retrieval, content-based image retrieval systems, and personalizing the Web browsing experience using media agents. (AEF)

ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials

PubMed Central

2012-01-01

Clinical trials are mandatory protocols describing medical research on humans and among the most valuable sources of medical practice evidence. Searching for trials relevant to some query is laborious due to the immense number of existing protocols. Apart from search, writing new trials includes composing detailed eligibility criteria, which might be time-consuming, especially for new researchers. In this paper we present ASCOT, an efficient search application customised for clinical trials. ASCOT uses text mining and data mining methods to enrich clinical trials with metadata, that in turn serve as effective tools to narrow down search. In addition, ASCOT integrates a component for recommending eligibility criteria based on a set of selected protocols. PMID:22595088

ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials.

PubMed

Korkontzelos, Ioannis; Mu, Tingting; Ananiadou, Sophia

2012-04-30

Clinical trials are mandatory protocols describing medical research on humans and among the most valuable sources of medical practice evidence. Searching for trials relevant to some query is laborious due to the immense number of existing protocols. Apart from search, writing new trials includes composing detailed eligibility criteria, which might be time-consuming, especially for new researchers. In this paper we present ASCOT, an efficient search application customised for clinical trials. ASCOT uses text mining and data mining methods to enrich clinical trials with metadata, that in turn serve as effective tools to narrow down search. In addition, ASCOT integrates a component for recommending eligibility criteria based on a set of selected protocols.

Deploying and sharing U-Compare workflows as web services.

PubMed

Kontonatsios, Georgios; Korkontzelos, Ioannis; Kolluru, Balakrishna; Thompson, Paul; Ananiadou, Sophia

2013-02-18

U-Compare is a text mining platform that allows the construction, evaluation and comparison of text mining workflows. U-Compare contains a large library of components that are tuned to the biomedical domain. Users can rapidly develop biomedical text mining workflows by mixing and matching U-Compare's components. Workflows developed using U-Compare can be exported and sent to other users who, in turn, can import and re-use them. However, the resulting workflows are standalone applications, i.e., software tools that run and are accessible only via a local machine, and that can only be run with the U-Compare platform. We address the above issues by extending U-Compare to convert standalone workflows into web services automatically, via a two-click process. The resulting web services can be registered on a central server and made publicly available. Alternatively, users can make web services available on their own servers, after installing the web application framework, which is part of the extension to U-Compare. We have performed a user-oriented evaluation of the proposed extension, by asking users who have tested the enhanced functionality of U-Compare to complete questionnaires that assess its functionality, reliability, usability, efficiency and maintainability. The results obtained reveal that the new functionality is well received by users. The web services produced by U-Compare are built on top of open standards, i.e., REST and SOAP protocols, and therefore, they are decoupled from the underlying platform. Exported workflows can be integrated with any application that supports these open standards. We demonstrate how the newly extended U-Compare enhances the cross-platform interoperability of workflows, by seamlessly importing a number of text mining workflow web services exported from U-Compare into Taverna, i.e., a generic scientific workflow construction platform.

Deploying and sharing U-Compare workflows as web services

PubMed Central

2013-01-01

Background U-Compare is a text mining platform that allows the construction, evaluation and comparison of text mining workflows. U-Compare contains a large library of components that are tuned to the biomedical domain. Users can rapidly develop biomedical text mining workflows by mixing and matching U-Compare’s components. Workflows developed using U-Compare can be exported and sent to other users who, in turn, can import and re-use them. However, the resulting workflows are standalone applications, i.e., software tools that run and are accessible only via a local machine, and that can only be run with the U-Compare platform. Results We address the above issues by extending U-Compare to convert standalone workflows into web services automatically, via a two-click process. The resulting web services can be registered on a central server and made publicly available. Alternatively, users can make web services available on their own servers, after installing the web application framework, which is part of the extension to U-Compare. We have performed a user-oriented evaluation of the proposed extension, by asking users who have tested the enhanced functionality of U-Compare to complete questionnaires that assess its functionality, reliability, usability, efficiency and maintainability. The results obtained reveal that the new functionality is well received by users. Conclusions The web services produced by U-Compare are built on top of open standards, i.e., REST and SOAP protocols, and therefore, they are decoupled from the underlying platform. Exported workflows can be integrated with any application that supports these open standards. We demonstrate how the newly extended U-Compare enhances the cross-platform interoperability of workflows, by seamlessly importing a number of text mining workflow web services exported from U-Compare into Taverna, i.e., a generic scientific workflow construction platform. PMID:23419017

The Voice of Chinese Health Consumers: A Text Mining Approach to Web-Based Physician Reviews

PubMed Central

Zhang, Kunpeng

2016-01-01

Background Many Web-based health care platforms allow patients to evaluate physicians by posting open-end textual reviews based on their experiences. These reviews are helpful resources for other patients to choose high-quality doctors, especially in countries like China where no doctor referral systems exist. Analyzing such a large amount of user-generated content to understand the voice of health consumers has attracted much attention from health care providers and health care researchers. Objective The aim of this paper is to automatically extract hidden topics from Web-based physician reviews using text-mining techniques to examine what Chinese patients have said about their doctors and whether these topics differ across various specialties. This knowledge will help health care consumers, providers, and researchers better understand this information. Methods We conducted two-fold analyses on the data collected from the “Good Doctor Online” platform, the largest online health community in China. First, we explored all reviews from 2006-2014 using descriptive statistics. Second, we applied the well-known topic extraction algorithm Latent Dirichlet Allocation to more than 500,000 textual reviews from over 75,000 Chinese doctors across four major specialty areas to understand what Chinese health consumers said online about their doctor visits. Results On the “Good Doctor Online” platform, 112,873 out of 314,624 doctors had been reviewed at least once by April 11, 2014. Among the 772,979 textual reviews, we chose to focus on four major specialty areas that received the most reviews: Internal Medicine, Surgery, Obstetrics/Gynecology and Pediatrics, and Chinese Traditional Medicine. Among the doctors who received reviews from those four medical specialties, two-thirds of them received more than two reviews and in a few extreme cases, some doctors received more than 500 reviews. Across the four major areas, the most popular topics reviewers found were the experience of finding doctors, doctors’ technical skills and bedside manner, general appreciation from patients, and description of various symptoms. Conclusions To the best of our knowledge, our work is the first study using an automated text-mining approach to analyze a large amount of unstructured textual data of Web-based physician reviews in China. Based on our analysis, we found that Chinese reviewers mainly concentrate on a few popular topics. This is consistent with the goal of Chinese online health platforms and demonstrates the health care focus in China’s health care system. Our text-mining approach reveals a new research area on how to use big data to help health care providers, health care administrators, and policy makers hear patient voices, target patient concerns, and improve the quality of care in this age of patient-centered care. Also, on the health care consumer side, our text mining technique helps patients make more informed decisions about which specialists to see without reading thousands of reviews, which is simply not feasible. In addition, our comparison analysis of Web-based physician reviews in China and the United States also indicates some cultural differences. PMID:27165558

The Voice of Chinese Health Consumers: A Text Mining Approach to Web-Based Physician Reviews.

PubMed

Hao, Haijing; Zhang, Kunpeng

2016-05-10

Many Web-based health care platforms allow patients to evaluate physicians by posting open-end textual reviews based on their experiences. These reviews are helpful resources for other patients to choose high-quality doctors, especially in countries like China where no doctor referral systems exist. Analyzing such a large amount of user-generated content to understand the voice of health consumers has attracted much attention from health care providers and health care researchers. The aim of this paper is to automatically extract hidden topics from Web-based physician reviews using text-mining techniques to examine what Chinese patients have said about their doctors and whether these topics differ across various specialties. This knowledge will help health care consumers, providers, and researchers better understand this information. We conducted two-fold analyses on the data collected from the "Good Doctor Online" platform, the largest online health community in China. First, we explored all reviews from 2006-2014 using descriptive statistics. Second, we applied the well-known topic extraction algorithm Latent Dirichlet Allocation to more than 500,000 textual reviews from over 75,000 Chinese doctors across four major specialty areas to understand what Chinese health consumers said online about their doctor visits. On the "Good Doctor Online" platform, 112,873 out of 314,624 doctors had been reviewed at least once by April 11, 2014. Among the 772,979 textual reviews, we chose to focus on four major specialty areas that received the most reviews: Internal Medicine, Surgery, Obstetrics/Gynecology and Pediatrics, and Chinese Traditional Medicine. Among the doctors who received reviews from those four medical specialties, two-thirds of them received more than two reviews and in a few extreme cases, some doctors received more than 500 reviews. Across the four major areas, the most popular topics reviewers found were the experience of finding doctors, doctors' technical skills and bedside manner, general appreciation from patients, and description of various symptoms. To the best of our knowledge, our work is the first study using an automated text-mining approach to analyze a large amount of unstructured textual data of Web-based physician reviews in China. Based on our analysis, we found that Chinese reviewers mainly concentrate on a few popular topics. This is consistent with the goal of Chinese online health platforms and demonstrates the health care focus in China's health care system. Our text-mining approach reveals a new research area on how to use big data to help health care providers, health care administrators, and policy makers hear patient voices, target patient concerns, and improve the quality of care in this age of patient-centered care. Also, on the health care consumer side, our text mining technique helps patients make more informed decisions about which specialists to see without reading thousands of reviews, which is simply not feasible. In addition, our comparison analysis of Web-based physician reviews in China and the United States also indicates some cultural differences.

Optimizing the Information Presentation on Mining Potential by using Web Services Technology with Restful Protocol

NASA Astrophysics Data System (ADS)

Abdillah, T.; Dai, R.; Setiawan, E.

2018-02-01

This study aims to develop the application of Web Services technology with RestFul Protocol to optimize the information presentation on mining potential. This study used User Interface Design approach for the information accuracy and relevance as well as the Web Service for the reliability in presenting the information. The results show that: the information accuracy and relevance regarding mining potential can be seen from the achievement of User Interface implementation in the application that is based on the following rules: The consideration of the appropriate colours and objects, the easiness of using the navigation, and users’ interaction with the applications that employs symbols and languages understood by the users; the information accuracy and relevance related to mining potential can be observed by the information presented by using charts and Tool Tip Text to help the users understand the provided chart/figure; the reliability of the information presentation is evident by the results of Web Services testing in Figure 4.5.6. This study finds out that User Interface Design and Web Services approaches (for the access of different Platform apps) are able to optimize the presentation. The results of this study can be used as a reference for software developers and Provincial Government of Gorontalo.

DISEASES: text mining and data integration of disease-gene associations.

PubMed

Pletscher-Frankild, Sune; Pallejà, Albert; Tsafou, Kalliopi; Binder, Janos X; Jensen, Lars Juhl

2015-03-01

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease-gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease-gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a web interface at http://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

PubMed Central

Harpaz, Rave; Callahan, Alison; Tamang, Suzanne; Low, Yen; Odgers, David; Finlayson, Sam; Jung, Kenneth; LePendu, Paea; Shah, Nigam H.

2014-01-01

Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. Text mining is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources—such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs—that are amenable to text-mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance. PMID:25151493

The Islamic State Battle Plan: Press Release Natural Language Processing

DTIC Science & Technology

2016-06-01

Processing, text mining , corpus, generalized linear model, cascade, R Shiny, leaflet, data visualization 15. NUMBER OF PAGES 83 16. PRICE CODE...Terrorism and Responses to Terrorism TDM Term Document Matrix TF Term Frequency TF-IDF Term Frequency-Inverse Document Frequency tm text mining (R...package=leaflet. Feinerer I, Hornik K (2015) Text Mining Package “tm,” Version 0.6-2. (Jul 3) https://cran.r-project.org/web/packages/tm/tm.pdf

Design and Implementation of a Comprehensive Web-based Survey for Ovarian Cancer Survivorship with an Analysis of Prediagnosis Symptoms via Text Mining

PubMed Central

Sun, Jiayang; Bogie, Kath M; Teagno, Joe; Sun, Yu-Hsiang (Sam); Carter, Rebecca R; Cui, Licong; Zhang, Guo-Qiang

2014-01-01

Ovarian cancer (OvCa) is the most lethal gynecologic disease in the United States, with an overall 5-year survival rate of 44.5%, about half of the 89.2% for all breast cancer patients. To identify factors that possibly contribute to the long-term survivorship of women with OvCa, we conducted a comprehensive online Ovarian Cancer Survivorship Survey from 2009 to 2013. This paper presents the design and implementation of our survey, introduces its resulting data source, the OVA-CRADLE™ (Clinical Research Analytics and Data Lifecycle Environment), and illustrates a sample application of the survey and data by an analysis of prediagnosis symptoms, using text mining and statistics. The OVA-CRADLE™ is an application of our patented Physio-MIMI technology, facilitating Web-based access, online query and exploration of data. The prediagnostic symptoms and association of early-stage OvCa diagnosis with endometriosis provide potentially important indicators for future studies in this field. PMID:25861211

Using text-mining techniques in electronic patient records to identify ADRs from medicine use.

PubMed

Warrer, Pernille; Hansen, Ebba Holme; Juhl-Jensen, Lars; Aagaard, Lise

2012-05-01

This literature review included studies that use text-mining techniques in narrative documents stored in electronic patient records (EPRs) to investigate ADRs. We searched PubMed, Embase, Web of Science and International Pharmaceutical Abstracts without restrictions from origin until July 2011. We included empirically based studies on text mining of electronic patient records (EPRs) that focused on detecting ADRs, excluding those that investigated adverse events not related to medicine use. We extracted information on study populations, EPR data sources, frequencies and types of the identified ADRs, medicines associated with ADRs, text-mining algorithms used and their performance. Seven studies, all from the United States, were eligible for inclusion in the review. Studies were published from 2001, the majority between 2009 and 2010. Text-mining techniques varied over time from simple free text searching of outpatient visit notes and inpatient discharge summaries to more advanced techniques involving natural language processing (NLP) of inpatient discharge summaries. Performance appeared to increase with the use of NLP, although many ADRs were still missed. Due to differences in study design and populations, various types of ADRs were identified and thus we could not make comparisons across studies. The review underscores the feasibility and potential of text mining to investigate narrative documents in EPRs for ADRs. However, more empirical studies are needed to evaluate whether text mining of EPRs can be used systematically to collect new information about ADRs. © 2011 The Authors. British Journal of Clinical Pharmacology © 2011 The British Pharmacological Society.

Using text-mining techniques in electronic patient records to identify ADRs from medicine use

PubMed Central

Warrer, Pernille; Hansen, Ebba Holme; Juhl-Jensen, Lars; Aagaard, Lise

2012-01-01

This literature review included studies that use text-mining techniques in narrative documents stored in electronic patient records (EPRs) to investigate ADRs. We searched PubMed, Embase, Web of Science and International Pharmaceutical Abstracts without restrictions from origin until July 2011. We included empirically based studies on text mining of electronic patient records (EPRs) that focused on detecting ADRs, excluding those that investigated adverse events not related to medicine use. We extracted information on study populations, EPR data sources, frequencies and types of the identified ADRs, medicines associated with ADRs, text-mining algorithms used and their performance. Seven studies, all from the United States, were eligible for inclusion in the review. Studies were published from 2001, the majority between 2009 and 2010. Text-mining techniques varied over time from simple free text searching of outpatient visit notes and inpatient discharge summaries to more advanced techniques involving natural language processing (NLP) of inpatient discharge summaries. Performance appeared to increase with the use of NLP, although many ADRs were still missed. Due to differences in study design and populations, various types of ADRs were identified and thus we could not make comparisons across studies. The review underscores the feasibility and potential of text mining to investigate narrative documents in EPRs for ADRs. However, more empirical studies are needed to evaluate whether text mining of EPRs can be used systematically to collect new information about ADRs. PMID:22122057

PPInterFinder--a mining tool for extracting causal relations on human proteins from literature.

PubMed

Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar

2013-01-01

One of the most common and challenging problem in biomedical text mining is to mine protein-protein interactions (PPIs) from MEDLINE abstracts and full-text research articles because PPIs play a major role in understanding the various biological processes and the impact of proteins in diseases. We implemented, PPInterFinder--a web-based text mining tool to extract human PPIs from biomedical literature. PPInterFinder uses relation keyword co-occurrences with protein names to extract information on PPIs from MEDLINE abstracts and consists of three phases. First, it identifies the relation keyword using a parser with Tregex and a relation keyword dictionary. Next, it automatically identifies the candidate PPI pairs with a set of rules related to PPI recognition. Finally, it extracts the relations by matching the sentence with a set of 11 specific patterns based on the syntactic nature of PPI pair. We find that PPInterFinder is capable of predicting PPIs with the accuracy of 66.05% on AIMED corpus and outperforms most of the existing systems. DATABASE URL: http://www.biomining-bu.in/ppinterfinder/

PPInterFinder—a mining tool for extracting causal relations on human proteins from literature

PubMed Central

Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar

2013-01-01

One of the most common and challenging problem in biomedical text mining is to mine protein–protein interactions (PPIs) from MEDLINE abstracts and full-text research articles because PPIs play a major role in understanding the various biological processes and the impact of proteins in diseases. We implemented, PPInterFinder—a web-based text mining tool to extract human PPIs from biomedical literature. PPInterFinder uses relation keyword co-occurrences with protein names to extract information on PPIs from MEDLINE abstracts and consists of three phases. First, it identifies the relation keyword using a parser with Tregex and a relation keyword dictionary. Next, it automatically identifies the candidate PPI pairs with a set of rules related to PPI recognition. Finally, it extracts the relations by matching the sentence with a set of 11 specific patterns based on the syntactic nature of PPI pair. We find that PPInterFinder is capable of predicting PPIs with the accuracy of 66.05% on AIMED corpus and outperforms most of the existing systems. Database URL: http://www.biomining-bu.in/ppinterfinder/ PMID:23325628

Text mining for adverse drug events: the promise, challenges, and state of the art.

PubMed

Harpaz, Rave; Callahan, Alison; Tamang, Suzanne; Low, Yen; Odgers, David; Finlayson, Sam; Jung, Kenneth; LePendu, Paea; Shah, Nigam H

2014-10-01

Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. It is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event (ADE) detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources-such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs-that are amenable to text mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance.

PubstractHelper: A Web-based Text-Mining Tool for Marking Sentences in Abstracts from PubMed Using Multiple User-Defined Keywords.

PubMed

Chen, Chou-Cheng; Ho, Chung-Liang

2014-01-01

While a huge amount of information about biological literature can be obtained by searching the PubMed database, reading through all the titles and abstracts resulting from such a search for useful information is inefficient. Text mining makes it possible to increase this efficiency. Some websites use text mining to gather information from the PubMed database; however, they are database-oriented, using pre-defined search keywords while lacking a query interface for user-defined search inputs. We present the PubMed Abstract Reading Helper (PubstractHelper) website which combines text mining and reading assistance for an efficient PubMed search. PubstractHelper can accept a maximum of ten groups of keywords, within each group containing up to ten keywords. The principle behind the text-mining function of PubstractHelper is that keywords contained in the same sentence are likely to be related. PubstractHelper highlights sentences with co-occurring keywords in different colors. The user can download the PMID and the abstracts with color markings to be reviewed later. The PubstractHelper website can help users to identify relevant publications based on the presence of related keywords, which should be a handy tool for their research. http://bio.yungyun.com.tw/ATM/PubstractHelper.aspx and http://holab.med.ncku.edu.tw/ATM/PubstractHelper.aspx.

Textpresso site-specific recombinases: A text-mining server for the recombinase literature including Cre mice and conditional alleles.

PubMed

Urbanski, William M; Condie, Brian G

2009-12-01

Textpresso Site Specific Recombinases (http://ssrc.genetics.uga.edu/) is a text-mining web server for searching a database of more than 9,000 full-text publications. The papers and abstracts in this database represent a wide range of topics related to site-specific recombinase (SSR) research tools. Included in the database are most of the papers that report the characterization or use of mouse strains that express Cre recombinase as well as papers that describe or analyze mouse lines that carry conditional (floxed) alleles or SSR-activated transgenes/knockins. The database also includes reports describing SSR-based cloning methods such as the Gateway or the Creator systems, papers reporting the development or use of SSR-based tools in systems such as Drosophila, bacteria, parasites, stem cells, yeast, plants, zebrafish, and Xenopus as well as publications that describe the biochemistry, genetics, or molecular structure of the SSRs themselves. Textpresso Site Specific Recombinases is the only comprehensive text-mining resource available for the literature describing the biology and technical applications of SSRs. (c) 2009 Wiley-Liss, Inc.

The design and implementation of web mining in web sites security

NASA Astrophysics Data System (ADS)

Li, Jian; Zhang, Guo-Yin; Gu, Guo-Chang; Li, Jian-Li

2003-06-01

The backdoor or information leak of Web servers can be detected by using Web Mining techniques on some abnormal Web log and Web application log data. The security of Web servers can be enhanced and the damage of illegal access can be avoided. Firstly, the system for discovering the patterns of information leakages in CGI scripts from Web log data was proposed. Secondly, those patterns for system administrators to modify their codes and enhance their Web site security were provided. The following aspects were described: one is to combine web application log with web log to extract more information, so web data mining could be used to mine web log for discovering the information that firewall and Information Detection System cannot find. Another approach is to propose an operation module of web site to enhance Web site security. In cluster server session, Density-Based Clustering technique is used to reduce resource cost and obtain better efficiency.

Data Mining for Web-Based Support Systems: A Case Study in e-Custom Systems

NASA Astrophysics Data System (ADS)

Razmerita, Liana; Kirchner, Kathrin

This chapter provides an example of a Web-based support system (WSS) used to streamline trade procedures, prevent potential security threats, and reduce tax-related fraud in cross-border trade. The architecture is based on a service-oriented architecture that includes smart seals and Web services. We discuss the implications and suggest further enhancements to demonstrate how such systems can move toward a Web-based decision support system with the support of data mining methods. We provide a concrete example of how data mining can help to analyze the vast amount of data collected while monitoring the container movements along its supply chain.

Web-Based Collaborative Writing in L2 Contexts: Methodological Insights from Text Mining

ERIC Educational Resources Information Center

Yim, Soobin; Warschauer, Mark

2017-01-01

The increasingly widespread use of social software (e.g., Wikis, Google Docs) in second language (L2) settings has brought a renewed attention to collaborative writing. Although the current methodological approaches to examining collaborative writing are valuable to understand L2 students' interactional patterns or perceived experiences, they can…

Effect of Temporal Relationships in Associative Rule Mining for Web Log Data

PubMed Central

Mohd Khairudin, Nazli; Mustapha, Aida

2014-01-01

The advent of web-based applications and services has created such diverse and voluminous web log data stored in web servers, proxy servers, client machines, or organizational databases. This paper attempts to investigate the effect of temporal attribute in relational rule mining for web log data. We incorporated the characteristics of time in the rule mining process and analysed the effect of various temporal parameters. The rules generated from temporal relational rule mining are then compared against the rules generated from the classical rule mining approach such as the Apriori and FP-Growth algorithms. The results showed that by incorporating the temporal attribute via time, the number of rules generated is subsequently smaller but is comparable in terms of quality. PMID:24587757

Collaborative biocuration--text-mining development task for document prioritization for curation.

PubMed

Wiegers, Thomas C; Davis, Allan Peter; Mattingly, Carolyn J

2012-01-01

The Critical Assessment of Information Extraction systems in Biology (BioCreAtIvE) challenge evaluation is a community-wide effort for evaluating text mining and information extraction systems for the biological domain. The 'BioCreative Workshop 2012' subcommittee identified three areas, or tracks, that comprised independent, but complementary aspects of data curation in which they sought community input: literature triage (Track I); curation workflow (Track II) and text mining/natural language processing (NLP) systems (Track III). Track I participants were invited to develop tools or systems that would effectively triage and prioritize articles for curation and present results in a prototype web interface. Training and test datasets were derived from the Comparative Toxicogenomics Database (CTD; http://ctdbase.org) and consisted of manuscripts from which chemical-gene-disease data were manually curated. A total of seven groups participated in Track I. For the triage component, the effectiveness of participant systems was measured by aggregate gene, disease and chemical 'named-entity recognition' (NER) across articles; the effectiveness of 'information retrieval' (IR) was also measured based on 'mean average precision' (MAP). Top recall scores for gene, disease and chemical NER were 49, 65 and 82%, respectively; the top MAP score was 80%. Each participating group also developed a prototype web interface; these interfaces were evaluated based on functionality and ease-of-use by CTD's biocuration project manager. In this article, we present a detailed description of the challenge and a summary of the results.

Service-based analysis of biological pathways

PubMed Central

Zheng, George; Bouguettaya, Athman

2009-01-01

Background Computer-based pathway discovery is concerned with two important objectives: pathway identification and analysis. Conventional mining and modeling approaches aimed at pathway discovery are often effective at achieving either objective, but not both. Such limitations can be effectively tackled leveraging a Web service-based modeling and mining approach. Results Inspired by molecular recognitions and drug discovery processes, we developed a Web service mining tool, named PathExplorer, to discover potentially interesting biological pathways linking service models of biological processes. The tool uses an innovative approach to identify useful pathways based on graph-based hints and service-based simulation verifying user's hypotheses. Conclusion Web service modeling of biological processes allows the easy access and invocation of these processes on the Web. Web service mining techniques described in this paper enable the discovery of biological pathways linking these process service models. Algorithms presented in this paper for automatically highlighting interesting subgraph within an identified pathway network enable the user to formulate hypothesis, which can be tested out using our simulation algorithm that are also described in this paper. PMID:19796403

StemTextSearch: Stem cell gene database with evidence from abstracts.

PubMed

Chen, Chou-Cheng; Ho, Chung-Liang

2017-05-01

Previous studies have used many methods to find biomarkers in stem cells, including text mining, experimental data and image storage. However, no text-mining methods have yet been developed which can identify whether a gene plays a positive or negative role in stem cells. StemTextSearch identifies the role of a gene in stem cells by using a text-mining method to find combinations of gene regulation, stem-cell regulation and cell processes in the same sentences of biomedical abstracts. The dataset includes 5797 genes, with 1534 genes having positive roles in stem cells, 1335 genes having negative roles, 1654 genes with both positive and negative roles, and 1274 with an uncertain role. The precision of gene role in StemTextSearch is 0.66, and the recall is 0.78. StemTextSearch is a web-based engine with queries that specify (i) gene, (ii) category of stem cell, (iii) gene role, (iv) gene regulation, (v) cell process, (vi) stem-cell regulation, and (vii) species. StemTextSearch is available through http://bio.yungyun.com.tw/StemTextSearch.aspx. Copyright © 2017. Published by Elsevier Inc.

Towards the Geospatial Web: Media Platforms for Managing Geotagged Knowledge Repositories

NASA Astrophysics Data System (ADS)

Scharl, Arno

International media have recognized the visual appeal of geo-browsers such as NASA World Wind and Google Earth, for example, when Web and television coverage on Hurricane Katrina used interactive geospatial projections to illustrate its path and the scale of destruction in August 2005. Yet these early applications only hint at the true potential of geospatial technology to build and maintain virtual communities and to revolutionize the production, distribution and consumption of media products. This chapter investigates this potential by reviewing the literature and discussing the integration of geospatial and semantic reference systems, with an emphasis on extracting geospatial context from unstructured text. A content analysis of news coverage based on a suite of text mining tools (webLyzard) sheds light on the popularity and adoption of geospatial platforms.

Biomedical data mining in clinical routine: expanding the impact of hospital information systems.

PubMed

Müller, Marcel; Markó, Kornel; Daumke, Philipp; Paetzold, Jan; Roesner, Arnold; Klar, Rüdiger

2007-01-01

In this paper we want to describe how the promising technology of biomedical data mining can improve the use of hospital information systems: a large set of unstructured, narrative clinical data from a dermatological university hospital like discharge letters or other dermatological reports were processed through a morpho-semantic text retrieval engine ("MorphoSaurus") and integrated with other clinical data using a web-based interface and brought into daily clinical routine. The user evaluation showed a very high user acceptance - this system seems to meet the clinicians' requirements for a vertical data mining in the electronic patient records. What emerges is the need for integration of biomedical data mining into hospital information systems for clinical, scientific, educational and economic reasons.

A construction scheme of web page comment information extraction system based on frequent subtree mining

NASA Astrophysics Data System (ADS)

Zhang, Xiaowen; Chen, Bingfeng

2017-08-01

Based on the frequent sub-tree mining algorithm, this paper proposes a construction scheme of web page comment information extraction system based on frequent subtree mining, referred to as FSM system. The entire system architecture and the various modules to do a brief introduction, and then the core of the system to do a detailed description, and finally give the system prototype.

Monitoring food safety violation reports from internet forums.

PubMed

Kate, Kiran; Negi, Sumit; Kalagnanam, Jayant

2014-01-01

Food-borne illness is a growing public health concern in the world. Government bodies, which regulate and monitor the state of food safety, solicit citizen feedback about food hygiene practices followed by food establishments. They use traditional channels like call center, e-mail for such feedback collection. With the growing popularity of Web 2.0 and social media, citizens often post such feedback on internet forums, message boards etc. The system proposed in this paper applies text mining techniques to identify and mine such food safety complaints posted by citizens on web data sources thereby enabling the government agencies to gather more information about the state of food safety. In this paper, we discuss the architecture of our system and the text mining methods used. We also present results which demonstrate the effectiveness of this system in a real-world deployment.

MetaRanker 2.0: a web server for prioritization of genetic variation data

PubMed Central

Pers, Tune H.; Dworzyński, Piotr; Thomas, Cecilia Engel; Lage, Kasper; Brunak, Søren

2013-01-01

MetaRanker 2.0 is a web server for prioritization of common and rare frequency genetic variation data. Based on heterogeneous data sets including genetic association data, protein–protein interactions, large-scale text-mining data, copy number variation data and gene expression experiments, MetaRanker 2.0 prioritizes the protein-coding part of the human genome to shortlist candidate genes for targeted follow-up studies. MetaRanker 2.0 is made freely available at www.cbs.dtu.dk/services/MetaRanker-2.0. PMID:23703204

MetaRanker 2.0: a web server for prioritization of genetic variation data.

PubMed

Pers, Tune H; Dworzyński, Piotr; Thomas, Cecilia Engel; Lage, Kasper; Brunak, Søren

2013-07-01

MetaRanker 2.0 is a web server for prioritization of common and rare frequency genetic variation data. Based on heterogeneous data sets including genetic association data, protein-protein interactions, large-scale text-mining data, copy number variation data and gene expression experiments, MetaRanker 2.0 prioritizes the protein-coding part of the human genome to shortlist candidate genes for targeted follow-up studies. MetaRanker 2.0 is made freely available at www.cbs.dtu.dk/services/MetaRanker-2.0.

Mining a Web Citation Database for Author Co-Citation Analysis.

ERIC Educational Resources Information Center

He, Yulan; Hui, Siu Cheung

2002-01-01

Proposes a mining process to automate author co-citation analysis based on the Web Citation Database, a data warehouse for storing citation indices of Web publications. Describes the use of agglomerative hierarchical clustering for author clustering and multidimensional scaling for displaying author cluster maps, and explains PubSearch, a…

Earth Science Mining Web Services

NASA Astrophysics Data System (ADS)

Pham, L. B.; Lynnes, C. S.; Hegde, M.; Graves, S.; Ramachandran, R.; Maskey, M.; Keiser, K.

2008-12-01

To allow scientists further capabilities in the area of data mining and web services, the Goddard Earth Sciences Data and Information Services Center (GES DISC) and researchers at the University of Alabama in Huntsville (UAH) have developed a system to mine data at the source without the need of network transfers. The system has been constructed by linking together several pre-existing technologies: the Simple Scalable Script-based Science Processor for Measurements (S4PM), a processing engine at the GES DISC; the Algorithm Development and Mining (ADaM) system, a data mining toolkit from UAH that can be configured in a variety of ways to create customized mining processes; ActiveBPEL, a workflow execution engine based on BPEL (Business Process Execution Language); XBaya, a graphical workflow composer; and the EOS Clearinghouse (ECHO). XBaya is used to construct an analysis workflow at UAH using ADaM components, which are also installed remotely at the GES DISC, wrapped as Web Services. The S4PM processing engine searches ECHO for data using space-time criteria, staging them to cache, allowing the ActiveBPEL engine to remotely orchestrates the processing workflow within S4PM. As mining is completed, the output is placed in an FTP holding area for the end user. The goals are to give users control over the data they want to process, while mining data at the data source using the server's resources rather than transferring the full volume over the internet. These diverse technologies have been infused into a functioning, distributed system with only minor changes to the underlying technologies. The key to this infusion is the loosely coupled, Web- Services based architecture: All of the participating components are accessible (one way or another) through (Simple Object Access Protocol) SOAP-based Web Services.

Earth Science Mining Web Services

NASA Technical Reports Server (NTRS)

Pham, Long; Lynnes, Christopher; Hegde, Mahabaleshwa; Graves, Sara; Ramachandran, Rahul; Maskey, Manil; Keiser, Ken

2008-01-01

To allow scientists further capabilities in the area of data mining and web services, the Goddard Earth Sciences Data and Information Services Center (GES DISC) and researchers at the University of Alabama in Huntsville (UAH) have developed a system to mine data at the source without the need of network transfers. The system has been constructed by linking together several pre-existing technologies: the Simple Scalable Script-based Science Processor for Measurements (S4PM), a processing engine at he GES DISC; the Algorithm Development and Mining (ADaM) system, a data mining toolkit from UAH that can be configured in a variety of ways to create customized mining processes; ActiveBPEL, a workflow execution engine based on BPEL (Business Process Execution Language); XBaya, a graphical workflow composer; and the EOS Clearinghouse (ECHO). XBaya is used to construct an analysis workflow at UAH using ADam components, which are also installed remotely at the GES DISC, wrapped as Web Services. The S4PM processing engine searches ECHO for data using space-time criteria, staging them to cache, allowing the ActiveBPEL engine to remotely orchestras the processing workflow within S4PM. As mining is completed, the output is placed in an FTP holding area for the end user. The goals are to give users control over the data they want to process, while mining data at the data source using the server's resources rather than transferring the full volume over the internet. These diverse technologies have been infused into a functioning, distributed system with only minor changes to the underlying technologies. The key to the infusion is the loosely coupled, Web-Services based architecture: All of the participating components are accessible (one way or another) through (Simple Object Access Protocol) SOAP-based Web Services.

Literature Mining Methods for Toxicology and Construction of ...

EPA Pesticide Factsheets

Webinar Presentation on text-mining methodologies in use at NCCT and how they can be used to assist with the OECD Retinoid project. Presentation to 1st Workshop/Scientific Expert Group meeting on the OECD Retinoid Project - April 26, 2016 –Brussels, Presented remotely via web.

Alkemio: association of chemicals with biomedical topics by text and data mining

PubMed Central

Gijón-Correas, José A.; Andrade-Navarro, Miguel A.; Fontaine, Jean F.

2014-01-01

The PubMed® database of biomedical citations allows the retrieval of scientific articles studying the function of chemicals in biology and medicine. Mining millions of available citations to search reported associations between chemicals and topics of interest would require substantial human time. We have implemented the Alkemio text mining web tool and SOAP web service to help in this task. The tool uses biomedical articles discussing chemicals (including drugs), predicts their relatedness to the query topic with a naïve Bayesian classifier and ranks all chemicals by P-values computed from random simulations. Benchmarks on seven human pathways showed good retrieval performance (areas under the receiver operating characteristic curves ranged from 73.6 to 94.5%). Comparison with existing tools to retrieve chemicals associated to eight diseases showed the higher precision and recall of Alkemio when considering the top 10 candidate chemicals. Alkemio is a high performing web tool ranking chemicals for any biomedical topics and it is free to non-commercial users. Availability: http://cbdm.mdc-berlin.de/∼medlineranker/cms/alkemio. PMID:24838570

BioTextQuest: a web-based biomedical text mining suite for concept discovery.

PubMed

Papanikolaou, Nikolas; Pafilis, Evangelos; Nikolaou, Stavros; Ouzounis, Christos A; Iliopoulos, Ioannis; Promponas, Vasilis J

2011-12-01

BioTextQuest combines automated discovery of significant terms in article clusters with structured knowledge annotation, via Named Entity Recognition services, offering interactive user-friendly visualization. A tag-cloud-based illustration of terms labeling each document cluster are semantically annotated according to the biological entity, and a list of document titles enable users to simultaneously compare terms and documents of each cluster, facilitating concept association and hypothesis generation. BioTextQuest allows customization of analysis parameters, e.g. clustering/stemming algorithms, exclusion of documents/significant terms, to better match the biological question addressed. http://biotextquest.biol.ucy.ac.cy vprobon@ucy.ac.cy; iliopj@med.uoc.gr Supplementary data are available at Bioinformatics online.

Web-video-mining-supported workflow modeling for laparoscopic surgeries.

PubMed

Liu, Rui; Zhang, Xiaoli; Zhang, Hao

2016-11-01

As quality assurance is of strong concern in advanced surgeries, intelligent surgical systems are expected to have knowledge such as the knowledge of the surgical workflow model (SWM) to support their intuitive cooperation with surgeons. For generating a robust and reliable SWM, a large amount of training data is required. However, training data collected by physically recording surgery operations is often limited and data collection is time-consuming and labor-intensive, severely influencing knowledge scalability of the surgical systems. The objective of this research is to solve the knowledge scalability problem in surgical workflow modeling with a low cost and labor efficient way. A novel web-video-mining-supported surgical workflow modeling (webSWM) method is developed. A novel video quality analysis method based on topic analysis and sentiment analysis techniques is developed to select high-quality videos from abundant and noisy web videos. A statistical learning method is then used to build the workflow model based on the selected videos. To test the effectiveness of the webSWM method, 250 web videos were mined to generate a surgical workflow for the robotic cholecystectomy surgery. The generated workflow was evaluated by 4 web-retrieved videos and 4 operation-room-recorded videos, respectively. The evaluation results (video selection consistency n-index ≥0.60; surgical workflow matching degree ≥0.84) proved the effectiveness of the webSWM method in generating robust and reliable SWM knowledge by mining web videos. With the webSWM method, abundant web videos were selected and a reliable SWM was modeled in a short time with low labor cost. Satisfied performances in mining web videos and learning surgery-related knowledge show that the webSWM method is promising in scaling knowledge for intelligent surgical systems. Copyright © 2016 Elsevier B.V. All rights reserved.

Using an improved association rules mining optimization algorithm in web-based mobile-learning system

NASA Astrophysics Data System (ADS)

Huang, Yin; Chen, Jianhua; Xiong, Shaojun

2009-07-01

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

GenCLiP 2.0: a web server for functional clustering of genes and construction of molecular networks based on free terms.

PubMed

Wang, Jia-Hong; Zhao, Ling-Feng; Lin, Pei; Su, Xiao-Rong; Chen, Shi-Jun; Huang, Li-Qiang; Wang, Hua-Feng; Zhang, Hai; Hu, Zhen-Fu; Yao, Kai-Tai; Huang, Zhong-Xi

2014-09-01

Identifying biological functions and molecular networks in a gene list and how the genes may relate to various topics is of considerable value to biomedical researchers. Here, we present a web-based text-mining server, GenCLiP 2.0, which can analyze human genes with enriched keywords and molecular interactions. Compared with other similar tools, GenCLiP 2.0 offers two unique features: (i) analysis of gene functions with free terms (i.e. any terms in the literature) generated by literature mining or provided by the user and (ii) accurate identification and integration of comprehensive molecular interactions from Medline abstracts, to construct molecular networks and subnetworks related to the free terms. http://ci.smu.edu.cn. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

The Effectiveness of Web-Based Learning Environment: A Case Study of Public Universities in Kenya

ERIC Educational Resources Information Center

Kirui, Paul A.; Mutai, Sheila J.

2010-01-01

Web mining is emerging in many aspects of e-learning, aiming at improving online learning and teaching processes and making them more transparent and effective. Researchers using Web mining tools and techniques are challenged to learn more about the online students' reshaping online courses and educational websites, and create tools for…

A UIMA wrapper for the NCBO annotator.

PubMed

Roeder, Christophe; Jonquet, Clement; Shah, Nigam H; Baumgartner, William A; Verspoor, Karin; Hunter, Lawrence

2010-07-15

The Unstructured Information Management Architecture (UIMA) framework and web services are emerging as useful tools for integrating biomedical text mining tools. This note describes our work, which wraps the National Center for Biomedical Ontology (NCBO) Annotator-an ontology-based annotation service-to make it available as a component in UIMA workflows. This wrapper is freely available on the web at http://bionlp-uima.sourceforge.net/ as part of the UIMA tools distribution from the Center for Computational Pharmacology (CCP) at the University of Colorado School of Medicine. It has been implemented in Java for support on Mac OS X, Linux and MS Windows.

Analysing Customer Opinions with Text Mining Algorithms

NASA Astrophysics Data System (ADS)

Consoli, Domenico

2009-08-01

Knowing what the customer thinks of a particular product/service helps top management to introduce improvements in processes and products, thus differentiating the company from their competitors and gain competitive advantages. The customers, with their preferences, determine the success or failure of a company. In order to know opinions of the customers we can use technologies available from the web 2.0 (blog, wiki, forums, chat, social networking, social commerce). From these web sites, useful information must be extracted, for strategic purposes, using techniques of sentiment analysis or opinion mining.

Internet of Things in Health Trends Through Bibliometrics and Text Mining.

PubMed

Konstantinidis, Stathis Th; Billis, Antonis; Wharrad, Heather; Bamidis, Panagiotis D

2017-01-01

Recently a new buzzword has slowly but surely emerged, namely the Internet of Things (IoT). The importance of IoT is identified worldwide both by organisations and governments and the scientific community with an incremental number of publications during the last few years. IoT in Health is one of the main pillars of this evolution, but limited research has been performed on future visions and trends. Thus, in this study we investigate the longitudinal trends of Internet of Things in Health through bibliometrics and use of text mining. Seven hundred seventy eight (778) articles were retrieved form The Web of Science database from 1998 to 2016. The publications are grouped into thirty (30) clusters based on abstract text analysis resulting into some eight (8) trends of IoT in Health. Research in this field is obviously obtaining a worldwide character with specific trends, which are worth delineating to be in favour of some areas.

Using Syntactic Patterns to Enhance Text Analytics

ERIC Educational Resources Information Center

Meyer, Bradley B.

2017-01-01

Large scale product and service reviews proliferate and are commonly found across the web. The ability to harvest, digest and analyze a large corpus of reviews from online websites is still however a difficult problem. This problem is referred to as "opinion mining." Opinion mining is an important area of research as advances in the…

A semantic model for multimodal data mining in healthcare information systems.

PubMed

Iakovidis, Dimitris; Smailis, Christos

2012-01-01

Electronic health records (EHRs) are representative examples of multimodal/multisource data collections; including measurements, images and free texts. The diversity of such information sources and the increasing amounts of medical data produced by healthcare institutes annually, pose significant challenges in data mining. In this paper we present a novel semantic model that describes knowledge extracted from the lowest-level of a data mining process, where information is represented by multiple features i.e. measurements or numerical descriptors extracted from measurements, images, texts or other medical data, forming multidimensional feature spaces. Knowledge collected by manual annotation or extracted by unsupervised data mining from one or more feature spaces is modeled through generalized qualitative spatial semantics. This model enables a unified representation of knowledge across multimodal data repositories. It contributes to bridging the semantic gap, by enabling direct links between low-level features and higher-level concepts e.g. describing body parts, anatomies and pathological findings. The proposed model has been developed in web ontology language based on description logics (OWL-DL) and can be applied to a variety of data mining tasks in medical informatics. It utility is demonstrated for automatic annotation of medical data.

Lightweight monitoring and control system for coal mine safety using REST style.

PubMed

Cheng, Bo; Cheng, Xin; Chen, Junliang

2015-01-01

The complex environment of a coal mine requires the underground environment, devices and miners to be constantly monitored to ensure safe coal production. However, existing coal mines do not meet these coverage requirements because blind spots occur when using a wired network. In this paper, we develop a Web-based, lightweight remote monitoring and control platform using a wireless sensor network (WSN) with the REST style to collect temperature, humidity and methane concentration data in a coal mine using sensor nodes. This platform also collects information on personnel positions inside the mine. We implement a RESTful application programming interface (API) that provides access to underground sensors and instruments through the Web such that underground coal mine physical devices can be easily interfaced to remote monitoring and control applications. We also implement three different scenarios for Web-based, lightweight remote monitoring and control of coal mine safety and measure and analyze the system performance. Finally, we present the conclusions from this study and discuss future work. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

VisualUrText: A Text Analytics Tool for Unstructured Textual Data

NASA Astrophysics Data System (ADS)

Zainol, Zuraini; Jaymes, Mohd T. H.; Nohuddin, Puteri N. E.

2018-05-01

The growing amount of unstructured text over Internet is tremendous. Text repositories come from Web 2.0, business intelligence and social networking applications. It is also believed that 80-90% of future growth data is available in the form of unstructured text databases that may potentially contain interesting patterns and trends. Text Mining is well known technique for discovering interesting patterns and trends which are non-trivial knowledge from massive unstructured text data. Text Mining covers multidisciplinary fields involving information retrieval (IR), text analysis, natural language processing (NLP), data mining, machine learning statistics and computational linguistics. This paper discusses the development of text analytics tool that is proficient in extracting, processing, analyzing the unstructured text data and visualizing cleaned text data into multiple forms such as Document Term Matrix (DTM), Frequency Graph, Network Analysis Graph, Word Cloud and Dendogram. This tool, VisualUrText, is developed to assist students and researchers for extracting interesting patterns and trends in document analyses.

Alkemio: association of chemicals with biomedical topics by text and data mining.

PubMed

Gijón-Correas, José A; Andrade-Navarro, Miguel A; Fontaine, Jean F

2014-07-01

The PubMed® database of biomedical citations allows the retrieval of scientific articles studying the function of chemicals in biology and medicine. Mining millions of available citations to search reported associations between chemicals and topics of interest would require substantial human time. We have implemented the Alkemio text mining web tool and SOAP web service to help in this task. The tool uses biomedical articles discussing chemicals (including drugs), predicts their relatedness to the query topic with a naïve Bayesian classifier and ranks all chemicals by P-values computed from random simulations. Benchmarks on seven human pathways showed good retrieval performance (areas under the receiver operating characteristic curves ranged from 73.6 to 94.5%). Comparison with existing tools to retrieve chemicals associated to eight diseases showed the higher precision and recall of Alkemio when considering the top 10 candidate chemicals. Alkemio is a high performing web tool ranking chemicals for any biomedical topics and it is free to non-commercial users. http://cbdm.mdc-berlin.de/∼medlineranker/cms/alkemio. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

U-Compare: share and compare text mining tools with UIMA.

PubMed

Kano, Yoshinobu; Baumgartner, William A; McCrohon, Luke; Ananiadou, Sophia; Cohen, K Bretonnel; Hunter, Lawrence; Tsujii, Jun'ichi

2009-08-01

Due to the increasing number of text mining resources (tools and corpora) available to biologists, interoperability issues between these resources are becoming significant obstacles to using them effectively. UIMA, the Unstructured Information Management Architecture, is an open framework designed to aid in the construction of more interoperable tools. U-Compare is built on top of the UIMA framework, and provides both a concrete framework for out-of-the-box text mining and a sophisticated evaluation platform allowing users to run specific tools on any target text, generating both detailed statistics and instance-based visualizations of outputs. U-Compare is a joint project, providing the world's largest, and still growing, collection of UIMA-compatible resources. These resources, originally developed by different groups for a variety of domains, include many famous tools and corpora. U-Compare can be launched straight from the web, without needing to be manually installed. All U-Compare components are provided ready-to-use and can be combined easily via a drag-and-drop interface without any programming. External UIMA components can also simply be mixed with U-Compare components, without distinguishing between locally and remotely deployed resources. http://u-compare.org/

Automatic generation of Web mining environments

NASA Astrophysics Data System (ADS)

Cibelli, Maurizio; Costagliola, Gennaro

1999-02-01

The main problem related to the retrieval of information from the world wide web is the enormous number of unstructured documents and resources, i.e., the difficulty of locating and tracking appropriate sources. This paper presents a web mining environment (WME), which is capable of finding, extracting and structuring information related to a particular domain from web documents, using general purpose indices. The WME architecture includes a web engine filter (WEF), to sort and reduce the answer set returned by a web engine, a data source pre-processor (DSP), which processes html layout cues in order to collect and qualify page segments, and a heuristic-based information extraction system (HIES), to finally retrieve the required data. Furthermore, we present a web mining environment generator, WMEG, that allows naive users to generate a WME specific to a given domain by providing a set of specifications.

Document Exploration and Automatic Knowledge Extraction for Unstructured Biomedical Text

NASA Astrophysics Data System (ADS)

Chu, S.; Totaro, G.; Doshi, N.; Thapar, S.; Mattmann, C. A.; Ramirez, P.

2015-12-01

We describe our work on building a web-browser based document reader with built-in exploration tool and automatic concept extraction of medical entities for biomedical text. Vast amounts of biomedical information are offered in unstructured text form through scientific publications and R&D reports. Utilizing text mining can help us to mine information and extract relevant knowledge from a plethora of biomedical text. The ability to employ such technologies to aid researchers in coping with information overload is greatly desirable. In recent years, there has been an increased interest in automatic biomedical concept extraction [1, 2] and intelligent PDF reader tools with the ability to search on content and find related articles [3]. Such reader tools are typically desktop applications and are limited to specific platforms. Our goal is to provide researchers with a simple tool to aid them in finding, reading, and exploring documents. Thus, we propose a web-based document explorer, which we called Shangri-Docs, which combines a document reader with automatic concept extraction and highlighting of relevant terms. Shangri-Docsalso provides the ability to evaluate a wide variety of document formats (e.g. PDF, Words, PPT, text, etc.) and to exploit the linked nature of the Web and personal content by performing searches on content from public sites (e.g. Wikipedia, PubMed) and private cataloged databases simultaneously. Shangri-Docsutilizes Apache cTAKES (clinical Text Analysis and Knowledge Extraction System) [4] and Unified Medical Language System (UMLS) to automatically identify and highlight terms and concepts, such as specific symptoms, diseases, drugs, and anatomical sites, mentioned in the text. cTAKES was originally designed specially to extract information from clinical medical records. Our investigation leads us to extend the automatic knowledge extraction process of cTAKES for biomedical research domain by improving the ontology guided information extraction process. We will describe our experience and implementation of our system and share lessons learned from our development. We will also discuss ways in which this could be adapted to other science fields. [1] Funk et al., 2014. [2] Kang et al., 2014. [3] Utopia Documents, http://utopiadocs.com [4] Apache cTAKES, http://ctakes.apache.org

Numerical linear algebra in data mining

NASA Astrophysics Data System (ADS)

Eldén, Lars

Ideas and algorithms from numerical linear algebra are important in several areas of data mining. We give an overview of linear algebra methods in text mining (information retrieval), pattern recognition (classification of handwritten digits), and PageRank computations for web search engines. The emphasis is on rank reduction as a method of extracting information from a data matrix, low-rank approximation of matrices using the singular value decomposition and clustering, and on eigenvalue methods for network analysis.

Application of data mining in science and technology management information system based on WebGIS

NASA Astrophysics Data System (ADS)

Wu, Xiaofang; Xu, Zhiyong; Bao, Shitai; Chen, Feixiang

2009-10-01

With the rapid development of science and technology and the quick increase of information, a great deal of data is accumulated in the management department of science and technology. Usually, many knowledge and rules are contained and concealed in the data. Therefore, how to excavate and use the knowledge fully is very important in the management of science and technology. It will help to examine and approve the project of science and technology more scientifically and make the achievement transformed as the realistic productive forces easier. Therefore, the data mine technology will be researched and applied to the science and technology management information system to find and excavate the knowledge in the paper. According to analyzing the disadvantages of traditional science and technology management information system, the database technology, data mining and web geographic information systems (WebGIS) technology will be introduced to develop and construct the science and technology management information system based on WebGIS. The key problems are researched in detail such as data mining and statistical analysis. What's more, the prototype system is developed and validated based on the project data of National Natural Science Foundation Committee. The spatial data mining is done from the axis of time, space and other factors. Then the variety of knowledge and rules will be excavated by using data mining technology, which helps to provide an effective support for decisionmaking.

Web data mining

NASA Astrophysics Data System (ADS)

Wibonele, Kasanda J.; Zhang, Yanqing

2002-03-01

A web data mining system using granular computing and ASP programming is proposed. This is a web based application, which allows web users to submit survey data for many different companies. This survey is a collection of questions that will help these companies develop and improve their business and customer service with their clients by analyzing survey data. This web application allows users to submit data anywhere. All the survey data is collected into a database for further analysis. An administrator of this web application can login to the system and view all the data submitted. This web application resides on a web server, and the database resides on the MS SQL server.

Mining Student Data Captured from a Web-Based Tutoring Tool: Initial Exploration and Results

ERIC Educational Resources Information Center

Merceron, Agathe; Yacef, Kalina

2004-01-01

In this article we describe the initial investigations that we have conducted on student data collected from a web-based tutoring tool. We have used some data mining techniques such as association rule and symbolic data analysis, as well as traditional SQL queries to gain further insight on the students' learning and deduce information to improve…

Terminologies for text-mining; an experiment in the lipoprotein metabolism domain

PubMed Central

Alexopoulou, Dimitra; Wächter, Thomas; Pickersgill, Laura; Eyre, Cecilia; Schroeder, Michael

2008-01-01

Background The engineering of ontologies, especially with a view to a text-mining use, is still a new research field. There does not yet exist a well-defined theory and technology for ontology construction. Many of the ontology design steps remain manual and are based on personal experience and intuition. However, there exist a few efforts on automatic construction of ontologies in the form of extracted lists of terms and relations between them. Results We share experience acquired during the manual development of a lipoprotein metabolism ontology (LMO) to be used for text-mining. We compare the manually created ontology terms with the automatically derived terminology from four different automatic term recognition (ATR) methods. The top 50 predicted terms contain up to 89% relevant terms. For the top 1000 terms the best method still generates 51% relevant terms. In a corpus of 3066 documents 53% of LMO terms are contained and 38% can be generated with one of the methods. Conclusions Given high precision, automatic methods can help decrease development time and provide significant support for the identification of domain-specific vocabulary. The coverage of the domain vocabulary depends strongly on the underlying documents. Ontology development for text mining should be performed in a semi-automatic way; taking ATR results as input and following the guidelines we described. Availability The TFIDF term recognition is available as Web Service, described at PMID:18460175

Stratification-Based Outlier Detection over the Deep Web.

PubMed

Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

2016-01-01

For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web.

Stratification-Based Outlier Detection over the Deep Web

PubMed Central

Xian, Xuefeng; Zhao, Pengpeng; Sheng, Victor S.; Fang, Ligang; Gu, Caidong; Yang, Yuanfeng; Cui, Zhiming

2016-01-01

For many applications, finding rare instances or outliers can be more interesting than finding common patterns. Existing work in outlier detection never considers the context of deep web. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. Therefore, traditional data mining methods cannot be directly applied. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. In our approach, the query space of a deep web data source is stratified based on a pilot sample. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web. PMID:27313603

Web mining in soft computing framework: relevance, state of the art and future directions.

PubMed

Pal, S K; Talwar, V; Mitra, P

2002-01-01

The paper summarizes the different characteristics of Web data, the basic components of Web mining and its different types, and the current state of the art. The reason for considering Web mining, a separate field from data mining, is explained. The limitations of some of the existing Web mining methods and tools are enunciated, and the significance of soft computing (comprising fuzzy logic (FL), artificial neural networks (ANNs), genetic algorithms (GAs), and rough sets (RSs) are highlighted. A survey of the existing literature on "soft Web mining" is provided along with the commercially available systems. The prospective areas of Web mining where the application of soft computing needs immediate attention are outlined with justification. Scope for future research in developing "soft Web mining" systems is explained. An extensive bibliography is also provided.

A UIMA wrapper for the NCBO annotator

PubMed Central

Roeder, Christophe; Jonquet, Clement; Shah, Nigam H.; Baumgartner, William A.; Verspoor, Karin; Hunter, Lawrence

2010-01-01

Summary: The Unstructured Information Management Architecture (UIMA) framework and web services are emerging as useful tools for integrating biomedical text mining tools. This note describes our work, which wraps the National Center for Biomedical Ontology (NCBO) Annotator—an ontology-based annotation service—to make it available as a component in UIMA workflows. Availability: This wrapper is freely available on the web at http://bionlp-uima.sourceforge.net/ as part of the UIMA tools distribution from the Center for Computational Pharmacology (CCP) at the University of Colorado School of Medicine. It has been implemented in Java for support on Mac OS X, Linux and MS Windows. Contact: chris.roeder@ucdenver.edu PMID:20505005

PubMed-EX: a web browser extension to enhance PubMed search with text mining features.

PubMed

Tsai, Richard Tzong-Han; Dai, Hong-Jie; Lai, Po-Ting; Huang, Chi-Hsin

2009-11-15

PubMed-EX is a browser extension that marks up PubMed search results with additional text-mining information. PubMed-EX's page mark-up, which includes section categorization and gene/disease and relation mark-up, can help researchers to quickly focus on key terms and provide additional information on them. All text processing is performed server-side, freeing up user resources. PubMed-EX is freely available at http://bws.iis.sinica.edu.tw/PubMed-EX and http://iisr.cse.yzu.edu.tw:8000/PubMed-EX/.

TREC Microblog 2012 Track: Real-Time Algorithm for Microblog Ranking Systems

DTIC Science & Technology

2012-11-01

such as information about the tweet and the user profile. We collected those tweets by means of web crawler and extract several features from the raw...Mining Text Data. 2012. [5] D. Feltoni. Twittersa: un sistema per l’analisi del sentimento nelle reti sociali. Master’s thesis, Roma Tre University...Morris. Twittersearch: a comparison of microblog search and web search. Proceedings of the fourth ACM international conference on Web search, 2011

A novel web informatics approach for automated surveillance of cancer mortality trends✩

PubMed Central

Tourassi, Georgia; Yoon, Hong-Jun; Xu, Songhua

2016-01-01

Cancer surveillance data are collected every year in the United States via the National Program of Cancer Registries (NPCR) and the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute (NCI). General trends are closely monitored to measure the nation's progress against cancer. The objective of this study was to apply a novel web informatics approach for enabling fully automated monitoring of cancer mortality trends. The approach involves automated collection and text mining of online obituaries to derive the age distribution, geospatial, and temporal trends of cancer deaths in the US. Using breast and lung cancer as examples, we mined 23,850 cancer-related and 413,024 general online obituaries spanning the timeframe 2008–2012. There was high correlation between the web-derived mortality trends and the official surveillance statistics reported by NCI with respect to the age distribution (ρ = 0.981 for breast; ρ = 0.994 for lung), the geospatial distribution (ρ = 0.939 for breast; ρ = 0.881 for lung), and the annual rates of cancer deaths (ρ = 0.661 for breast; ρ = 0.839 for lung). Additional experiments investigated the effect of sample size on the consistency of the web-based findings. Overall, our study findings support web informatics as a promising, cost-effective way to dynamically monitor spatiotemporal cancer mortality trends. PMID:27044930

A novel web informatics approach for automated surveillance of cancer mortality trends

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tourassi, Georgia; Yoon, Hong -Jun; Xu, Songhua

Cancer surveillance data are collected every year in the United States via the National Program of Cancer Registries (NPCR) and the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute (NCI). General trends are closely monitored to measure the nation’s progress against cancer. The objective of this study was to apply a novel web informatics approach for enabling fully automated monitoring of cancer mortality trends. The approach involves automated collection and text mining of online obituaries to derive the age distribution, geospatial, and temporal trends of cancer deaths in the US. Using breast and lung cancer asmore » examples, we mined 23,850 cancer-related and 413,024 general online obituaries spanning the timeframe 2008–2012. There was high correlation between the web-derived mortality trends and the official surveillance statistics reported by NCI with respect to the age distribution (ρ = 0.981 for breast; ρ = 0.994 for lung), the geospatial distribution (ρ = 0.939 for breast; ρ = 0.881 for lung), and the annual rates of cancer deaths (ρ = 0.661 for breast; ρ = 0.839 for lung). Additional experiments investigated the effect of sample size on the consistency of the web-based findings. Altogether, our study findings support web informatics as a promising, cost-effective way to dynamically monitor spatiotemporal cancer mortality trends.« less

A novel web informatics approach for automated surveillance of cancer mortality trends

DOE PAGES

Tourassi, Georgia; Yoon, Hong -Jun; Xu, Songhua

2016-04-01

Cancer surveillance data are collected every year in the United States via the National Program of Cancer Registries (NPCR) and the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute (NCI). General trends are closely monitored to measure the nation’s progress against cancer. The objective of this study was to apply a novel web informatics approach for enabling fully automated monitoring of cancer mortality trends. The approach involves automated collection and text mining of online obituaries to derive the age distribution, geospatial, and temporal trends of cancer deaths in the US. Using breast and lung cancer asmore » examples, we mined 23,850 cancer-related and 413,024 general online obituaries spanning the timeframe 2008–2012. There was high correlation between the web-derived mortality trends and the official surveillance statistics reported by NCI with respect to the age distribution (ρ = 0.981 for breast; ρ = 0.994 for lung), the geospatial distribution (ρ = 0.939 for breast; ρ = 0.881 for lung), and the annual rates of cancer deaths (ρ = 0.661 for breast; ρ = 0.839 for lung). Additional experiments investigated the effect of sample size on the consistency of the web-based findings. Altogether, our study findings support web informatics as a promising, cost-effective way to dynamically monitor spatiotemporal cancer mortality trends.« less

Rule-based statistical data mining agents for an e-commerce application

NASA Astrophysics Data System (ADS)

Qin, Yi; Zhang, Yan-Qing; King, K. N.; Sunderraman, Rajshekhar

2003-03-01

Intelligent data mining techniques have useful e-Business applications. Because an e-Commerce application is related to multiple domains such as statistical analysis, market competition, price comparison, profit improvement and personal preferences, this paper presents a hybrid knowledge-based e-Commerce system fusing intelligent techniques, statistical data mining, and personal information to enhance QoS (Quality of Service) of e-Commerce. A Web-based e-Commerce application software system, eDVD Web Shopping Center, is successfully implemented uisng Java servlets and an Oracle81 database server. Simulation results have shown that the hybrid intelligent e-Commerce system is able to make smart decisions for different customers.

Discovering and visualizing indirect associations between biomedical concepts

PubMed Central

Tsuruoka, Yoshimasa; Miwa, Makoto; Hamamoto, Kaisei; Tsujii, Jun'ichi; Ananiadou, Sophia

2011-01-01

Motivation: Discovering useful associations between biomedical concepts has been one of the main goals in biomedical text-mining, and understanding their biomedical contexts is crucial in the discovery process. Hence, we need a text-mining system that helps users explore various types of (possibly hidden) associations in an easy and comprehensible manner. Results: This article describes FACTA+, a real-time text-mining system for finding and visualizing indirect associations between biomedical concepts from MEDLINE abstracts. The system can be used as a text search engine like PubMed with additional features to help users discover and visualize indirect associations between important biomedical concepts such as genes, diseases and chemical compounds. FACTA+ inherits all functionality from its predecessor, FACTA, and extends it by incorporating three new features: (i) detecting biomolecular events in text using a machine learning model, (ii) discovering hidden associations using co-occurrence statistics between concepts, and (iii) visualizing associations to improve the interpretability of the output. To the best of our knowledge, FACTA+ is the first real-time web application that offers the functionality of finding concepts involving biomolecular events and visualizing indirect associations of concepts with both their categories and importance. Availability: FACTA+ is available as a web application at http://refine1-nactem.mc.man.ac.uk/facta/, and its visualizer is available at http://refine1-nactem.mc.man.ac.uk/facta-visualizer/. Contact: tsuruoka@jaist.ac.jp PMID:21685059

Clustering and Dimensionality Reduction to Discover Interesting Patterns in Binary Data

NASA Astrophysics Data System (ADS)

Palumbo, Francesco; D'Enza, Alfonso Iodice

The attention towards binary data coding increased consistently in the last decade due to several reasons. The analysis of binary data characterizes several fields of application, such as market basket analysis, DNA microarray data, image mining, text mining and web-clickstream mining. The paper illustrates two different approaches exploiting a profitable combination of clustering and dimensionality reduction for the identification of non-trivial association structures in binary data. An application in the Association Rules framework supports the theory with the empirical evidence.

Effective Filtering of Query Results on Updated User Behavioral Profiles in Web Mining

PubMed Central

Sadesh, S.; Suganthe, R. C.

2015-01-01

Web with tremendous volume of information retrieves result for user related queries. With the rapid growth of web page recommendation, results retrieved based on data mining techniques did not offer higher performance filtering rate because relationships between user profile and queries were not analyzed in an extensive manner. At the same time, existing user profile based prediction in web data mining is not exhaustive in producing personalized result rate. To improve the query result rate on dynamics of user behavior over time, Hamilton Filtered Regime Switching User Query Probability (HFRS-UQP) framework is proposed. HFRS-UQP framework is split into two processes, where filtering and switching are carried out. The data mining based filtering in our research work uses the Hamilton Filtering framework to filter user result based on personalized information on automatic updated profiles through search engine. Maximized result is fetched, that is, filtered out with respect to user behavior profiles. The switching performs accurate filtering updated profiles using regime switching. The updating in profile change (i.e., switches) regime in HFRS-UQP framework identifies the second- and higher-order association of query result on the updated profiles. Experiment is conducted on factors such as personalized information search retrieval rate, filtering efficiency, and precision ratio. PMID:26221626

Data Mining Web Services for Science Data Repositories

NASA Astrophysics Data System (ADS)

Graves, S.; Ramachandran, R.; Keiser, K.; Maskey, M.; Lynnes, C.; Pham, L.

2006-12-01

The maturation of web services standards and technologies sets the stage for a distributed "Service-Oriented Architecture" (SOA) for NASA's next generation science data processing. This architecture will allow members of the scientific community to create and combine persistent distributed data processing services and make them available to other users over the Internet. NASA has initiated a project to create a suite of specialized data mining web services designed specifically for science data. The project leverages the Algorithm Development and Mining (ADaM) toolkit as its basis. The ADaM toolkit is a robust, mature and freely available science data mining toolkit that is being used by several research organizations and educational institutions worldwide. These mining services will give the scientific community a powerful and versatile data mining capability that can be used to create higher order products such as thematic maps from current and future NASA satellite data records with methods that are not currently available. The package of mining and related services are being developed using Web Services standards so that community-based measurement processing systems can access and interoperate with them. These standards-based services allow users different options for utilizing them, from direct remote invocation by a client application to deployment of a Business Process Execution Language (BPEL) solutions package where a complex data mining workflow is exposed to others as a single service. The ability to deploy and operate these services at a data archive allows the data mining algorithms to be run where the data are stored, a more efficient scenario than moving large amounts of data over the network. This will be demonstrated in a scenario in which a user uses a remote Web-Service-enabled clustering algorithm to create cloud masks from satellite imagery at the Goddard Earth Sciences Data and Information Services Center (GES DISC).

U-Compare: share and compare text mining tools with UIMA

PubMed Central

Kano, Yoshinobu; Baumgartner, William A.; McCrohon, Luke; Ananiadou, Sophia; Cohen, K. Bretonnel; Hunter, Lawrence; Tsujii, Jun'ichi

2009-01-01

Summary: Due to the increasing number of text mining resources (tools and corpora) available to biologists, interoperability issues between these resources are becoming significant obstacles to using them effectively. UIMA, the Unstructured Information Management Architecture, is an open framework designed to aid in the construction of more interoperable tools. U-Compare is built on top of the UIMA framework, and provides both a concrete framework for out-of-the-box text mining and a sophisticated evaluation platform allowing users to run specific tools on any target text, generating both detailed statistics and instance-based visualizations of outputs. U-Compare is a joint project, providing the world's largest, and still growing, collection of UIMA-compatible resources. These resources, originally developed by different groups for a variety of domains, include many famous tools and corpora. U-Compare can be launched straight from the web, without needing to be manually installed. All U-Compare components are provided ready-to-use and can be combined easily via a drag-and-drop interface without any programming. External UIMA components can also simply be mixed with U-Compare components, without distinguishing between locally and remotely deployed resources. Availability: http://u-compare.org/ Contact: kano@is.s.u-tokyo.ac.jp PMID:19414535

Analysis of mesenchymal stem cell differentiation in vitro using classification association rule mining.

PubMed

Wang, Weiqi; Wang, Yanbo Justin; Bañares-Alcántara, René; Coenen, Frans; Cui, Zhanfeng

2009-12-01

In this paper, data mining is used to analyze the data on the differentiation of mammalian Mesenchymal Stem Cells (MSCs), aiming at discovering known and hidden rules governing MSC differentiation, following the establishment of a web-based public database containing experimental data on the MSC proliferation and differentiation. To this effect, a web-based public interactive database comprising the key parameters which influence the fate and destiny of mammalian MSCs has been constructed and analyzed using Classification Association Rule Mining (CARM) as a data-mining technique. The results show that the proposed approach is technically feasible and performs well with respect to the accuracy of (classification) prediction. Key rules mined from the constructed MSC database are consistent with experimental observations, indicating the validity of the method developed and the first step in the application of data mining to the study of MSCs.

pubmed.mineR: an R package with text-mining algorithms to analyse PubMed abstracts.

PubMed

Rani, Jyoti; Shah, A B Rauf; Ramachandran, Srinivasan

2015-10-01

The PubMed literature database is a valuable source of information for scientific research. It is rich in biomedical literature with more than 24 million citations. Data-mining of voluminous literature is a challenging task. Although several text-mining algorithms have been developed in recent years with focus on data visualization, they have limitations such as speed, are rigid and are not available in the open source. We have developed an R package, pubmed.mineR, wherein we have combined the advantages of existing algorithms, overcome their limitations, and offer user flexibility and link with other packages in Bioconductor and the Comprehensive R Network (CRAN) in order to expand the user capabilities for executing multifaceted approaches. Three case studies are presented, namely, 'Evolving role of diabetes educators', 'Cancer risk assessment' and 'Dynamic concepts on disease and comorbidity' to illustrate the use of pubmed.mineR. The package generally runs fast with small elapsed times in regular workstations even on large corpus sizes and with compute intensive functions. The pubmed.mineR is available at http://cran.rproject. org/web/packages/pubmed.mineR.

Building a glaucoma interaction network using a text mining approach.

PubMed

Soliman, Maha; Nasraoui, Olfa; Cooper, Nigel G F

2016-01-01

The volume of biomedical literature and its underlying knowledge base is rapidly expanding, making it beyond the ability of a single human being to read through all the literature. Several automated methods have been developed to help make sense of this dilemma. The present study reports on the results of a text mining approach to extract gene interactions from the data warehouse of published experimental results which are then used to benchmark an interaction network associated with glaucoma. To the best of our knowledge, there is, as yet, no glaucoma interaction network derived solely from text mining approaches. The presence of such a network could provide a useful summative knowledge base to complement other forms of clinical information related to this disease. A glaucoma corpus was constructed from PubMed Central and a text mining approach was applied to extract genes and their relations from this corpus. The extracted relations between genes were checked using reference interaction databases and classified generally as known or new relations. The extracted genes and relations were then used to construct a glaucoma interaction network. Analysis of the resulting network indicated that it bears the characteristics of a small world interaction network. Our analysis showed the presence of seven glaucoma linked genes that defined the network modularity. A web-based system for browsing and visualizing the extracted glaucoma related interaction networks is made available at http://neurogene.spd.louisville.edu/GlaucomaINViewer/Form1.aspx. This study has reported the first version of a glaucoma interaction network using a text mining approach. The power of such an approach is in its ability to cover a wide range of glaucoma related studies published over many years. Hence, a bigger picture of the disease can be established. To the best of our knowledge, this is the first glaucoma interaction network to summarize the known literature. The major findings were a set of relations that could not be found in existing interaction databases and that were found to be new, in addition to a smaller subnetwork consisting of interconnected clusters of seven glaucoma genes. Future improvements can be applied towards obtaining a better version of this network.

75 FR 17803 - Self-Regulatory Organizations; NASDAQ OMX PHLX, Inc.; Notice of Filing and Immediate...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-07

.... The text of the proposed rule change is available on the Exchange's Web site at http://nasdaqtrader... and discussed any comments it received on the proposed rule change. The text of these statements may... Mining Corporation (``NEM''); Palm, Inc. (``PALM''); Pfizer, Inc. (``PFE''); ''); Potash Corp...

Motivation Mining: Prospecting the Web.

ERIC Educational Resources Information Center

Small, Ruth V.; Arnone, Marilyn P.

1999-01-01

Describes WebMAC instruments, which differ from other Web-evaluation instruments because they have a theoretical base, are user-centered, are designed for students in grades 7 through 12, and assess the motivational quality of Web sites. Examples are given of uses of WebMAC Middle and WebMAC Senior in activities to promote evaluation and…

Web Mining for Web Image Retrieval.

ERIC Educational Resources Information Center

Chen, Zheng; Wenyin, Liu; Zhang, Feng; Li, Mingjing; Zhang, Hongjiang

2001-01-01

Presents a prototype system for image retrieval from the Internet using Web mining. Discusses the architecture of the Web image retrieval prototype; document space modeling; user log mining; and image retrieval experiments to evaluate the proposed system. (AEF)

Data Mining of Web-Based Documents on Social Networking Sites That Included Suicide-Related Words Among Korean Adolescents.

PubMed

Song, Juyoung; Song, Tae Min; Seo, Dong-Chul; Jin, Jae Hyun

2016-12-01

To investigate online search activity of suicide-related words in South Korean adolescents through data mining of social media Web sites as the suicide rate in South Korea is one of the highest in the world. Out of more than 2.35 billion posts for 2 years from January 1, 2011 to December 31, 2012 on 163 social media Web sites in South Korea, 99,693 suicide-related documents were retrieved by Crawler and analyzed using text mining and opinion mining. These data were further combined with monthly employment rate, monthly rental prices index, monthly youth suicide rate, and monthly number of reported bully victims to fit multilevel models as well as structural equation models. The link from grade pressure to suicide risk showed the largest standardized path coefficient (beta = .357, p < .001) in structural models and a significant random effect (p < .01) in multilevel models. Depression was a partial mediator between suicide risk and grade pressure, low body image, victims of bullying, and concerns about disease. The largest total effect was observed in the grade pressure to depression to suicide risk. The multilevel models indicate about 27% of the variance in the daily suicide-related word search activity is explained by month-to-month variations. A lower employment rate, a higher rental prices index, and more bullying were associated with an increased suicide-related word search activity. Academic pressure appears to be the biggest contributor to Korean adolescents' suicide risk. Real-time suicide-related word search activity monitoring and response system needs to be developed. Copyright © 2016 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Study on online community user motif using web usage mining

NASA Astrophysics Data System (ADS)

Alphy, Meera; Sharma, Ajay

2016-04-01

The Web usage mining is the application of data mining, which is used to extract useful information from the online community. The World Wide Web contains at least 4.73 billion pages according to Indexed Web and it contains at least 228.52 million pages according Dutch Indexed web on 6th august 2015, Thursday. It’s difficult to get needed data from these billions of web pages in World Wide Web. Here is the importance of web usage mining. Personalizing the search engine helps the web user to identify the most used data in an easy way. It reduces the time consumption; automatic site search and automatic restore the useful sites. This study represents the old techniques to latest techniques used in pattern discovery and analysis in web usage mining from 1996 to 2015. Analyzing user motif helps in the improvement of business, e-commerce, personalisation and improvement of websites.

Data mining for personal navigation

NASA Astrophysics Data System (ADS)

Hariharan, Gurushyam; Franti, Pasi; Mehta, Sandeep

2002-03-01

Relevance is the key in defining what data is to be extracted from the Internet. Traditionally, relevance has been defined mainly by keywords and user profiles. In this paper we discuss a fairly untouched dimension to relevance: location. Any navigational information sought by a user at large on earth is evidently governed by his location. We believe that task oriented data mining of the web amalgamated with location information is the key to providing relevant information for personal navigation. We explore the existential hurdles and propose novel approaches to tackle them. We also present naive, task-oriented data mining based approaches and their implementations in Java, to extract location based information. Ad-hoc pairing of data with coordinates (x, y) is very rare on the web. But if the same co-ordinates are converted to a logical address (state/city/street), a wide spectrum of location-based information base opens up. Hence, given the coordinates (x, y) on the earth, the scheme points to the logical address of the user. Location based information could either be picked up from fixed and known service providers (e.g. Yellow Pages) or from any arbitrary website on the Web. Once the web servers providing information relevant to the logical address are located, task oriented data mining is performed over these sites keeping in mind what information is interesting to the contemporary user. After all this, a simple data stream is provided to the user with information scaled to his convenience. The scheme has been implemented for cities of Finland.

Evaluation of an open source tool for indexing and searching enterprise radiology and pathology reports

NASA Astrophysics Data System (ADS)

Kim, Woojin; Boonn, William

2010-03-01

Data mining of existing radiology and pathology reports within an enterprise health system can be used for clinical decision support, research, education, as well as operational analyses. In our health system, the database of radiology and pathology reports exceeds 13 million entries combined. We are building a web-based tool to allow search and data analysis of these combined databases using freely available and open source tools. This presentation will compare performance of an open source full-text indexing tool to MySQL's full-text indexing and searching and describe implementation procedures to incorporate these capabilities into a radiology-pathology search engine.

Mining Formative Evaluation Rules Using Web-Based Learning Portfolios for Web-Based Learning Systems

ERIC Educational Resources Information Center

Chen, Chih-Ming; Hong, Chin-Ming; Chen, Shyuan-Yi; Liu, Chao-Yu

2006-01-01

Learning performance assessment aims to evaluate what knowledge learners have acquired from teaching activities. Objective technical measures of learning performance are difficult to develop, but are extremely important for both teachers and learners. Learning performance assessment using learning portfolios or web server log data is becoming an…

QuadBase2: web server for multiplexed guanine quadruplex mining and visualization

PubMed Central

Dhapola, Parashar; Chowdhury, Shantanu

2016-01-01

DNA guanine quadruplexes or G4s are non-canonical DNA secondary structures which affect genomic processes like replication, transcription and recombination. G4s are computationally identified by specific nucleotide motifs which are also called putative G4 (PG4) motifs. Despite the general relevance of these structures, there is currently no tool available that can allow batch queries and genome-wide analysis of these motifs in a user-friendly interface. QuadBase2 (quadbase.igib.res.in) presents a completely reinvented web server version of previously published QuadBase database. QuadBase2 enables users to mine PG4 motifs in up to 178 eukaryotes through the EuQuad module. This module interfaces with Ensembl Compara database, to allow users mine PG4 motifs in the orthologues of genes of interest across eukaryotes. PG4 motifs can be mined across genes and their promoter sequences in 1719 prokaryotes through ProQuad module. This module includes a feature that allows genome-wide mining of PG4 motifs and their visualization as circular histograms. TetraplexFinder, the module for mining PG4 motifs in user-provided sequences is now capable of handling up to 20 MB of data. QuadBase2 is a comprehensive PG4 motif mining tool that further expands the configurations and algorithms for mining PG4 motifs in a user-friendly way. PMID:27185890

Knowledge Discovery in Literature Data Bases

NASA Astrophysics Data System (ADS)

Albrecht, Rudolf; Merkl, Dieter

The concept of knowledge discovery as defined through ``establishing previously unknown and unsuspected relations of features in a data base'' is, cum grano salis, relatively easy to implement for data bases containing numerical data. Increasingly we find at our disposal data bases containing scientific literature. Computer assisted detection of unknown relations of features in such data bases would be extremely valuable and would lead to new scientific insights. However, the current representation of scientific knowledge in such data bases is not conducive to computer processing. Any correlation of features still has to be done by the human reader, a process which is plagued by ineffectiveness and incompleteness. On the other hand we note that considerable progress is being made in an area where reading all available material is totally prohibitive: the World Wide Web. Robots and Web crawlers mine the Web continuously and construct data bases which allow the identification of pages of interest in near real time. An obvious step is to categorize and classify the documents in the text data base. This can be used to identify papers worth reading, or which are of unexpected cross-relevance. We show the results of first experiments using unsupervised classification based on neural networks.

Research on the optimization strategy of web search engine based on data mining

NASA Astrophysics Data System (ADS)

Chen, Ronghua

2018-04-01

With the wide application of search engines, web site information has become an important way for people to obtain information. People have found that they are growing in an increasingly explosive manner. Web site information is verydifficult to find the information they need, and now the search engine can not meet the need, so there is an urgent need for the network to provide website personalized information service, data mining technology for this new challenge is to find a breakthrough. In order to improve people's accuracy of finding information from websites, a website search engine optimization strategy based on data mining is proposed, and verified by website search engine optimization experiment. The results show that the proposed strategy improves the accuracy of the people to find information, and reduces the time for people to find information. It has an important practical value.

A Framework for Web Usage Mining in Electronic Government

NASA Astrophysics Data System (ADS)

Zhou, Ping; Le, Zhongjian

Web usage mining has been a major component of management strategy to enhance organizational analysis and decision. The literature on Web usage mining that deals with strategies and technologies for effectively employing Web usage mining is quite vast. In recent years, E-government has received much attention from researchers and practitioners. Huge amounts of user access data are produced in Electronic government Web site everyday. The role of these data in the success of government management cannot be overstated because they affect government analysis, prediction, strategies, tactical, operational planning and control. Web usage miming in E-government has an important role to play in setting government objectives, discovering citizen behavior, and determining future courses of actions. Web usage mining in E-government has not received adequate attention from researchers or practitioners. We developed a framework to promote a better understanding of the importance of Web usage mining in E-government. Using the current literature, we developed the framework presented herein, in hopes that it would stimulate more interest in this important area.

Online discourse on fibromyalgia: text-mining to identify clinical distinction and patient concerns.

PubMed

Park, Jungsik; Ryu, Young Uk

2014-10-07

The purpose of this study was to evaluate the possibility of using text-mining to identify clinical distinctions and patient concerns in online memoires posted by patients with fibromyalgia (FM). A total of 399 memoirs were collected from an FM group website. The unstructured data of memoirs associated with FM were collected through a crawling process and converted into structured data with a concordance, parts of speech tagging, and word frequency. We also conducted a lexical analysis and phrase pattern identification. After examining the data, a set of FM-related keywords were obtained and phrase net relationships were set through a web-based visualization tool. The clinical distinction of FM was verified. Pain is the biggest issue to the FM patients. The pains were affecting body parts including 'muscles,' 'leg,' 'neck,' 'back,' 'joints,' and 'shoulders' with accompanying symptoms such as 'spasms,' 'stiffness,' and 'aching,' and were described as 'sever,' 'chronic,' and 'constant.' This study also demonstrated that it was possible to understand the interests and concerns of FM patients through text-mining. FM patients wanted to escape from the pain and symptoms, so they were interested in medical treatment and help. Also, they seemed to have interest in their work and occupation, and hope to continue to live life through the relationships with the people around them. This research shows the potential for extracting keywords to confirm the clinical distinction of a certain disease, and text-mining can help objectively understand the concerns of patients by generalizing their large number of subjective illness experiences. However, it is believed that there are limitations to the processes and methods for organizing and classifying large amounts of text, so these limits have to be considered when analyzing the results. The development of research methodology to overcome these limitations is greatly needed.

Systematic Review of Data Mining Applications in Patient-Centered Mobile-Based Information Systems.

PubMed

Fallah, Mina; Niakan Kalhori, Sharareh R

2017-10-01

Smartphones represent a promising technology for patient-centered healthcare. It is claimed that data mining techniques have improved mobile apps to address patients' needs at subgroup and individual levels. This study reviewed the current literature regarding data mining applications in patient-centered mobile-based information systems. We systematically searched PubMed, Scopus, and Web of Science for original studies reported from 2014 to 2016. After screening 226 records at the title/abstract level, the full texts of 92 relevant papers were retrieved and checked against inclusion criteria. Finally, 30 papers were included in this study and reviewed. Data mining techniques have been reported in development of mobile health apps for three main purposes: data analysis for follow-up and monitoring, early diagnosis and detection for screening purpose, classification/prediction of outcomes, and risk calculation (n = 27); data collection (n = 3); and provision of recommendations (n = 2). The most accurate and frequently applied data mining method was support vector machine; however, decision tree has shown superior performance to enhance mobile apps applied for patients' self-management. Embedded data-mining-based feature in mobile apps, such as case detection, prediction/classification, risk estimation, or collection of patient data, particularly during self-management, would save, apply, and analyze patient data during and after care. More intelligent methods, such as artificial neural networks, fuzzy logic, and genetic algorithms, and even the hybrid methods may result in more patients-centered recommendations, providing education, guidance, alerts, and awareness of personalized output.

A review on computational systems biology of pathogen–host interactions

PubMed Central

Durmuş, Saliha; Çakır, Tunahan; Özgür, Arzucan; Guthke, Reinhard

2015-01-01

Pathogens manipulate the cellular mechanisms of host organisms via pathogen–host interactions (PHIs) in order to take advantage of the capabilities of host cells, leading to infections. The crucial role of these interspecies molecular interactions in initiating and sustaining infections necessitates a thorough understanding of the corresponding mechanisms. Unlike the traditional approach of considering the host or pathogen separately, a systems-level approach, considering the PHI system as a whole is indispensable to elucidate the mechanisms of infection. Following the technological advances in the post-genomic era, PHI data have been produced in large-scale within the last decade. Systems biology-based methods for the inference and analysis of PHI regulatory, metabolic, and protein–protein networks to shed light on infection mechanisms are gaining increasing demand thanks to the availability of omics data. The knowledge derived from the PHIs may largely contribute to the identification of new and more efficient therapeutics to prevent or cure infections. There are recent efforts for the detailed documentation of these experimentally verified PHI data through Web-based databases. Despite these advances in data archiving, there are still large amounts of PHI data in the biomedical literature yet to be discovered, and novel text mining methods are in development to unearth such hidden data. Here, we review a collection of recent studies on computational systems biology of PHIs with a special focus on the methods for the inference and analysis of PHI networks, covering also the Web-based databases and text-mining efforts to unravel the data hidden in the literature. PMID:25914674

Two-step web-mining approach to study geology/geophysics-related open-source software projects

NASA Astrophysics Data System (ADS)

Behrends, Knut; Conze, Ronald

2013-04-01

Geology/geophysics is a highly interdisciplinary science, overlapping with, for instance, physics, biology and chemistry. In today's software-intensive work environments, geoscientists often encounter new open-source software from scientific fields that are only remotely related to the own field of expertise. We show how web-mining techniques can help to carry out systematic discovery and evaluation of such software. In a first step, we downloaded ~500 abstracts (each consisting of ~1 kb UTF-8 text) from agu-fm12.abstractcentral.com. This web site hosts the abstracts of all publications presented at AGU Fall Meeting 2012, the world's largest annual geology/geophysics conference. All abstracts belonged to the category "Earth and Space Science Informatics", an interdisciplinary label cross-cutting many disciplines such as "deep biosphere", "atmospheric research", and "mineral physics". Each publication was represented by a highly structured record with ~20 short data attributes, the largest authorship-record being the unstructured "abstract" field. We processed texts of the abstracts with the statistics software "R" to calculate a corpus and a term-document matrix. Using R package "tm", we applied text-mining techniques to filter data and develop hypotheses about software-development activities happening in various geology/geophysics fields. Analyzing the term-document matrix with basic techniques (e.g., word frequencies, co-occurences, weighting) as well as more complex methods (clustering, classification) several key pieces of information were extracted. For example, text-mining can be used to identify scientists who are also developers of open-source scientific software, and the names of their programming projects and codes can also be identified. In a second step, based on the intermediate results found by processing the conference-abstracts, any new hypotheses can be tested in another webmining subproject: by merging the dataset with open data from github.com and stackoverflow.com. These popular, developer-centric websites have powerful application-programmer interfaces, and follow an open-data policy. In this regard, these sites offer a web-accessible reservoir of information that can be tapped to study questions such as: which open source software projects are eminent in the various geoscience fields? What are the most popular programming languages? How are they trending? Are there any interesting temporal patterns in committer activities? How large are programming teams and how do they change over time? What free software packages exist in the vast realms of related fields? Does the software from these fields have capabilities that might still be useful to me as a researcher, or can help me perform my work better? Are there any open-source projects that might be commercially interesting? This evaluation strategy reveals programming projects that tend to be new. As many important legacy codes are not hosted on open-source code-repositories, the presented search method might overlook some older projects.

Mining the pharmacogenomics literature—a survey of the state of the art

PubMed Central

Cohen, K. Bretonnel; Garten, Yael; Shah, Nigam H.

2012-01-01

This article surveys efforts on text mining of the pharmacogenomics literature, mainly from the period 2008 to 2011. Pharmacogenomics (or pharmacogenetics) is the field that studies how human genetic variation impacts drug response. Therefore, publications span the intersection of research in genotypes, phenotypes and pharmacology, a topic that has increasingly become a focus of active research in recent years. This survey covers efforts dealing with the automatic recognition of relevant named entities (e.g. genes, gene variants and proteins, diseases and other pathological phenomena, drugs and other chemicals relevant for medical treatment), as well as various forms of relations between them. A wide range of text genres is considered, such as scientific publications (abstracts, as well as full texts), patent texts and clinical narratives. We also discuss infrastructure and resources needed for advanced text analytics, e.g. document corpora annotated with corresponding semantic metadata (gold standards and training data), biomedical terminologies and ontologies providing domain-specific background knowledge at different levels of formality and specificity, software architectures for building complex and scalable text analytics pipelines and Web services grounded to them, as well as comprehensive ways to disseminate and interact with the typically huge amounts of semiformal knowledge structures extracted by text mining tools. Finally, we consider some of the novel applications that have already been developed in the field of pharmacogenomic text mining and point out perspectives for future research. PMID:22833496

Mining the pharmacogenomics literature--a survey of the state of the art.

PubMed

Hahn, Udo; Cohen, K Bretonnel; Garten, Yael; Shah, Nigam H

2012-07-01

This article surveys efforts on text mining of the pharmacogenomics literature, mainly from the period 2008 to 2011. Pharmacogenomics (or pharmacogenetics) is the field that studies how human genetic variation impacts drug response. Therefore, publications span the intersection of research in genotypes, phenotypes and pharmacology, a topic that has increasingly become a focus of active research in recent years. This survey covers efforts dealing with the automatic recognition of relevant named entities (e.g. genes, gene variants and proteins, diseases and other pathological phenomena, drugs and other chemicals relevant for medical treatment), as well as various forms of relations between them. A wide range of text genres is considered, such as scientific publications (abstracts, as well as full texts), patent texts and clinical narratives. We also discuss infrastructure and resources needed for advanced text analytics, e.g. document corpora annotated with corresponding semantic metadata (gold standards and training data), biomedical terminologies and ontologies providing domain-specific background knowledge at different levels of formality and specificity, software architectures for building complex and scalable text analytics pipelines and Web services grounded to them, as well as comprehensive ways to disseminate and interact with the typically huge amounts of semiformal knowledge structures extracted by text mining tools. Finally, we consider some of the novel applications that have already been developed in the field of pharmacogenomic text mining and point out perspectives for future research.

Web usage data mining agent

NASA Astrophysics Data System (ADS)

Madiraju, Praveen; Zhang, Yanqing

2002-03-01

When a user logs in to a website, behind the scenes the user leaves his/her impressions, usage patterns and also access patterns in the web servers log file. A web usage mining agent can analyze these web logs to help web developers to improve the organization and presentation of their websites. They can help system administrators in improving the system performance. Web logs provide invaluable help in creating adaptive web sites and also in analyzing the network traffic analysis. This paper presents the design and implementation of a Web usage mining agent for digging in to the web log files.

Integration of Geographical Information Systems and Geophysical Applications with Distributed Computing Technologies.

NASA Astrophysics Data System (ADS)

Pierce, M. E.; Aktas, M. S.; Aydin, G.; Fox, G. C.; Gadgil, H.; Sayar, A.

2005-12-01

We examine the application of Web Service Architectures and Grid-based distributed computing technologies to geophysics and geo-informatics. We are particularly interested in the integration of Geographical Information System (GIS) services with distributed data mining applications. GIS services provide the general purpose framework for building archival data services, real time streaming data services, and map-based visualization services that may be integrated with data mining and other applications through the use of distributed messaging systems and Web Service orchestration tools. Building upon on our previous work in these areas, we present our current research efforts. These include fundamental investigations into increasing XML-based Web service performance, supporting real time data streams, and integrating GIS mapping tools with audio/video collaboration systems for shared display and annotation.

Documenting research with transgender and gender diverse people: protocol for an evidence map and thematic analysis.

PubMed

Marshall, Zack; Welch, Vivian; Thomas, James; Brunger, Fern; Swab, Michelle; Shemilt, Ian; Kaposy, Chris

2017-02-20

There is limited information about how transgender, gender diverse, and Two-Spirit (trans) people have been represented and studied by researchers. The objectives of this study are to (1) map and describe trans research in the social sciences, sciences, humanities, health, education, and business, (2) identify evidence gaps and opportunities for more responsible research with trans people, (3) assess the use of text mining for study identification, and (4) increase access to trans research for key stakeholders through the creation of a web-based evidence map. Study design was informed by community consultations and pilot searches. Eligibility criteria were established to include all original research of any design, including trans people or their health information, and published in English in peer-reviewed journals. A complex electronic search strategy based on relevant concepts in 15 databases was developed to obtain a broad range of results linked to transgender, gender diverse, and Two-Spirit individuals and communities. Searches conducted in early 2015 resulted in 25,242 references after removal of duplicates. Based on the number of references, resources, and an objective to capture upwards of 90% of the existing literature, this study is a good candidate for text mining using Latent Dirichlet Allocation to improve efficiency of the screening process. The following information will be collected for evidence mapping: study topic, study design, methods and data sources, recruitment strategies, sample size, sample demographics, researcher name and affiliation, country where research was conducted, funding source, and year of publication. The proposed research incorporates an extensive search strategy, text mining, and evidence map; it therefore has the potential to build on knowledge in several fields. Review results will increase awareness of existing trans research, identify evidence gaps, and inform strategic research prioritization. Publishing the map online will improve access to research for key stakeholders including community members, policy makers, and healthcare providers. This study will also contribute to knowledge in the area of text mining for study identification by providing an example of how semi-automation performs for screening on title and abstract and on full text.

Large-Scale Overlays and Trends: Visually Mining, Panning and Zooming the Observable Universe.

PubMed

Luciani, Timothy Basil; Cherinka, Brian; Oliphant, Daniel; Myers, Sean; Wood-Vasey, W Michael; Labrinidis, Alexandros; Marai, G Elisabeta

2014-07-01

We introduce a web-based computing infrastructure to assist the visual integration, mining and interactive navigation of large-scale astronomy observations. Following an analysis of the application domain, we design a client-server architecture to fetch distributed image data and to partition local data into a spatial index structure that allows prefix-matching of spatial objects. In conjunction with hardware-accelerated pixel-based overlays and an online cross-registration pipeline, this approach allows the fetching, displaying, panning and zooming of gigabit panoramas of the sky in real time. To further facilitate the integration and mining of spatial and non-spatial data, we introduce interactive trend images-compact visual representations for identifying outlier objects and for studying trends within large collections of spatial objects of a given class. In a demonstration, images from three sky surveys (SDSS, FIRST and simulated LSST results) are cross-registered and integrated as overlays, allowing cross-spectrum analysis of astronomy observations. Trend images are interactively generated from catalog data and used to visually mine astronomy observations of similar type. The front-end of the infrastructure uses the web technologies WebGL and HTML5 to enable cross-platform, web-based functionality. Our approach attains interactive rendering framerates; its power and flexibility enables it to serve the needs of the astronomy community. Evaluation on three case studies, as well as feedback from domain experts emphasize the benefits of this visual approach to the observational astronomy field; and its potential benefits to large scale geospatial visualization in general.

Discriminative and informative features for biomolecular text mining with ensemble feature selection.

PubMed

Van Landeghem, Sofie; Abeel, Thomas; Saeys, Yvan; Van de Peer, Yves

2010-09-15

In the field of biomolecular text mining, black box behavior of machine learning systems currently limits understanding of the true nature of the predictions. However, feature selection (FS) is capable of identifying the most relevant features in any supervised learning setting, providing insight into the specific properties of the classification algorithm. This allows us to build more accurate classifiers while at the same time bridging the gap between the black box behavior and the end-user who has to interpret the results. We show that our FS methodology successfully discards a large fraction of machine-generated features, improving classification performance of state-of-the-art text mining algorithms. Furthermore, we illustrate how FS can be applied to gain understanding in the predictions of a framework for biomolecular event extraction from text. We include numerous examples of highly discriminative features that model either biological reality or common linguistic constructs. Finally, we discuss a number of insights from our FS analyses that will provide the opportunity to considerably improve upon current text mining tools. The FS algorithms and classifiers are available in Java-ML (http://java-ml.sf.net). The datasets are publicly available from the BioNLP'09 Shared Task web site (http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/SharedTask/).

WebGIVI: a web-based gene enrichment analysis and visualization tool.

PubMed

Sun, Liang; Zhu, Yongnan; Mahmood, A S M Ashique; Tudor, Catalina O; Ren, Jia; Vijay-Shanker, K; Chen, Jian; Schmidt, Carl J

2017-05-04

A major challenge of high throughput transcriptome studies is presenting the data to researchers in an interpretable format. In many cases, the outputs of such studies are gene lists which are then examined for enriched biological concepts. One approach to help the researcher interpret large gene datasets is to associate genes and informative terms (iTerm) that are obtained from the biomedical literature using the eGIFT text-mining system. However, examining large lists of iTerm and gene pairs is a daunting task. We have developed WebGIVI, an interactive web-based visualization tool ( http://raven.anr.udel.edu/webgivi/ ) to explore gene:iTerm pairs. WebGIVI was built via Cytoscape and Data Driven Document JavaScript libraries and can be used to relate genes to iTerms and then visualize gene and iTerm pairs. WebGIVI can accept a gene list that is used to retrieve the gene symbols and corresponding iTerm list. This list can be submitted to visualize the gene iTerm pairs using two distinct methods: a Concept Map or a Cytoscape Network Map. In addition, WebGIVI also supports uploading and visualization of any two-column tab separated data. WebGIVI provides an interactive and integrated network graph of gene and iTerms that allows filtering, sorting, and grouping, which can aid biologists in developing hypothesis based on the input gene lists. In addition, WebGIVI can visualize hundreds of nodes and generate a high-resolution image that is important for most of research publications. The source code can be freely downloaded at https://github.com/sunliang3361/WebGIVI . The WebGIVI tutorial is available at http://raven.anr.udel.edu/webgivi/tutorial.php .

pGenN, a gene normalization tool for plant genes and proteins in scientific literature.

PubMed

Ding, Ruoyao; Arighi, Cecilia N; Lee, Jung-Youn; Wu, Cathy H; Vijay-Shanker, K

2015-01-01

Automatically detecting gene/protein names in the literature and connecting them to databases records, also known as gene normalization, provides a means to structure the information buried in free-text literature. Gene normalization is critical for improving the coverage of annotation in the databases, and is an essential component of many text mining systems and database curation pipelines. In this manuscript, we describe a gene normalization system specifically tailored for plant species, called pGenN (pivot-based Gene Normalization). The system consists of three steps: dictionary-based gene mention detection, species assignment, and intra species normalization. We have developed new heuristics to improve each of these phases. We evaluated the performance of pGenN on an in-house expertly annotated corpus consisting of 104 plant relevant abstracts. Our system achieved an F-value of 88.9% (Precision 90.9% and Recall 87.2%) on this corpus, outperforming state-of-art systems presented in BioCreative III. We have processed over 440,000 plant-related Medline abstracts using pGenN. The gene normalization results are stored in a local database for direct query from the pGenN web interface (proteininformationresource.org/pgenn/). The annotated literature corpus is also publicly available through the PIR text mining portal (proteininformationresource.org/iprolink/).

Using sentiment analysis to review patient satisfaction data located on the internet.

PubMed

Hopper, Anthony M; Uriyo, Maria

2015-01-01

The purpose of this paper is to test the usefulness of sentiment analysis and time-to-next-complaint methods in quantifying text-based information located on the internet. As important, the authors demonstrate how managers can use time-to-next-complaint techniques to organize sentiment analysis derived data into useful information, which can be shared with doctors and other staff. The authors used sentiment analysis to review patient feedback for a select group of gynecologists in Virginia. The authors utilized time-to-next-complaint methods along with other techniques to organize this data into meaningful information. The authors demonstrated that sentiment analysis and time-to-next-complaint techniques might be useful tools for healthcare managers who are interested in transforming web-based text into meaningful, quantifiable information. This study has several limitations. For one thing, neither the data set nor the techniques the authors used to analyze it will account for biases that resulted from selection issues related to gender, income, and culture, as well as from other socio-demographic concerns. Additionally, the authors lacked key data concerning patient volumes for the targeted physicians. Finally, it may be difficult to convince doctors to consider web-based comments as truthful, thereby preventing healthcare managers from using data located on the internet. The report illustrates some of the ways in which healthcare administrators can utilize sentiment analysis, along with time-to-next-complaint techniques, to mine web-based, patient comments for meaningful information. The paper is one of the first to illustrate ways in which administrators at clinics and physicians' offices can utilize sentiment analysis and time-to-next-complaint methods to analyze web-based patient comments.

MyWEST: my Web Extraction Software Tool for effective mining of annotations from web-based databanks.

PubMed

Masseroli, Marco; Stella, Andrea; Meani, Natalia; Alcalay, Myriam; Pinciroli, Francesco

2004-12-12

High-throughput technologies create the necessity to mine large amounts of gene annotations from diverse databanks, and to integrate the resulting data. Most databanks can be interrogated only via Web, for a single gene at a time, and query results are generally available only in the HTML format. Although some databanks provide batch retrieval of data via FTP, this requires expertise and resources for locally reimplementing the databank. We developed MyWEST, a tool aimed at researchers without extensive informatics skills or resources, which exploits user-defined templates to easily mine selected annotations from different Web-interfaced databanks, and aggregates and structures results in an automatically updated database. Using microarray results from a model system of retinoic acid-induced differentiation, MyWEST effectively gathered relevant annotations from various biomolecular databanks, highlighted significant biological characteristics and supported a global approach to the understanding of complex cellular mechanisms. MyWEST is freely available for non-profit use at http://www.medinfopoli.polimi.it/MyWEST/

How Artificial Intelligence Can Improve Our Understanding of the Genes Associated with Endometriosis: Natural Language Processing of the PubMed Database

PubMed Central

Mashiach, R.; Cohen, S.; Kedem, A.; Baron, A.; Zajicek, M.; Feldman, I.; Seidman, D.; Soriano, D.

2018-01-01

Endometriosis is a disease characterized by the development of endometrial tissue outside the uterus, but its cause remains largely unknown. Numerous genes have been studied and proposed to help explain its pathogenesis. However, the large number of these candidate genes has made functional validation through experimental methodologies nearly impossible. Computational methods could provide a useful alternative for prioritizing those most likely to be susceptibility genes. Using artificial intelligence applied to text mining, this study analyzed the genes involved in the pathogenesis, development, and progression of endometriosis. The data extraction by text mining of the endometriosis-related genes in the PubMed database was based on natural language processing, and the data were filtered to remove false positives. Using data from the text mining and gene network information as input for the web-based tool, 15,207 endometriosis-related genes were ranked according to their score in the database. Characterization of the filtered gene set through gene ontology, pathway, and network analysis provided information about the numerous mechanisms hypothesized to be responsible for the establishment of ectopic endometrial tissue, as well as the migration, implantation, survival, and proliferation of ectopic endometrial cells. Finally, the human genome was scanned through various databases using filtered genes as a seed to determine novel genes that might also be involved in the pathogenesis of endometriosis but which have not yet been characterized. These genes could be promising candidates to serve as useful diagnostic biomarkers and therapeutic targets in the management of endometriosis. PMID:29750165

How Artificial Intelligence Can Improve Our Understanding of the Genes Associated with Endometriosis: Natural Language Processing of the PubMed Database.

PubMed

Bouaziz, J; Mashiach, R; Cohen, S; Kedem, A; Baron, A; Zajicek, M; Feldman, I; Seidman, D; Soriano, D

2018-01-01

Endometriosis is a disease characterized by the development of endometrial tissue outside the uterus, but its cause remains largely unknown. Numerous genes have been studied and proposed to help explain its pathogenesis. However, the large number of these candidate genes has made functional validation through experimental methodologies nearly impossible. Computational methods could provide a useful alternative for prioritizing those most likely to be susceptibility genes. Using artificial intelligence applied to text mining, this study analyzed the genes involved in the pathogenesis, development, and progression of endometriosis. The data extraction by text mining of the endometriosis-related genes in the PubMed database was based on natural language processing, and the data were filtered to remove false positives. Using data from the text mining and gene network information as input for the web-based tool, 15,207 endometriosis-related genes were ranked according to their score in the database. Characterization of the filtered gene set through gene ontology, pathway, and network analysis provided information about the numerous mechanisms hypothesized to be responsible for the establishment of ectopic endometrial tissue, as well as the migration, implantation, survival, and proliferation of ectopic endometrial cells. Finally, the human genome was scanned through various databases using filtered genes as a seed to determine novel genes that might also be involved in the pathogenesis of endometriosis but which have not yet been characterized. These genes could be promising candidates to serve as useful diagnostic biomarkers and therapeutic targets in the management of endometriosis.

Mining Learning Social Networks for Cooperative Learning with Appropriate Learning Partners in a Problem-Based Learning Environment

ERIC Educational Resources Information Center

Chen, Chih-Ming; Chang, Chia-Cheng

2014-01-01

Many studies have identified web-based cooperative learning as an increasingly popular educational paradigm with potential to increase learner satisfaction and interactions. However, peer-to-peer interaction often suffers barriers owing to a failure to explore useful social interaction information in web-based cooperative learning environments.…

Evaluation of the Kloswall longwall mining system

NASA Astrophysics Data System (ADS)

Guay, P. J.

1982-04-01

A new longwal mining system specifically designed to extract a very deep web (48 inches or deeper) from a longwall panel was studied. Productivity and cost analysis comparing the new mining system with a conventional longwall operation taking a 30 inch wide web is presented. It is shown that the new system will increase annual production and return on investment in most cases. Conceptual drawings and specifications for a high capacity three drum shearer and a unique shield type of roof support specifically designed for very wide web operation are reported. The advantages and problems associated with wide web mining in general and as they relate specifically to the equipment selected for the new mining system are discussed.

BioTextQuest(+): a knowledge integration platform for literature mining and concept discovery.

PubMed

Papanikolaou, Nikolas; Pavlopoulos, Georgios A; Pafilis, Evangelos; Theodosiou, Theodosios; Schneider, Reinhard; Satagopam, Venkata P; Ouzounis, Christos A; Eliopoulos, Aristides G; Promponas, Vasilis J; Iliopoulos, Ioannis

2014-11-15

The iterative process of finding relevant information in biomedical literature and performing bioinformatics analyses might result in an endless loop for an inexperienced user, considering the exponential growth of scientific corpora and the plethora of tools designed to mine PubMed(®) and related biological databases. Herein, we describe BioTextQuest(+), a web-based interactive knowledge exploration platform with significant advances to its predecessor (BioTextQuest), aiming to bridge processes such as bioentity recognition, functional annotation, document clustering and data integration towards literature mining and concept discovery. BioTextQuest(+) enables PubMed and OMIM querying, retrieval of abstracts related to a targeted request and optimal detection of genes, proteins, molecular functions, pathways and biological processes within the retrieved documents. The front-end interface facilitates the browsing of document clustering per subject, the analysis of term co-occurrence, the generation of tag clouds containing highly represented terms per cluster and at-a-glance popup windows with information about relevant genes and proteins. Moreover, to support experimental research, BioTextQuest(+) addresses integration of its primary functionality with biological repositories and software tools able to deliver further bioinformatics services. The Google-like interface extends beyond simple use by offering a range of advanced parameterization for expert users. We demonstrate the functionality of BioTextQuest(+) through several exemplary research scenarios including author disambiguation, functional term enrichment, knowledge acquisition and concept discovery linking major human diseases, such as obesity and ageing. The service is accessible at http://bioinformatics.med.uoc.gr/biotextquest. g.pavlopoulos@gmail.com or georgios.pavlopoulos@esat.kuleuven.be Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Web Mining: Machine Learning for Web Applications.

ERIC Educational Resources Information Center

Chen, Hsinchun; Chau, Michael

2004-01-01

Presents an overview of machine learning research and reviews methods used for evaluating machine learning systems. Ways that machine-learning algorithms were used in traditional information retrieval systems in the "pre-Web" era are described, and the field of Web mining and how machine learning has been used in different Web mining…

Information Retrieval and Text Mining Technologies for Chemistry.

PubMed

Krallinger, Martin; Rabal, Obdulia; Lourenço, Anália; Oyarzabal, Julen; Valencia, Alfonso

2017-06-28

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Raymond, David W.; Gaither, Katherine N.; Polsky, Yarom

Sandia National Laboratories (Sandia) has a long history in developing compact, mobile, very high-speed drilling systems and this technology could be applied to increasing the rate at which boreholes are drilled during a mine accident response. The present study reviews current technical approaches, primarily based on technology developed under other programs, analyzes mine rescue specific requirements to develop a conceptual mine rescue drilling approach, and finally, proposes development of a phased mine rescue drilling system (MRDS) that accomplishes (1) development of rapid drilling MRDS equipment; (2) structuring improved web communication through the Mine Safety & Health Administration (MSHA) web site;more » (3) development of an improved protocol for employment of existing drilling technology in emergencies; (4) deployment of advanced technologies to complement mine rescue drilling operations during emergency events; and (5) preliminary discussion of potential future technology development of specialized MRDS equipment. This phased approach allows for rapid fielding of a basic system for improved rescue drilling, with the ability to improve the system over time at a reasonable cost.« less

A web-based genomic sequence database for the Streptomycetaceae: a tool for systematics and genome mining

USDA-ARS?s Scientific Manuscript database

The ARS Microbial Genome Sequence Database (http://199.133.98.43), a web-based database server, was established utilizing the BIGSdb (Bacterial Isolate Genomics Sequence Database) software package, developed at Oxford University, as a tool to manage multi-locus sequence data for the family Streptomy...

TCGA4U: A Web-Based Genomic Analysis Platform To Explore And Mine TCGA Genomic Data For Translational Research.

PubMed

Huang, Zhenzhen; Duan, Huilong; Li, Haomin

2015-01-01

Large-scale human cancer genomics projects, such as TCGA, generated large genomics data for further study. Exploring and mining these data to obtain meaningful analysis results can help researchers find potential genomics alterations that intervene the development and metastasis of tumors. We developed a web-based gene analysis platform, named TCGA4U, which used statistics methods and models to help translational investigators explore, mine and visualize human cancer genomic characteristic information from the TCGA datasets. Furthermore, through Gene Ontology (GO) annotation and clinical data integration, the genomic data were transformed into biological process, molecular function, cellular component and survival curves to help researchers identify potential driver genes. Clinical researchers without expertise in data analysis will benefit from such a user-friendly genomic analysis platform.

On-Board Mining in the Sensor Web

NASA Astrophysics Data System (ADS)

Tanner, S.; Conover, H.; Graves, S.; Ramachandran, R.; Rushing, J.

2004-12-01

On-board data mining can contribute to many research and engineering applications, including natural hazard detection and prediction, intelligent sensor control, and the generation of customized data products for direct distribution to users. The ability to mine sensor data in real time can also be a critical component of autonomous operations, supporting deep space missions, unmanned aerial and ground-based vehicles (UAVs, UGVs), and a wide range of sensor meshes, webs and grids. On-board processing is expected to play a significant role in the next generation of NASA, Homeland Security, Department of Defense and civilian programs, providing for greater flexibility and versatility in measurements of physical systems. In addition, the use of UAV and UGV systems is increasing in military, emergency response and industrial applications. As research into the autonomy of these vehicles progresses, especially in fleet or web configurations, the applicability of on-board data mining is expected to increase significantly. Data mining in real time on board sensor platforms presents unique challenges. Most notably, the data to be mined is a continuous stream, rather than a fixed store such as a database. This means that the data mining algorithms must be modified to make only a single pass through the data. In addition, the on-board environment requires real time processing with limited computing resources, thus the algorithms must use fixed and relatively small amounts of processing time and memory. The University of Alabama in Huntsville is developing an innovative processing framework for the on-board data and information environment. The Environment for On-Board Processing (EVE) and the Adaptive On-board Data Processing (AODP) projects serve as proofs-of-concept of advanced information systems for remote sensing platforms. The EVE real-time processing infrastructure will upload, schedule and control the execution of processing plans on board remote sensors. These plans provide capabilities for autonomous data mining, classification and feature extraction using both streaming and buffered data sources. A ground-based testbed provides a heterogeneous, embedded hardware and software environment representing both space-based and ground-based sensor platforms, including wireless sensor mesh architectures. The AODP project explores the EVE concepts in the world of sensor-networks, including ad-hoc networks of small sensor platforms.

A Selection of Gilded-Age Resources on the World Wide Web.

ERIC Educational Resources Information Center

Lewis, Alison

1999-01-01

Provides a collection of websites on the Gilded Age that include lesson plans, a chronology, electronic texts, and other resources. Offers a variety of topics such as, but not limited to, African American history, coal mining, political cartoons, architecture, bibliographical information on public figures, and the Spanish-American War. (CMK)

Multi-Filter String Matching and Human-Centric Entity Matching for Information Extraction

ERIC Educational Resources Information Center

Sun, Chong

2012-01-01

More and more information is being generated in text documents, such as Web pages, emails and blogs. To effectively manage this unstructured information, one broadly used approach includes locating relevant content in documents, extracting structured information and integrating the extracted information for querying, mining or further analysis. In…

Data Mining and the Twitter Platform for Prescribed Burn and Wildfire Incident Reporting with Geospatial Applications

NASA Astrophysics Data System (ADS)

Endsley, K.; McCarty, J. L.

2012-12-01

Data mining techniques have been applied to social media in a variety of contexts, from mapping the evolution of the Tahrir Square protests in Egypt to predicting influenza outbreaks. The Twitter platform is a particular favorite due to its robust application programming interface (API) and high throughput. Twitter, Inc. estimated in 2011 that over 2,200 messages or "tweets" are generated every second. Also helpful is Twitter's semblance in operation to the short message service (SMS), better known as "texting," available on cellular phones and the most popular means of wide telecommunications in many developing countries. In the United States, Twitter has been used by a number of federal, state and local officials as well as motivated individuals to report prescribed burns in advance (sometimes as part of a reporting obligation) or to communicate the emergence, response to, and containment of wildfires. These reports are unstructured and, like all Twitter messages, limited to 140 UTF-8 characters. Through internal research and development at the Michigan Tech Research Institute, the authors have developed a data mining routine that gathers potential tweets of interest using the Twitter API, eliminates duplicates ("retweets"), and extracts relevant information such as the approximate size and condition of the fire. Most importantly, the message is geocoded and/or contains approximate locational information, allowing for prescribed and wildland fires to be mapped. Natural language processing techniques, adapted to improve computational performance, are used to tokenize and tag these elements for each tweet. The entire routine is implemented in the Python programming language, using open-source libraries. As such, it is demonstrated in a web-based framework where prescribed burns and/or wildfires are mapped in real time, visualized through a JavaScript-based mapping client in any web browser. The practices demonstrated here generalize to an SMS platform (or any short text-based platform) and thus provide exciting opportunities for the cultivation of fire or other disaster alerts and response here in the U.S. and in the developing world.

Detecting causality from online psychiatric texts using inter-sentential language patterns

PubMed Central

2012-01-01

Background Online psychiatric texts are natural language texts expressing depressive problems, published by Internet users via community-based web services such as web forums, message boards and blogs. Understanding the cause-effect relations embedded in these psychiatric texts can provide insight into the authors’ problems, thus increasing the effectiveness of online psychiatric services. Methods Previous studies have proposed the use of word pairs extracted from a set of sentence pairs to identify cause-effect relations between sentences. A word pair is made up of two words, with one coming from the cause text span and the other from the effect text span. Analysis of the relationship between these words can be used to capture individual word associations between cause and effect sentences. For instance, (broke up, life) and (boyfriend, meaningless) are two word pairs extracted from the sentence pair: “I broke up with my boyfriend. Life is now meaningless to me”. The major limitation of word pairs is that individual words in sentences usually cannot reflect the exact meaning of the cause and effect events, and thus may produce semantically incomplete word pairs, as the previous examples show. Therefore, this study proposes the use of inter-sentential language patterns such as ≪broke up, boyfriend>,

Processing biological literature with customizable Web services supporting interoperable formats.

PubMed

Rak, Rafal; Batista-Navarro, Riza Theresa; Carter, Jacob; Rowley, Andrew; Ananiadou, Sophia

2014-01-01

Web services have become a popular means of interconnecting solutions for processing a body of scientific literature. This has fuelled research on high-level data exchange formats suitable for a given domain and ensuring the interoperability of Web services. In this article, we focus on the biological domain and consider four interoperability formats, BioC, BioNLP, XMI and RDF, that represent domain-specific and generic representations and include well-established as well as emerging specifications. We use the formats in the context of customizable Web services created in our Web-based, text-mining workbench Argo that features an ever-growing library of elementary analytics and capabilities to build and deploy Web services straight from a convenient graphical user interface. We demonstrate a 2-fold customization of Web services: by building task-specific processing pipelines from a repository of available analytics, and by configuring services to accept and produce a combination of input and output data interchange formats. We provide qualitative evaluation of the formats as well as quantitative evaluation of automatic analytics. The latter was carried out as part of our participation in the fourth edition of the BioCreative challenge. Our analytics built into Web services for recognizing biochemical concepts in BioC collections achieved the highest combined scores out of 10 participating teams. Database URL: http://argo.nactem.ac.uk. © The Author(s) 2014. Published by Oxford University Press.

Processing biological literature with customizable Web services supporting interoperable formats

PubMed Central

Rak, Rafal; Batista-Navarro, Riza Theresa; Carter, Jacob; Rowley, Andrew; Ananiadou, Sophia

2014-01-01

Web services have become a popular means of interconnecting solutions for processing a body of scientific literature. This has fuelled research on high-level data exchange formats suitable for a given domain and ensuring the interoperability of Web services. In this article, we focus on the biological domain and consider four interoperability formats, BioC, BioNLP, XMI and RDF, that represent domain-specific and generic representations and include well-established as well as emerging specifications. We use the formats in the context of customizable Web services created in our Web-based, text-mining workbench Argo that features an ever-growing library of elementary analytics and capabilities to build and deploy Web services straight from a convenient graphical user interface. We demonstrate a 2-fold customization of Web services: by building task-specific processing pipelines from a repository of available analytics, and by configuring services to accept and produce a combination of input and output data interchange formats. We provide qualitative evaluation of the formats as well as quantitative evaluation of automatic analytics. The latter was carried out as part of our participation in the fourth edition of the BioCreative challenge. Our analytics built into Web services for recognizing biochemical concepts in BioC collections achieved the highest combined scores out of 10 participating teams. Database URL: http://argo.nactem.ac.uk. PMID:25006225

Chapter 16: text mining for translational bioinformatics.

PubMed

Cohen, K Bretonnel; Hunter, Lawrence E

2013-04-01

Text mining for translational bioinformatics is a new field with tremendous research potential. It is a subfield of biomedical natural language processing that concerns itself directly with the problem of relating basic biomedical research to clinical practice, and vice versa. Applications of text mining fall both into the category of T1 translational research-translating basic science results into new interventions-and T2 translational research, or translational research for public health. Potential use cases include better phenotyping of research subjects, and pharmacogenomic research. A variety of methods for evaluating text mining applications exist, including corpora, structured test suites, and post hoc judging. Two basic principles of linguistic structure are relevant for building text mining applications. One is that linguistic structure consists of multiple levels. The other is that every level of linguistic structure is characterized by ambiguity. There are two basic approaches to text mining: rule-based, also known as knowledge-based; and machine-learning-based, also known as statistical. Many systems are hybrids of the two approaches. Shared tasks have had a strong effect on the direction of the field. Like all translational bioinformatics software, text mining software for translational bioinformatics can be considered health-critical and should be subject to the strictest standards of quality assurance and software testing.

Health Terrain: Visualizing Large Scale Health Data

DTIC Science & Technology

2015-12-01

Text mining ; Data mining . 16. SECURITY  CLASSIFICATION  OF: 17... text   mining  algorithms  to  construct  a  concept  space.  A   browser-­‐based  user  interface  is  developed  to...Public  health  data,  Notifiable  condition  detector,   Text   mining ,  Data   mining   4 of 29 Disease Patient Location Term

pGenN, a Gene Normalization Tool for Plant Genes and Proteins in Scientific Literature

PubMed Central

Ding, Ruoyao; Arighi, Cecilia N.; Lee, Jung-Youn; Wu, Cathy H.; Vijay-Shanker, K.

2015-01-01

Background Automatically detecting gene/protein names in the literature and connecting them to databases records, also known as gene normalization, provides a means to structure the information buried in free-text literature. Gene normalization is critical for improving the coverage of annotation in the databases, and is an essential component of many text mining systems and database curation pipelines. Methods In this manuscript, we describe a gene normalization system specifically tailored for plant species, called pGenN (pivot-based Gene Normalization). The system consists of three steps: dictionary-based gene mention detection, species assignment, and intra species normalization. We have developed new heuristics to improve each of these phases. Results We evaluated the performance of pGenN on an in-house expertly annotated corpus consisting of 104 plant relevant abstracts. Our system achieved an F-value of 88.9% (Precision 90.9% and Recall 87.2%) on this corpus, outperforming state-of-art systems presented in BioCreative III. We have processed over 440,000 plant-related Medline abstracts using pGenN. The gene normalization results are stored in a local database for direct query from the pGenN web interface (proteininformationresource.org/pgenn/). The annotated literature corpus is also publicly available through the PIR text mining portal (proteininformationresource.org/iprolink/). PMID:26258475

Data warehousing as a basis for web-based documentation of data mining and analysis.

PubMed

Karlsson, J; Eklund, P; Hallgren, C G; Sjödin, J G

1999-01-01

In this paper we present a case study for data warehousing intended to support data mining and analysis. We also describe a prototype for data retrieval. Further we discuss some technical issues related to a particular choice of a patient record environment.

Influence of plankton mercury dynamics and trophic pathways on mercury concentrations of top predator fish of a mining-impacted reservoir

USGS Publications Warehouse

Stewart, A.R.; Saiki, M.K.; Kuwabara, J.S.; Alpers, Charles N.; Marvin-DiPasquale, M.; Krabbenhoft, D.P.

2008-01-01

Physical and biogeochemical characteristics of the aquatic environment that affect growth dynamics of phytoplankton and the zooplankton communities that depend on them may also affect uptake of methylmercury (MeHg) into the pelagic food web of oligotrophic reservoirs. We evaluated changes in the quality and quantity of suspended particulate material, zooplankton taxonomy, and MeHg concentrations coincident with seasonal changes in water storage of a mining-impacted reservoir in northern California, USA. MeHg concentrations in bulk zooplankton increased from 4 ng??g-1 at low water to 77 ?? 6.1 ng??g-1 at high water and were positively correlated with cladoceran biomass (r = 0.66) and negatively correlated with rotifer biomass (r = -0.65). Stable isotope analysis revealed overall higher MeHg concentrations in the pelagic-based food web relative to the benthic-based food web. Statistically similar patterns of trophic enrichment of MeHg (slopes) for the pelagic and benthic food webs and slightly higher MeHg concentrations in zooplankton than in benthic invertebrates suggest that the difference in MeHg bioaccumulation among trophic pathways is set at the base of the food webs. These results suggest an important role for plankton dynamics in driving the MeHg content of zooplankton and ultimately MeHg bioaccumulation in top predators in pelagic-based food webs. ?? 2008 NRC.

DiseaseConnect: a comprehensive web server for mechanism-based disease–disease connections

PubMed Central

Liu, Chun-Chi; Tseng, Yu-Ting; Li, Wenyuan; Wu, Chia-Yu; Mayzus, Ilya; Rzhetsky, Andrey; Sun, Fengzhu; Waterman, Michael; Chen, Jeremy J. W.; Chaudhary, Preet M.; Loscalzo, Joseph; Crandall, Edward; Zhou, Xianghong Jasmine

2014-01-01

The DiseaseConnect (http://disease-connect.org) is a web server for analysis and visualization of a comprehensive knowledge on mechanism-based disease connectivity. The traditional disease classification system groups diseases with similar clinical symptoms and phenotypic traits. Thus, diseases with entirely different pathologies could be grouped together, leading to a similar treatment design. Such problems could be avoided if diseases were classified based on their molecular mechanisms. Connecting diseases with similar pathological mechanisms could inspire novel strategies on the effective repositioning of existing drugs and therapies. Although there have been several studies attempting to generate disease connectivity networks, they have not yet utilized the enormous and rapidly growing public repositories of disease-related omics data and literature, two primary resources capable of providing insights into disease connections at an unprecedented level of detail. Our DiseaseConnect, the first public web server, integrates comprehensive omics and literature data, including a large amount of gene expression data, Genome-Wide Association Studies catalog, and text-mined knowledge, to discover disease–disease connectivity via common molecular mechanisms. Moreover, the clinical comorbidity data and a comprehensive compilation of known drug–disease relationships are additionally utilized for advancing the understanding of the disease landscape and for facilitating the mechanism-based development of new drug treatments. PMID:24895436

SureChEMBL: a large-scale, chemically annotated patent document database.

PubMed

Papadatos, George; Davies, Mark; Dedman, Nathan; Chambers, Jon; Gaulton, Anna; Siddle, James; Koks, Richard; Irvine, Sean A; Pettersson, Joe; Goncharoff, Nicko; Hersey, Anne; Overington, John P

2016-01-04

SureChEMBL is a publicly available large-scale resource containing compounds extracted from the full text, images and attachments of patent documents. The data are extracted from the patent literature according to an automated text and image-mining pipeline on a daily basis. SureChEMBL provides access to a previously unavailable, open and timely set of annotated compound-patent associations, complemented with sophisticated combined structure and keyword-based search capabilities against the compound repository and patent document corpus; given the wealth of knowledge hidden in patent documents, analysis of SureChEMBL data has immediate applications in drug discovery, medicinal chemistry and other commercial areas of chemical science. Currently, the database contains 17 million compounds extracted from 14 million patent documents. Access is available through a dedicated web-based interface and data downloads at: https://www.surechembl.org/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

ezTag: tagging biomedical concepts via interactive learning.

PubMed

Kwon, Dongseop; Kim, Sun; Wei, Chih-Hsuan; Leaman, Robert; Lu, Zhiyong

2018-05-18

Recently, advanced text-mining techniques have been shown to speed up manual data curation by providing human annotators with automated pre-annotations generated by rules or machine learning models. Due to the limited training data available, however, current annotation systems primarily focus only on common concept types such as genes or diseases. To support annotating a wide variety of biological concepts with or without pre-existing training data, we developed ezTag, a web-based annotation tool that allows curators to perform annotation and provide training data with humans in the loop. ezTag supports both abstracts in PubMed and full-text articles in PubMed Central. It also provides lexicon-based concept tagging as well as the state-of-the-art pre-trained taggers such as TaggerOne, GNormPlus and tmVar. ezTag is freely available at http://eztag.bioqrator.org.

SureChEMBL: a large-scale, chemically annotated patent document database

PubMed Central

Papadatos, George; Davies, Mark; Dedman, Nathan; Chambers, Jon; Gaulton, Anna; Siddle, James; Koks, Richard; Irvine, Sean A.; Pettersson, Joe; Goncharoff, Nicko; Hersey, Anne; Overington, John P.

2016-01-01

SureChEMBL is a publicly available large-scale resource containing compounds extracted from the full text, images and attachments of patent documents. The data are extracted from the patent literature according to an automated text and image-mining pipeline on a daily basis. SureChEMBL provides access to a previously unavailable, open and timely set of annotated compound-patent associations, complemented with sophisticated combined structure and keyword-based search capabilities against the compound repository and patent document corpus; given the wealth of knowledge hidden in patent documents, analysis of SureChEMBL data has immediate applications in drug discovery, medicinal chemistry and other commercial areas of chemical science. Currently, the database contains 17 million compounds extracted from 14 million patent documents. Access is available through a dedicated web-based interface and data downloads at: https://www.surechembl.org/. PMID:26582922

Socio-contextual Network Mining for User Assistance in Web-based Knowledge Gathering Tasks

NASA Astrophysics Data System (ADS)

Rajendran, Balaji; Kombiah, Iyakutti

Web-based Knowledge Gathering (WKG) is a specialized and complex information seeking task carried out by many users on the web, for their various learning, and decision-making requirements. We construct a contextual semantic structure by observing the actions of the users involved in WKG task, in order to gain an understanding of their task and requirement. We also build a knowledge warehouse in the form of a master Semantic Link Network (SLX) that accommodates and assimilates all the contextual semantic structures. This master SLX, which is a socio-contextual network, is then mined to provide contextual inputs to the current users through their agents. We validated our approach through experiments and analyzed the benefits to the users in terms of resource explorations and the time saved. The results are positive enough to motivate us to implement in a larger scale.

NCBI GEO: mining tens of millions of expression profiles--database and tools update.

PubMed

Barrett, Tanya; Troup, Dennis B; Wilhite, Stephen E; Ledoux, Pierre; Rudnev, Dmitry; Evangelista, Carlos; Kim, Irene F; Soboleva, Alexandra; Tomashevsky, Maxim; Edgar, Ron

2007-01-01

The Gene Expression Omnibus (GEO) repository at the National Center for Biotechnology Information (NCBI) archives and freely disseminates microarray and other forms of high-throughput data generated by the scientific community. The database has a minimum information about a microarray experiment (MIAME)-compliant infrastructure that captures fully annotated raw and processed data. Several data deposit options and formats are supported, including web forms, spreadsheets, XML and Simple Omnibus Format in Text (SOFT). In addition to data storage, a collection of user-friendly web-based interfaces and applications are available to help users effectively explore, visualize and download the thousands of experiments and tens of millions of gene expression patterns stored in GEO. This paper provides a summary of the GEO database structure and user facilities, and describes recent enhancements to database design, performance, submission format options, data query and retrieval utilities. GEO is accessible at http://www.ncbi.nlm.nih.gov/geo/

Cart'Eaux: an automatic mapping procedure for wastewater networks using machine learning and data mining

NASA Astrophysics Data System (ADS)

Bailly, J. S.; Delenne, C.; Chahinian, N.; Bringay, S.; Commandré, B.; Chaumont, M.; Derras, M.; Deruelle, L.; Roche, M.; Rodriguez, F.; Subsol, G.; Teisseire, M.

2017-12-01

In France, local government institutions must establish a detailed description of wastewater networks. The information should be available, but it remains fragmented (different formats held by different stakeholders) and incomplete. In the "Cart'Eaux" project, a multidisciplinary team, including an industrial partner, develops a global methodology using Machine Learning and Data Mining approaches applied to various types of large data to recover information in the aim of mapping urban sewage systems for hydraulic modelling. Deep-learning is first applied using a Convolution Neural Network to localize manhole covers on 5 cm resolution aerial RGB images. The detected manhole covers are then automatically connected using a tree-shaped graph constrained by industry rules. Based on a Delaunay triangulation, connections are chosen to minimize a cost function depending on pipe length, slope and possible intersection with roads or buildings. A stochastic version of this algorithm is currently being developed to account for positional uncertainty and detection errors, and generate sets of probable networks. As more information is required for hydraulic modeling (slopes, diameters, materials, etc.), text data mining is used to extract network characteristics from data posted on the Web or available through governmental or specific databases. Using an appropriate list of keywords, the web is scoured for documents which are saved in text format. The thematic entities are identified and linked to the surrounding spatial and temporal entities. The methodology is developed and tested on two towns in southern France. The primary results are encouraging: 54% of manhole covers are detected with few false detections, enabling the reconstruction of probable networks. The data mining results are still being investigated. It is clear at this stage that getting numerical values on specific pipes will be challenging. Thus, when no information is found, decision rules will be used to assign admissible numerical values to enable the final hydraulic modelling. Consequently, sensitivity analysis of the hydraulic model will be performed to take into account the uncertainty associated with each piece of information. Project funded by the European Regional Development Fund and the Occitanie Region.

Asymmetric threat data mining and knowledge discovery

NASA Astrophysics Data System (ADS)

Gilmore, John F.; Pagels, Michael A.; Palk, Justin

2001-03-01

Asymmetric threats differ from the conventional force-on- force military encounters that the Defense Department has historically been trained to engage. Terrorism by its nature is now an operational activity that is neither easily detected or countered as its very existence depends on small covert attacks exploiting the element of surprise. But terrorism does have defined forms, motivations, tactics and organizational structure. Exploiting a terrorism taxonomy provides the opportunity to discover and assess knowledge of terrorist operations. This paper describes the Asymmetric Threat Terrorist Assessment, Countering, and Knowledge (ATTACK) system. ATTACK has been developed to (a) data mine open source intelligence (OSINT) information from web-based newspaper sources, video news web casts, and actual terrorist web sites, (b) evaluate this information against a terrorism taxonomy, (c) exploit country/region specific social, economic, political, and religious knowledge, and (d) discover and predict potential terrorist activities and association links. Details of the asymmetric threat structure and the ATTACK system architecture are presented with results of an actual terrorist data mining and knowledge discovery test case shown.

Text mining for neuroanatomy using WhiteText with an updated corpus and a new web application

PubMed Central

French, Leon; Liu, Po; Marais, Olivia; Koreman, Tianna; Tseng, Lucia; Lai, Artemis; Pavlidis, Paul

2015-01-01

We describe the WhiteText project, and its progress towards automatically extracting statements of neuroanatomical connectivity from text. We review progress to date on the three main steps of the project: recognition of brain region mentions, standardization of brain region mentions to neuroanatomical nomenclature, and connectivity statement extraction. We further describe a new version of our manually curated corpus that adds 2,111 connectivity statements from 1,828 additional abstracts. Cross-validation classification within the new corpus replicates results on our original corpus, recalling 67% of connectivity statements at 51% precision. The resulting merged corpus provides 5,208 connectivity statements that can be used to seed species-specific connectivity matrices and to better train automated techniques. Finally, we present a new web application that allows fast interactive browsing of the over 70,000 sentences indexed by the system, as a tool for accessing the data and assisting in further curation. Software and data are freely available at http://www.chibi.ubc.ca/WhiteText/. PMID:26052282

Simple, Scalable, Script-based, Science Processor for Measurements - Data Mining Edition (S4PM-DME)

NASA Astrophysics Data System (ADS)

Pham, L. B.; Eng, E. K.; Lynnes, C. S.; Berrick, S. W.; Vollmer, B. E.

2005-12-01

The S4PM-DME is the Goddard Earth Sciences Distributed Active Archive Center's (GES DAAC) web-based data mining environment. The S4PM-DME replaces the Near-line Archive Data Mining (NADM) system with a better web environment and a richer set of production rules. S4PM-DME enables registered users to submit and execute custom data mining algorithms. The S4PM-DME system uses the GES DAAC developed Simple Scalable Script-based Science Processor for Measurements (S4PM) to automate tasks and perform the actual data processing. A web interface allows the user to access the S4PM-DME system. The user first develops personalized data mining algorithm on his/her home platform and then uploads them to the S4PM-DME system. Algorithms in C and FORTRAN languages are currently supported. The user developed algorithm is automatically audited for any potential security problems before it is installed within the S4PM-DME system and made available to the user. Once the algorithm has been installed the user can promote the algorithm to the "operational" environment. From here the user can search and order the data available in the GES DAAC archive for his/her science algorithm. The user can also set up a processing subscription. The subscription will automatically process new data as it becomes available in the GES DAAC archive. The generated mined data products are then made available for FTP pickup. The benefits of using S4PM-DME are 1) to decrease the downloading time it typically takes a user to transfer the GES DAAC data to his/her system thus off-load the heavy network traffic, 2) to free-up the load on their system, and last 3) to utilize the rich and abundance ocean, atmosphere data from the MODIS and AIRS instruments available from the GES DAAC.

Thematic clustering of text documents using an EM-based approach

PubMed Central

2012-01-01

Clustering textual contents is an important step in mining useful information on the web or other text-based resources. The common task in text clustering is to handle text in a multi-dimensional space, and to partition documents into groups, where each group contains documents that are similar to each other. However, this strategy lacks a comprehensive view for humans in general since it cannot explain the main subject of each cluster. Utilizing semantic information can solve this problem, but it needs a well-defined ontology or pre-labeled gold standard set. In this paper, we present a thematic clustering algorithm for text documents. Given text, subject terms are extracted and used for clustering documents in a probabilistic framework. An EM approach is used to ensure documents are assigned to correct subjects, hence it converges to a locally optimal solution. The proposed method is distinctive because its results are sufficiently explanatory for human understanding as well as efficient for clustering performance. The experimental results show that the proposed method provides a competitive performance compared to other state-of-the-art approaches. We also show that the extracted themes from the MEDLINE® dataset represent the subjects of clusters reasonably well. PMID:23046528

Adaptive semantic tag mining from heterogeneous clinical research texts.

PubMed

Hao, T; Weng, C

2015-01-01

To develop an adaptive approach to mine frequent semantic tags (FSTs) from heterogeneous clinical research texts. We develop a "plug-n-play" framework that integrates replaceable unsupervised kernel algorithms with formatting, functional, and utility wrappers for FST mining. Temporal information identification and semantic equivalence detection were two example functional wrappers. We first compared this approach's recall and efficiency for mining FSTs from ClinicalTrials.gov to that of a recently published tag-mining algorithm. Then we assessed this approach's adaptability to two other types of clinical research texts: clinical data requests and clinical trial protocols, by comparing the prevalence trends of FSTs across three texts. Our approach increased the average recall and speed by 12.8% and 47.02% respectively upon the baseline when mining FSTs from ClinicalTrials.gov, and maintained an overlap in relevant FSTs with the base- line ranging between 76.9% and 100% for varying FST frequency thresholds. The FSTs saturated when the data size reached 200 documents. Consistent trends in the prevalence of FST were observed across the three texts as the data size or frequency threshold changed. This paper contributes an adaptive tag-mining framework that is scalable and adaptable without sacrificing its recall. This component-based architectural design can be potentially generalizable to improve the adaptability of other clinical text mining methods.

Data-driven decision support for radiologists: re-using the National Lung Screening Trial dataset for pulmonary nodule management.

PubMed

Morrison, James J; Hostetter, Jason; Wang, Kenneth; Siegel, Eliot L

2015-02-01

Real-time mining of large research trial datasets enables development of case-based clinical decision support tools. Several applicable research datasets exist including the National Lung Screening Trial (NLST), a dataset unparalleled in size and scope for studying population-based lung cancer screening. Using these data, a clinical decision support tool was developed which matches patient demographics and lung nodule characteristics to a cohort of similar patients. The NLST dataset was converted into Structured Query Language (SQL) tables hosted on a web server, and a web-based JavaScript application was developed which performs real-time queries. JavaScript is used for both the server-side and client-side language, allowing for rapid development of a robust client interface and server-side data layer. Real-time data mining of user-specified patient cohorts achieved a rapid return of cohort cancer statistics and lung nodule distribution information. This system demonstrates the potential of individualized real-time data mining using large high-quality clinical trial datasets to drive evidence-based clinical decision-making.

Astrophysical data mining with GPU. A case study: Genetic classification of globular clusters

NASA Astrophysics Data System (ADS)

Cavuoti, S.; Garofalo, M.; Brescia, M.; Paolillo, M.; Pescape', A.; Longo, G.; Ventre, G.

2014-01-01

We present a multi-purpose genetic algorithm, designed and implemented with GPGPU/CUDA parallel computing technology. The model was derived from our CPU serial implementation, named GAME (Genetic Algorithm Model Experiment). It was successfully tested and validated on the detection of candidate Globular Clusters in deep, wide-field, single band HST images. The GPU version of GAME will be made available to the community by integrating it into the web application DAMEWARE (DAta Mining Web Application REsource, http://dame.dsf.unina.it/beta_info.html), a public data mining service specialized on massive astrophysical data. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm leads to a speedup of a factor of 200× in the training phase with respect to the CPU based version.

Analyzing depression tendency of web posts using an event-driven depression tendency warning model.

PubMed

Tung, Chiaming; Lu, Wenhsiang

2016-01-01

The Internet has become a platform to express individual moods/feelings of daily life, where authors share their thoughts in web blogs, micro-blogs, forums, bulletin board systems or other media. In this work, we investigate text-mining technology to analyze and predict the depression tendency of web posts. In this paper, we defined depression factors, which include negative events, negative emotions, symptoms, and negative thoughts from web posts. We proposed an enhanced event extraction (E3) method to automatically extract negative event terms. In addition, we also proposed an event-driven depression tendency warning (EDDTW) model to predict the depression tendency of web bloggers or post authors by analyzing their posted articles. We compare the performance among the proposed EDDTW model, negative emotion evaluation (NEE) model, and the diagnostic and statistical manual of mental disorders-based depression tendency evaluation method. The EDDTW model obtains the best recall rate and F-measure at 0.668 and 0.624, respectively, while the diagnostic and statistical manual of mental disorders-based method achieves the best precision rate of 0.666. The main reason is that our enhanced event extraction method can increase recall rate by enlarging the negative event lexicon at the expense of precision. Our EDDTW model can also be used to track the change or trend of depression tendency for each post author. The depression tendency trend can help doctors to diagnose and even track depression of web post authors more efficiently. This paper presents an E3 method to automatically extract negative event terms in web posts. We also proposed a new EDDTW model to predict the depression tendency of web posts and possibly help bloggers or post authors to early detect major depressive disorder. Copyright © 2015 Elsevier B.V. All rights reserved.

BAGEL4: a user-friendly web server to thoroughly mine RiPPs and bacteriocins.

PubMed

van Heel, Auke J; de Jong, Anne; Song, Chunxu; Viel, Jakob H; Kok, Jan; Kuipers, Oscar P

2018-05-21

Interest in secondary metabolites such as RiPPs (ribosomally synthesized and posttranslationally modified peptides) is increasing worldwide. To facilitate the research in this field we have updated our mining web server. BAGEL4 is faster than its predecessor and is now fully independent from ORF-calling. Gene clusters of interest are discovered using the core-peptide database and/or through HMM motifs that are present in associated context genes. The databases used for mining have been updated and extended with literature references and links to UniProt and NCBI. Additionally, we have included automated promoter and terminator prediction and the option to upload RNA expression data, which can be displayed along with the identified clusters. Further improvements include the annotation of the context genes, which is now based on a fast blast against the prokaryote part of the UniRef90 database, and the improved web-BLAST feature that dynamically loads structural data such as internal cross-linking from UniProt. Overall BAGEL4 provides the user with more information through a user-friendly web-interface which simplifies data evaluation. BAGEL4 is freely accessible at http://bagel4.molgenrug.nl.

IPAT: a freely accessible software tool for analyzing multiple patent documents with inbuilt landscape visualizer.

PubMed

Ajay, Dara; Gangwal, Rahul P; Sangamwar, Abhay T

2015-01-01

Intelligent Patent Analysis Tool (IPAT) is an online data retrieval tool, operated based on text mining algorithm to extract specific patent information in a predetermined pattern into an Excel sheet. The software is designed and developed to retrieve and analyze technology information from multiple patent documents and generate various patent landscape graphs and charts. The software is C# coded in visual studio 2010, which extracts the publicly available patent information from the web pages like Google Patent and simultaneously study the various technology trends based on user-defined parameters. In other words, IPAT combined with the manual categorization will act as an excellent technology assessment tool in competitive intelligence and due diligence for predicting the future R&D forecast.

Trends of Serious Games Research from 2007 to 2017: A Bibliometric Analysis

ERIC Educational Resources Information Center

Çiftci, Serdar

2018-01-01

This study examines the tendencies of studies carried out using text mining methods under the title of "serious game". A query was run for the "serious game" keyword in the Web of Science search engine to acquire the data. The study included publications that were scanned in the SCI-EXPANDED, SSCI and A&HCI indices between…

Addressing Information Proliferation: Applications of Information Extraction and Text Mining

ERIC Educational Resources Information Center

Li, Jingjing

2013-01-01

The advent of the Internet and the ever-increasing capacity of storage media have made it easy to store, deliver, and share enormous volumes of data, leading to a proliferation of information on the Web, in online libraries, on news wires, and almost everywhere in our daily lives. Since our ability to process and absorb this information remains…

Recognition of pornographic web pages by classifying texts and images.

PubMed

Hu, Weiming; Wu, Ou; Chen, Zhouyao; Fu, Zhouyu; Maybank, Steve

2007-06-01

With the rapid development of the World Wide Web, people benefit more and more from the sharing of information. However, Web pages with obscene, harmful, or illegal content can be easily accessed. It is important to recognize such unsuitable, offensive, or pornographic Web pages. In this paper, a novel framework for recognizing pornographic Web pages is described. A C4.5 decision tree is used to divide Web pages, according to content representations, into continuous text pages, discrete text pages, and image pages. These three categories of Web pages are handled, respectively, by a continuous text classifier, a discrete text classifier, and an algorithm that fuses the results from the image classifier and the discrete text classifier. In the continuous text classifier, statistical and semantic features are used to recognize pornographic texts. In the discrete text classifier, the naive Bayes rule is used to calculate the probability that a discrete text is pornographic. In the image classifier, the object's contour-based features are extracted to recognize pornographic images. In the text and image fusion algorithm, the Bayes theory is used to combine the recognition results from images and texts. Experimental results demonstrate that the continuous text classifier outperforms the traditional keyword-statistics-based classifier, the contour-based image classifier outperforms the traditional skin-region-based image classifier, the results obtained by our fusion algorithm outperform those by either of the individual classifiers, and our framework can be adapted to different categories of Web pages.

Sting_RDB: a relational database of structural parameters for protein analysis with support for data warehousing and data mining.

PubMed

Oliveira, S R M; Almeida, G V; Souza, K R R; Rodrigues, D N; Kuser-Falcão, P R; Yamagishi, M E B; Santos, E H; Vieira, F D; Jardine, J G; Neshich, G

2007-10-05

An effective strategy for managing protein databases is to provide mechanisms to transform raw data into consistent, accurate and reliable information. Such mechanisms will greatly reduce operational inefficiencies and improve one's ability to better handle scientific objectives and interpret the research results. To achieve this challenging goal for the STING project, we introduce Sting_RDB, a relational database of structural parameters for protein analysis with support for data warehousing and data mining. In this article, we highlight the main features of Sting_RDB and show how a user can explore it for efficient and biologically relevant queries. Considering its importance for molecular biologists, effort has been made to advance Sting_RDB toward data quality assessment. To the best of our knowledge, Sting_RDB is one of the most comprehensive data repositories for protein analysis, now also capable of providing its users with a data quality indicator. This paper differs from our previous study in many aspects. First, we introduce Sting_RDB, a relational database with mechanisms for efficient and relevant queries using SQL. Sting_rdb evolved from the earlier, text (flat file)-based database, in which data consistency and integrity was not guaranteed. Second, we provide support for data warehousing and mining. Third, the data quality indicator was introduced. Finally and probably most importantly, complex queries that could not be posed on a text-based database, are now easily implemented. Further details are accessible at the Sting_RDB demo web page: http://www.cbi.cnptia.embrapa.br/StingRDB.

HC StratoMineR: A Web-Based Tool for the Rapid Analysis of High-Content Datasets.

PubMed

Omta, Wienand A; van Heesbeen, Roy G; Pagliero, Romina J; van der Velden, Lieke M; Lelieveld, Daphne; Nellen, Mehdi; Kramer, Maik; Yeong, Marley; Saeidi, Amir M; Medema, Rene H; Spruit, Marco; Brinkkemper, Sjaak; Klumperman, Judith; Egan, David A

2016-10-01

High-content screening (HCS) can generate large multidimensional datasets and when aligned with the appropriate data mining tools, it can yield valuable insights into the mechanism of action of bioactive molecules. However, easy-to-use data mining tools are not widely available, with the result that these datasets are frequently underutilized. Here, we present HC StratoMineR, a web-based tool for high-content data analysis. It is a decision-supportive platform that guides even non-expert users through a high-content data analysis workflow. HC StratoMineR is built by using My Structured Query Language for storage and querying, PHP: Hypertext Preprocessor as the main programming language, and jQuery for additional user interface functionality. R is used for statistical calculations, logic and data visualizations. Furthermore, C++ and graphical processor unit power is diffusely embedded in R by using the rcpp and rpud libraries for operations that are computationally highly intensive. We show that we can use HC StratoMineR for the analysis of multivariate data from a high-content siRNA knock-down screen and a small-molecule screen. It can be used to rapidly filter out undesirable data; to select relevant data; and to perform quality control, data reduction, data exploration, morphological hit picking, and data clustering. Our results demonstrate that HC StratoMineR can be used to functionally categorize HCS hits and, thus, provide valuable information for hit prioritization.

A Node Linkage Approach for Sequential Pattern Mining

PubMed Central

Navarro, Osvaldo; Cumplido, René; Villaseñor-Pineda, Luis; Feregrino-Uribe, Claudia; Carrasco-Ochoa, Jesús Ariel

2014-01-01

Sequential Pattern Mining is a widely addressed problem in data mining, with applications such as analyzing Web usage, examining purchase behavior, and text mining, among others. Nevertheless, with the dramatic increase in data volume, the current approaches prove inefficient when dealing with large input datasets, a large number of different symbols and low minimum supports. In this paper, we propose a new sequential pattern mining algorithm, which follows a pattern-growth scheme to discover sequential patterns. Unlike most pattern growth algorithms, our approach does not build a data structure to represent the input dataset, but instead accesses the required sequences through pseudo-projection databases, achieving better runtime and reducing memory requirements. Our algorithm traverses the search space in a depth-first fashion and only preserves in memory a pattern node linkage and the pseudo-projections required for the branch being explored at the time. Experimental results show that our new approach, the Node Linkage Depth-First Traversal algorithm (NLDFT), has better performance and scalability in comparison with state of the art algorithms. PMID:24933123

Text mining and natural language processing approaches for automatic categorization of lay requests to web-based expert forums.

PubMed

Himmel, Wolfgang; Reincke, Ulrich; Michelmann, Hans Wilhelm

2009-07-22

Both healthy and sick people increasingly use electronic media to obtain medical information and advice. For example, Internet users may send requests to Web-based expert forums, or so-called "ask the doctor" services. To automatically classify lay requests to an Internet medical expert forum using a combination of different text-mining strategies. We first manually classified a sample of 988 requests directed to a involuntary childlessness forum on the German website "Rund ums Baby" ("Everything about Babies") into one or more of 38 categories belonging to two dimensions ("subject matter" and "expectations"). After creating start and synonym lists, we calculated the average Cramer's V statistic for the association of each word with each category. We also used principle component analysis and singular value decomposition as further text-mining strategies. With these measures we trained regression models and determined, on the basis of best regression models, for any request the probability of belonging to each of the 38 different categories, with a cutoff of 50%. Recall and precision of a test sample were calculated as a measure of quality for the automatic classification. According to the manual classification of 988 documents, 102 (10%) documents fell into the category "in vitro fertilization (IVF)," 81 (8%) into the category "ovulation," 79 (8%) into "cycle," and 57 (6%) into "semen analysis." These were the four most frequent categories in the subject matter dimension (consisting of 32 categories). The expectation dimension comprised six categories; we classified 533 documents (54%) as "general information" and 351 (36%) as a wish for "treatment recommendations." The generation of indicator variables based on the chi-square analysis and Cramer's V proved to be the best approach for automatic classification in about half of the categories. In combination with the two other approaches, 100% precision and 100% recall were realized in 18 (47%) out of the 38 categories in the test sample. For 35 (92%) categories, precision and recall were better than 80%. For some categories, the input variables (ie, "words") also included variables from other categories, most often with a negative sign. For example, absence of words predictive for "menstruation" was a strong indicator for the category "pregnancy test." Our approach suggests a way of automatically classifying and analyzing unstructured information in Internet expert forums. The technique can perform a preliminary categorization of new requests and help Internet medical experts to better handle the mass of information and to give professional feedback.

Evaluation of longitudinal tracking and data mining for an imaging informatics-based multiple sclerosis e-folder (Conference Presentation)

NASA Astrophysics Data System (ADS)

Ma, Kevin C.; Forsyth, Sydney; Amezcua, Lilyana; Liu, Brent J.

2017-03-01

We have designed and developed a multiple sclerosis eFolder system for patient data storage, image viewing, and automatic lesion quantification results to allow patient tracking. The web-based system aims to be integrated in DICOM-compliant clinical and research environments to aid clinicians in patient treatments and data analysis. The system quantifies lesion volumes, identify and register lesion locations to track shifts in volume and quantity of lesions in a longitudinal study. We aim to evaluate the two most important features of the system, data mining and longitudinal lesion tracking, to demonstrate the MS eFolder's capability in improving clinical workflow efficiency and outcome analysis for research. In order to evaluate data mining capabilities, we have collected radiological and neurological data from 72 patients, 36 Caucasian and 36 Hispanic matched by gender, disease duration, and age. Data analysis on those patients based on ethnicity is performed, and analysis results are displayed by the system's web-based user interface. The data mining module is able to successfully separate Hispanic and Caucasian patients and compare their disease profiles. For longitudinal lesion tracking, we have collected 4 longitudinal cases and simulated different lesion growths over the next year. As a result, the eFolder is able to detect changes in lesion volume and identifying lesions with the most changes. Data mining and lesion tracking evaluation results show high potential of eFolder's usefulness in patientcare and informatics research for multiple sclerosis.

Vaccine adverse event text mining system for extracting features from vaccine safety reports.

PubMed

Botsis, Taxiarchis; Buttolph, Thomas; Nguyen, Michael D; Winiecki, Scott; Woo, Emily Jane; Ball, Robert

2012-01-01

To develop and evaluate a text mining system for extracting key clinical features from vaccine adverse event reporting system (VAERS) narratives to aid in the automated review of adverse event reports. Based upon clinical significance to VAERS reviewing physicians, we defined the primary (diagnosis and cause of death) and secondary features (eg, symptoms) for extraction. We built a novel vaccine adverse event text mining (VaeTM) system based on a semantic text mining strategy. The performance of VaeTM was evaluated using a total of 300 VAERS reports in three sequential evaluations of 100 reports each. Moreover, we evaluated the VaeTM contribution to case classification; an information retrieval-based approach was used for the identification of anaphylaxis cases in a set of reports and was compared with two other methods: a dedicated text classifier and an online tool. The performance metrics of VaeTM were text mining metrics: recall, precision and F-measure. We also conducted a qualitative difference analysis and calculated sensitivity and specificity for classification of anaphylaxis cases based on the above three approaches. VaeTM performed best in extracting diagnosis, second level diagnosis, drug, vaccine, and lot number features (lenient F-measure in the third evaluation: 0.897, 0.817, 0.858, 0.874, and 0.914, respectively). In terms of case classification, high sensitivity was achieved (83.1%); this was equal and better compared to the text classifier (83.1%) and the online tool (40.7%), respectively. Our VaeTM implementation of a semantic text mining strategy shows promise in providing accurate and efficient extraction of key features from VAERS narratives.

Endeavour update: a web resource for gene prioritization in multiple species

PubMed Central

Tranchevent, Léon-Charles; Barriot, Roland; Yu, Shi; Van Vooren, Steven; Van Loo, Peter; Coessens, Bert; De Moor, Bart; Aerts, Stein; Moreau, Yves

2008-01-01

Endeavour (http://www.esat.kuleuven.be/endeavourweb; this web site is free and open to all users and there is no login requirement) is a web resource for the prioritization of candidate genes. Using a training set of genes known to be involved in a biological process of interest, our approach consists of (i) inferring several models (based on various genomic data sources), (ii) applying each model to the candidate genes to rank those candidates against the profile of the known genes and (iii) merging the several rankings into a global ranking of the candidate genes. In the present article, we describe the latest developments of Endeavour. First, we provide a web-based user interface, besides our Java client, to make Endeavour more universally accessible. Second, we support multiple species: in addition to Homo sapiens, we now provide gene prioritization for three major model organisms: Mus musculus, Rattus norvegicus and Caenorhabditis elegans. Third, Endeavour makes use of additional data sources and is now including numerous databases: ontologies and annotations, protein–protein interactions, cis-regulatory information, gene expression data sets, sequence information and text-mining data. We tested the novel version of Endeavour on 32 recent disease gene associations from the literature. Additionally, we describe a number of recent independent studies that made use of Endeavour to prioritize candidate genes for obesity and Type II diabetes, cleft lip and cleft palate, and pulmonary fibrosis. PMID:18508807

Using ant-behavior-based simulation model AntWeb to improve website organization

NASA Astrophysics Data System (ADS)

Li, Weigang; Pinheiro Dib, Marcos V.; Teles, Wesley M.; Morais de Andrade, Vlaudemir; Alves de Melo, Alba C. M.; Cariolano, Judas T.

2002-03-01

Some web usage mining algorithms showed the potential application to find the difference among the organizations expected by visitors to the website. However, there are still no efficient method and criterion for a web administrator to measure the performance of the modification. In this paper, we developed an AntWeb, a model inspired by ants' behavior to simulate the sequence of visiting the website, in order to measure the efficient of the web structure. We implemented a web usage mining algorithm using backtrack to the intranet website of the Politec Informatic Ltd., Brazil. We defined throughput (the number of visitors to reach their target pages per time unit relates to the total number of visitors) as an index to measure the website's performance. We also used the link in a web page to represent the effect of visitors' pheromone trails. For every modification in the website organization, for example, putting a link from the expected location to the target object, the simulation reported the value of throughput as a quick answer about this modification. The experiment showed the stability of our simulation model, and a positive modification to the intranet website of the Politec.

Semantic web for integrated network analysis in biomedicine.

PubMed

Chen, Huajun; Ding, Li; Wu, Zhaohui; Yu, Tong; Dhanapalan, Lavanya; Chen, Jake Y

2009-03-01

The Semantic Web technology enables integration of heterogeneous data on the World Wide Web by making the semantics of data explicit through formal ontologies. In this article, we survey the feasibility and state of the art of utilizing the Semantic Web technology to represent, integrate and analyze the knowledge in various biomedical networks. We introduce a new conceptual framework, semantic graph mining, to enable researchers to integrate graph mining with ontology reasoning in network data analysis. Through four case studies, we demonstrate how semantic graph mining can be applied to the analysis of disease-causal genes, Gene Ontology category cross-talks, drug efficacy analysis and herb-drug interactions analysis.

TOPSAN: a dynamic web database for structural genomics.

PubMed

Ellrott, Kyle; Zmasek, Christian M; Weekes, Dana; Sri Krishna, S; Bakolitsa, Constantina; Godzik, Adam; Wooley, John

2011-01-01

The Open Protein Structure Annotation Network (TOPSAN) is a web-based collaboration platform for exploring and annotating structures determined by structural genomics efforts. Characterization of those structures presents a challenge since the majority of the proteins themselves have not yet been characterized. Responding to this challenge, the TOPSAN platform facilitates collaborative annotation and investigation via a user-friendly web-based interface pre-populated with automatically generated information. Semantic web technologies expand and enrich TOPSAN's content through links to larger sets of related databases, and thus, enable data integration from disparate sources and data mining via conventional query languages. TOPSAN can be found at http://www.topsan.org.

Using the Textpresso Site-Specific Recombinases Web server to identify Cre expressing mouse strains and floxed alleles.

PubMed

Condie, Brian G; Urbanski, William M

2014-01-01

Effective tools for searching the biomedical literature are essential for identifying reagents or mouse strains as well as for effective experimental design and informed interpretation of experimental results. We have built the Textpresso Site Specific Recombinases (Textpresso SSR) Web server to enable researchers who use mice to perform in-depth searches of a rapidly growing and complex part of the mouse literature. Our Textpresso Web server provides an interface for searching the full text of most of the peer-reviewed publications that report the characterization or use of mouse strains that express Cre or Flp recombinase. The database also contains most of the publications that describe the characterization or analysis of strains carrying conditional alleles or transgenes that can be inactivated or activated by site-specific recombinases such as Cre or Flp. Textpresso SSR complements the existing online databases that catalog Cre and Flp expression patterns by providing a unique online interface for the in-depth text mining of the site specific recombinase literature.

Structuring and extracting knowledge for the support of hypothesis generation in molecular biology

PubMed Central

Roos, Marco; Marshall, M Scott; Gibson, Andrew P; Schuemie, Martijn; Meij, Edgar; Katrenko, Sophia; van Hage, Willem Robert; Krommydas, Konstantinos; Adriaans, Pieter W

2009-01-01

Background Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes. Results We describe progress towards automated support for the generation of biomolecular hypotheses. Semantic Web technologies are used to structure and store knowledge, while a workflow extracts knowledge from text. We designed minimal proto-ontologies in OWL for capturing different aspects of a text mining experiment: the biological hypothesis, text and documents, text mining, and workflow provenance. The models fit a methodology that allows focus on the requirements of a single experiment while supporting reuse and posterior analysis of extracted knowledge from multiple experiments. Our workflow is composed of services from the 'Adaptive Information Disclosure Application' (AIDA) toolkit as well as a few others. The output is a semantic model with putative biological relations, with each relation linked to the corresponding evidence. Conclusion We demonstrated a 'do-it-yourself' approach for structuring and extracting knowledge in the context of experimental research on biomolecular mechanisms. The methodology can be used to bootstrap the construction of semantically rich biological models using the results of knowledge extraction processes. Models specific to particular experiments can be constructed that, in turn, link with other semantic models, creating a web of knowledge that spans experiments. Mapping mechanisms can link to other knowledge resources such as OBO ontologies or SKOS vocabularies. AIDA Web Services can be used to design personalized knowledge extraction procedures. In our example experiment, we found three proteins (NF-Kappa B, p21, and Bax) potentially playing a role in the interplay between nutrients and epigenetic gene regulation. PMID:19796406

Geovisualization of Local and Regional Migration Using Web-mined Demographics

NASA Astrophysics Data System (ADS)

Schuermann, R. T.; Chow, T. E.

2014-11-01

The intent of this research was to augment and facilitate analyses, which gauges the feasibility of web-mined demographics to study spatio-temporal dynamics of migration. As a case study, we explored the spatio-temporal dynamics of Vietnamese Americans (VA) in Texas through geovisualization of mined demographic microdata from the World Wide Web. Based on string matching across all demographic attributes, including full name, address, date of birth, age and phone number, multiple records of the same entity (i.e. person) over time were resolved and reconciled into a database. Migration trajectories were geovisualized through animated sprites by connecting the different addresses associated with the same person and segmenting the trajectory into small fragments. Intra-metropolitan migration patterns appeared at the local scale within many metropolitan areas. At the scale of metropolitan area, varying degrees of immigration and emigration manifest different types of migration clusters. This paper presents a methodology incorporating GIS methods and cartographic design to produce geovisualization animation, enabling the cognitive identification of migration patterns at multiple scales. Identification of spatio-temporal patterns often stimulates further research to better understand the phenomenon and enhance subsequent modeling.

Proceedings: Fourth Workshop on Mining Scientific Datasets

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kamath, C

Commercial applications of data mining in areas such as e-commerce, market-basket analysis, text-mining, and web-mining have taken on a central focus in the JCDD community. However, there is a significant amount of innovative data mining work taking place in the context of scientific and engineering applications that is not well represented in the mainstream KDD conferences. For example, scientific data mining techniques are being developed and applied to diverse fields such as remote sensing, physics, chemistry, biology, astronomy, structural mechanics, computational fluid dynamics etc. In these areas, data mining frequently complements and enhances existing analysis methods based on statistics, exploratorymore » data analysis, and domain-specific approaches. On the surface, it may appear that data from one scientific field, say genomics, is very different from another field, such as physics. However, despite their diversity, there is much that is common across the mining of scientific and engineering data. For example, techniques used to identify objects in images are very similar, regardless of whether the images came from a remote sensing application, a physics experiment, an astronomy observation, or a medical study. Further, with data mining being applied to new types of data, such as mesh data from scientific simulations, there is the opportunity to apply and extend data mining to new scientific domains. This one-day workshop brings together data miners analyzing science data and scientists from diverse fields to share their experiences, learn how techniques developed in one field can be applied in another, and better understand some of the newer techniques being developed in the KDD community. This is the fourth workshop on the topic of Mining Scientific Data sets; for information on earlier workshops, see http://www.ahpcrc.org/conferences/. This workshop continues the tradition of addressing challenging problems in a field where the diversity of applications is matched only by the opportunities that await a practitioner.« less

SA-Search: a web tool for protein structure mining based on a Structural Alphabet

PubMed Central

Guyon, Frédéric; Camproux, Anne-Claude; Hochez, Joëlle; Tufféry, Pierre

2004-01-01

SA-Search is a web tool that can be used to mine for protein structures and extract structural similarities. It is based on a hidden Markov model derived Structural Alphabet (SA) that allows the compression of three-dimensional (3D) protein conformations into a one-dimensional (1D) representation using a limited number of prototype conformations. Using such a representation, classical methods developed for amino acid sequences can be employed. Currently, SA-Search permits the performance of fast 3D similarity searches such as the extraction of exact words using a suffix tree approach, and the search for fuzzy words viewed as a simple 1D sequence alignment problem. SA-Search is available at http://bioserv.rpbs.jussieu.fr/cgi-bin/SA-Search. PMID:15215446

SA-Search: a web tool for protein structure mining based on a Structural Alphabet.

PubMed

Guyon, Frédéric; Camproux, Anne-Claude; Hochez, Joëlle; Tufféry, Pierre

2004-07-01

SA-Search is a web tool that can be used to mine for protein structures and extract structural similarities. It is based on a hidden Markov model derived Structural Alphabet (SA) that allows the compression of three-dimensional (3D) protein conformations into a one-dimensional (1D) representation using a limited number of prototype conformations. Using such a representation, classical methods developed for amino acid sequences can be employed. Currently, SA-Search permits the performance of fast 3D similarity searches such as the extraction of exact words using a suffix tree approach, and the search for fuzzy words viewed as a simple 1D sequence alignment problem. SA-Search is available at http://bioserv.rpbs.jussieu.fr/cgi-bin/SA-Search.

Who wrote the "Letter to the Hebrews"?: data mining for detection of text authorship

NASA Astrophysics Data System (ADS)

Sabordo, Madeleine; Chai, Shong Y.; Berryman, Matthew J.; Abbott, Derek

2005-02-01

This paper explores the authorship of the Letter to the Hebrews using a number of different measures of relationship between different texts of the New Testament. The methods used in the study include file zipping and compression techniques, prediction by the partial matching technique and the word recurrence interval technique. The long term motivation is that the techniques employed in this study may find applicability in future generation web search engines, email authorship identification, detection of plagiarism and terrorist email traffic filtration.

The Distribution of the Informative Intensity of the Text in Terms of its Structure (On Materials of the English Texts in the Mining Sphere)

NASA Astrophysics Data System (ADS)

Znikina, Ludmila; Rozhneva, Elena

2017-11-01

The article deals with the distribution of informative intensity of the English-language scientific text based on its structural features contributing to the process of formalization of the scientific text and the preservation of the adequacy of the text with derived semantic information in relation to the primary. Discourse analysis is built on specific compositional and meaningful examples of scientific texts taken from the mining field. It also analyzes the adequacy of the translation of foreign texts into another language, the relationships between elements of linguistic systems, the degree of a formal conformance, translation with the specific objectives and information needs of the recipient. Some key words and ideas are emphasized in the paragraphs of the English-language mining scientific texts. The article gives the characteristic features of the structure of paragraphs of technical text and examples of constructions in English scientific texts based on a mining theme with the aim to explain the possible ways of their adequate translation.

minepath.org: a free interactive pathway analysis web server.

PubMed

Koumakis, Lefteris; Roussos, Panos; Potamias, George

2017-07-03

( www.minepath.org ) is a web-based platform that elaborates on, and radically extends the identification of differentially expressed sub-paths in molecular pathways. Besides the network topology, the underlying MinePath algorithmic processes exploit exact gene-gene molecular relationships (e.g. activation, inhibition) and are able to identify differentially expressed pathway parts. Each pathway is decomposed into all its constituent sub-paths, which in turn are matched with corresponding gene expression profiles. The highly ranked, and phenotype inclined sub-paths are kept. Apart from the pathway analysis algorithm, the fundamental innovation of the MinePath web-server concerns its advanced visualization and interactive capabilities. To our knowledge, this is the first pathway analysis server that introduces and offers visualization of the underlying and active pathway regulatory mechanisms instead of genes. Other features include live interaction, immediate visualization of functional sub-paths per phenotype and dynamic linked annotations for the engaged genes and molecular relations. The user can download not only the results but also the corresponding web viewer framework of the performed analysis. This feature provides the flexibility to immediately publish results without publishing source/expression data, and get all the functionality of a web based pathway analysis viewer. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Gene prioritization and clustering by multi-view text mining

PubMed Central

2010-01-01

Background Text mining has become a useful tool for biologists trying to understand the genetics of diseases. In particular, it can help identify the most interesting candidate genes for a disease for further experimental analysis. Many text mining approaches have been introduced, but the effect of disease-gene identification varies in different text mining models. Thus, the idea of incorporating more text mining models may be beneficial to obtain more refined and accurate knowledge. However, how to effectively combine these models still remains a challenging question in machine learning. In particular, it is a non-trivial issue to guarantee that the integrated model performs better than the best individual model. Results We present a multi-view approach to retrieve biomedical knowledge using different controlled vocabularies. These controlled vocabularies are selected on the basis of nine well-known bio-ontologies and are applied to index the vast amounts of gene-based free-text information available in the MEDLINE repository. The text mining result specified by a vocabulary is considered as a view and the obtained multiple views are integrated by multi-source learning algorithms. We investigate the effect of integration in two fundamental computational disease gene identification tasks: gene prioritization and gene clustering. The performance of the proposed approach is systematically evaluated and compared on real benchmark data sets. In both tasks, the multi-view approach demonstrates significantly better performance than other comparing methods. Conclusions In practical research, the relevance of specific vocabulary pertaining to the task is usually unknown. In such case, multi-view text mining is a superior and promising strategy for text-based disease gene identification. PMID:20074336

Biblio-MetReS for user-friendly mining of genes and biological processes in scientific documents.

PubMed

Usie, Anabel; Karathia, Hiren; Teixidó, Ivan; Alves, Rui; Solsona, Francesc

2014-01-01

One way to initiate the reconstruction of molecular circuits is by using automated text-mining techniques. Developing more efficient methods for such reconstruction is a topic of active research, and those methods are typically included by bioinformaticians in pipelines used to mine and curate large literature datasets. Nevertheless, experimental biologists have a limited number of available user-friendly tools that use text-mining for network reconstruction and require no programming skills to use. One of these tools is Biblio-MetReS. Originally, this tool permitted an on-the-fly analysis of documents contained in a number of web-based literature databases to identify co-occurrence of proteins/genes. This approach ensured results that were always up-to-date with the latest live version of the databases. However, this 'up-to-dateness' came at the cost of large execution times. Here we report an evolution of the application Biblio-MetReS that permits constructing co-occurrence networks for genes, GO processes, Pathways, or any combination of the three types of entities and graphically represent those entities. We show that the performance of Biblio-MetReS in identifying gene co-occurrence is as least as good as that of other comparable applications (STRING and iHOP). In addition, we also show that the identification of GO processes is on par to that reported in the latest BioCreAtIvE challenge. Finally, we also report the implementation of a new strategy that combines on-the-fly analysis of new documents with preprocessed information from documents that were encountered in previous analyses. This combination simultaneously decreases program run time and maintains 'up-to-dateness' of the results. http://metres.udl.cat/index.php/downloads, metres.cmb@gmail.com.

The utility of web mining for epidemiological research: studying the association between parity and cancer risk [Web Mining for Epidemiological Research. Assessing its Utility in Exploring the Association Between Parity and Cancer Risk

DOE PAGES

Tourassi, Georgia; Yoon, Hong-Jun; Xu, Songhua; ...

2015-11-27

Background: The World Wide Web has emerged as a powerful data source for epidemiological studies related to infectious disease surveillance. However, its potential for cancer-related epidemiological discoveries is largely unexplored. Methods: Using advanced web crawling and tailored information extraction procedures we automatically collected and analyzed the text content of 79,394 online obituary articles published between 1998 and 2014. The collected data included 51,911 cancer (27,330 breast; 9,470 lung; 6,496 pancreatic; 6,342 ovarian; 2,273 colon) and 27,483 non-cancer cases. With the derived information, we replicated a case-control study design to investigate the association between parity and cancer risk. Age-adjusted odds ratiosmore » (ORs) with 95% confidence intervals (CIs) were calculated for each cancer type and compared to those reported in large-scale epidemiological studies. Results: Parity was found to be associated with a significantly reduced risk of breast cancer (OR=0.78, 95% CI = 0.75 to 0.82), pancreatic cancer (OR=0.78, 95% CI = 0.72 to 0.83), colon cancer (OR=0.67, 95% CI = 0.60 to 0.74), and ovarian cancer (OR=0.58, 95% CI = 0.54 to 0.62). Marginal association was found for lung cancer prevalence (OR=0.87, 95% CI = 0.81 to 0.92). The linear trend between multi-parity and reduced cancer risk was dramatically more pronounced for breast and ovarian cancer than the other cancers included in the analysis. Conclusion: This large web-mining study on parity and cancer risk produced findings very similar to those reported with traditional observational studies. It may be used as a promising strategy to generate study hypotheses for guiding and prioritizing future epidemiological studies.« less

The utility of web mining for epidemiological research: studying the association between parity and cancer risk [Web Mining for Epidemiological Research. Assessing its Utility in Exploring the Association Between Parity and Cancer Risk

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tourassi, Georgia; Yoon, Hong-Jun; Xu, Songhua

Background: The World Wide Web has emerged as a powerful data source for epidemiological studies related to infectious disease surveillance. However, its potential for cancer-related epidemiological discoveries is largely unexplored. Methods: Using advanced web crawling and tailored information extraction procedures we automatically collected and analyzed the text content of 79,394 online obituary articles published between 1998 and 2014. The collected data included 51,911 cancer (27,330 breast; 9,470 lung; 6,496 pancreatic; 6,342 ovarian; 2,273 colon) and 27,483 non-cancer cases. With the derived information, we replicated a case-control study design to investigate the association between parity and cancer risk. Age-adjusted odds ratiosmore » (ORs) with 95% confidence intervals (CIs) were calculated for each cancer type and compared to those reported in large-scale epidemiological studies. Results: Parity was found to be associated with a significantly reduced risk of breast cancer (OR=0.78, 95% CI = 0.75 to 0.82), pancreatic cancer (OR=0.78, 95% CI = 0.72 to 0.83), colon cancer (OR=0.67, 95% CI = 0.60 to 0.74), and ovarian cancer (OR=0.58, 95% CI = 0.54 to 0.62). Marginal association was found for lung cancer prevalence (OR=0.87, 95% CI = 0.81 to 0.92). The linear trend between multi-parity and reduced cancer risk was dramatically more pronounced for breast and ovarian cancer than the other cancers included in the analysis. Conclusion: This large web-mining study on parity and cancer risk produced findings very similar to those reported with traditional observational studies. It may be used as a promising strategy to generate study hypotheses for guiding and prioritizing future epidemiological studies.« less

Argo: an integrative, interactive, text mining-based workbench supporting curation

PubMed Central

Rak, Rafal; Rowley, Andrew; Black, William; Ananiadou, Sophia

2012-01-01

Curation of biomedical literature is often supported by the automatic analysis of textual content that generally involves a sequence of individual processing components. Text mining (TM) has been used to enhance the process of manual biocuration, but has been focused on specific databases and tasks rather than an environment integrating TM tools into the curation pipeline, catering for a variety of tasks, types of information and applications. Processing components usually come from different sources and often lack interoperability. The well established Unstructured Information Management Architecture is a framework that addresses interoperability by defining common data structures and interfaces. However, most of the efforts are targeted towards software developers and are not suitable for curators, or are otherwise inconvenient to use on a higher level of abstraction. To overcome these issues we introduce Argo, an interoperable, integrative, interactive and collaborative system for text analysis with a convenient graphic user interface to ease the development of processing workflows and boost productivity in labour-intensive manual curation. Robust, scalable text analytics follow a modular approach, adopting component modules for distinct levels of text analysis. The user interface is available entirely through a web browser that saves the user from going through often complicated and platform-dependent installation procedures. Argo comes with a predefined set of processing components commonly used in text analysis, while giving the users the ability to deposit their own components. The system accommodates various areas and levels of user expertise, from TM and computational linguistics to ontology-based curation. One of the key functionalities of Argo is its ability to seamlessly incorporate user-interactive components, such as manual annotation editors, into otherwise completely automatic pipelines. As a use case, we demonstrate the functionality of an in-built manual annotation editor that is well suited for in-text corpus annotation tasks. Database URL: http://www.nactem.ac.uk/Argo PMID:22434844

Knowledge based word-concept model estimation and refinement for biomedical text mining.

PubMed

Jimeno Yepes, Antonio; Berlanga, Rafael

2015-02-01

Text mining of scientific literature has been essential for setting up large public biomedical databases, which are being widely used by the research community. In the biomedical domain, the existence of a large number of terminological resources and knowledge bases (KB) has enabled a myriad of machine learning methods for different text mining related tasks. Unfortunately, KBs have not been devised for text mining tasks but for human interpretation, thus performance of KB-based methods is usually lower when compared to supervised machine learning methods. The disadvantage of supervised methods though is they require labeled training data and therefore not useful for large scale biomedical text mining systems. KB-based methods do not have this limitation. In this paper, we describe a novel method to generate word-concept probabilities from a KB, which can serve as a basis for several text mining tasks. This method not only takes into account the underlying patterns within the descriptions contained in the KB but also those in texts available from large unlabeled corpora such as MEDLINE. The parameters of the model have been estimated without training data. Patterns from MEDLINE have been built using MetaMap for entity recognition and related using co-occurrences. The word-concept probabilities were evaluated on the task of word sense disambiguation (WSD). The results showed that our method obtained a higher degree of accuracy than other state-of-the-art approaches when evaluated on the MSH WSD data set. We also evaluated our method on the task of document ranking using MEDLINE citations. These results also showed an increase in performance over existing baseline retrieval approaches. Copyright © 2014 Elsevier Inc. All rights reserved.

Fuzzy and rough formal concept analysis: a survey

NASA Astrophysics Data System (ADS)

Poelmans, Jonas; Ignatov, Dmitry I.; Kuznetsov, Sergei O.; Dedene, Guido

2014-02-01

Formal Concept Analysis (FCA) is a mathematical technique that has been extensively applied to Boolean data in knowledge discovery, information retrieval, web mining, etc. applications. During the past years, the research on extending FCA theory to cope with imprecise and incomplete information made significant progress. In this paper, we give a systematic overview of the more than 120 papers published between 2003 and 2011 on FCA with fuzzy attributes and rough FCA. We applied traditional FCA as a text-mining instrument to 1072 papers mentioning FCA in the abstract. These papers were formatted in pdf files and using a thesaurus with terms referring to research topics, we transformed them into concept lattices. These lattices were used to analyze and explore the most prominent research topics within the FCA with fuzzy attributes and rough FCA research communities. FCA turned out to be an ideal metatechnique for representing large volumes of unstructured texts.

A text-based data mining and toxicity prediction modeling system for a clinical decision support in radiation oncology: A preliminary study

NASA Astrophysics Data System (ADS)

Kim, Kwang Hyeon; Lee, Suk; Shim, Jang Bo; Chang, Kyung Hwan; Yang, Dae Sik; Yoon, Won Sup; Park, Young Je; Kim, Chul Yong; Cao, Yuan Jie

2017-08-01

The aim of this study is an integrated research for text-based data mining and toxicity prediction modeling system for clinical decision support system based on big data in radiation oncology as a preliminary research. The structured and unstructured data were prepared by treatment plans and the unstructured data were extracted by dose-volume data image pattern recognition of prostate cancer for research articles crawling through the internet. We modeled an artificial neural network to build a predictor model system for toxicity prediction of organs at risk. We used a text-based data mining approach to build the artificial neural network model for bladder and rectum complication predictions. The pattern recognition method was used to mine the unstructured toxicity data for dose-volume at the detection accuracy of 97.9%. The confusion matrix and training model of the neural network were achieved with 50 modeled plans (n = 50) for validation. The toxicity level was analyzed and the risk factors for 25% bladder, 50% bladder, 20% rectum, and 50% rectum were calculated by the artificial neural network algorithm. As a result, 32 plans could cause complication but 18 plans were designed as non-complication among 50 modeled plans. We integrated data mining and a toxicity modeling method for toxicity prediction using prostate cancer cases. It is shown that a preprocessing analysis using text-based data mining and prediction modeling can be expanded to personalized patient treatment decision support based on big data.

Exploiting Recurring Structure in a Semantic Network

NASA Technical Reports Server (NTRS)

Wolfe, Shawn R.; Keller, Richard M.

2004-01-01

With the growing popularity of the Semantic Web, an increasing amount of information is becoming available in machine interpretable, semantically structured networks. Within these semantic networks are recurring structures that could be mined by existing or novel knowledge discovery methods. The mining of these semantic structures represents an interesting area that focuses on mining both for and from the Semantic Web, with surprising applicability to problems confronting the developers of Semantic Web applications. In this paper, we present representative examples of recurring structures and show how these structures could be used to increase the utility of a semantic repository deployed at NASA.

Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER)system.

PubMed

Pandey, Abhishek; Kreimeyer, Kory; Foster, Matthew; Botsis, Taxiarchis; Dang, Oanh; Ly, Thomas; Wang, Wei; Forshee, Richard

2018-01-01

Structured Product Labels follow an XML-based document markup standard approved by the Health Level Seven organization and adopted by the US Food and Drug Administration as a mechanism for exchanging medical products information. Their current organization makes their secondary use rather challenging. We used the Side Effect Resource database and DailyMed to generate a comparison dataset of 1159 Structured Product Labels. We processed the Adverse Reaction section of these Structured Product Labels with the Event-based Text-mining of Health Electronic Records system and evaluated its ability to extract and encode Adverse Event terms to Medical Dictionary for Regulatory Activities Preferred Terms. A small sample of 100 labels was then selected for further analysis. Of the 100 labels, Event-based Text-mining of Health Electronic Records achieved a precision and recall of 81 percent and 92 percent, respectively. This study demonstrated Event-based Text-mining of Health Electronic Record's ability to extract and encode Adverse Event terms from Structured Product Labels which may potentially support multiple pharmacoepidemiological tasks.

String Mining in Bioinformatics

NASA Astrophysics Data System (ADS)

Abouelhoda, Mohamed; Ghanem, Moustafa

Sequence analysis is a major area in bioinformatics encompassing the methods and techniques for studying the biological sequences, DNA, RNA, and proteins, on the linear structure level. The focus of this area is generally on the identification of intra- and inter-molecular similarities. Identifying intra-molecular similarities boils down to detecting repeated segments within a given sequence, while identifying inter-molecular similarities amounts to spotting common segments among two or multiple sequences. From a data mining point of view, sequence analysis is nothing but string- or pattern mining specific to biological strings. For a long time, this point of view, however, has not been explicitly embraced neither in the data mining nor in the sequence analysis text books, which may be attributed to the co-evolution of the two apparently independent fields. In other words, although the word "data-mining" is almost missing in the sequence analysis literature, its basic concepts have been implicitly applied. Interestingly, recent research in biological sequence analysis introduced efficient solutions to many problems in data mining, such as querying and analyzing time series [49,53], extracting information from web pages [20], fighting spam mails [50], detecting plagiarism [22], and spotting duplications in software systems [14].

Kernel Methods for Mining Instance Data in Ontologies

NASA Astrophysics Data System (ADS)

Bloehdorn, Stephan; Sure, York

The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.

Text processing through Web services: calling Whatizit.

PubMed

Rebholz-Schuhmann, Dietrich; Arregui, Miguel; Gaudan, Sylvain; Kirsch, Harald; Jimeno, Antonio

2008-01-15

Text-mining (TM) solutions are developing into efficient services to researchers in the biomedical research community. Such solutions have to scale with the growing number and size of resources (e.g. available controlled vocabularies), with the amount of literature to be processed (e.g. about 17 million documents in PubMed) and with the demands of the user community (e.g. different methods for fact extraction). These demands motivated the development of a server-based solution for literature analysis. Whatizit is a suite of modules that analyse text for contained information, e.g. any scientific publication or Medline abstracts. Special modules identify terms and then link them to the corresponding entries in bioinformatics databases such as UniProtKb/Swiss-Prot data entries and gene ontology concepts. Other modules identify a set of selected annotation types like the set produced by the EBIMed analysis pipeline for proteins. In the case of Medline abstracts, Whatizit offers access to EBI's in-house installation via PMID or term query. For large quantities of the user's own text, the server can be operated in a streaming mode (http://www.ebi.ac.uk/webservices/whatizit).

Abandoned Uranium Mines (AUM) Site Screening Map Service, 2016, US EPA Region 9

EPA Pesticide Factsheets

As described in detail in the Five-Year Report, US EPA completed on-the-ground screening of 521 abandoned uranium mine areas. US EPA and the Navajo EPA are using the Comprehensive Database and Atlas to determine which mines should be cleaned up first. US EPA continues to research and identify Potentially Responsible Parties (PRPs) under Superfund to contribute to the costs of cleanup efforts.This US EPA Region 9 web service contains the following map layers:Abandoned Uranium Mines, Priority Mines, Tronox Mines, Navajo Environmental Response Trust Mines, Mines with Enforcement Actions, Superfund AUM Regions, Navajo Nation Administrative Boundaries and Chapter Houses.Mine points have a maximum scale of 1:220,000, while Mine polygons have a minimum scale of 1:220,000. Chapter houses have a minimum scale of 1:200,000. BLM Land Status has a minimum scale of 1:150,000.Full FGDC metadata records for each layer can be found by clicking the layer name at the web service endpoint and viewing the layer description. Data used to create this web service are available for download at https://edg.epa.gov/metadata/catalog/data/data.page.Security Classification: Public. Access Constraints: None. Use Constraints: None. Please check sources, scale, accuracy, currentness and other available information. Please confirm that you are using the most recent copy of both data and metadata. Acknowledgement of the EPA would be appreciated.

A Framework for Text Mining in Scientometric Study: A Case Study in Biomedicine Publications

NASA Astrophysics Data System (ADS)

Silalahi, V. M. M.; Hardiyati, R.; Nadhiroh, I. M.; Handayani, T.; Rahmaida, R.; Amelia, M.

2018-04-01

The data of Indonesians research publications in the domain of biomedicine has been collected to be text mined for the purpose of a scientometric study. The goal is to build a predictive model that provides a classification of research publications on the potency for downstreaming. The model is based on the drug development processes adapted from the literatures. An effort is described to build the conceptual model and the development of a corpus on the research publications in the domain of Indonesian biomedicine. Then an investigation is conducted relating to the problems associated with building a corpus and validating the model. Based on our experience, a framework is proposed to manage the scientometric study based on text mining. Our method shows the effectiveness of conducting a scientometric study based on text mining in order to get a valid classification model. This valid model is mainly supported by the iterative and close interactions with the domain experts starting from identifying the issues, building a conceptual model, to the labelling, validation and results interpretation.

Hymenoptera Genome Database: integrating genome annotations in HymenopteraMine

PubMed Central

Elsik, Christine G.; Tayal, Aditi; Diesh, Colin M.; Unni, Deepak R.; Emery, Marianne L.; Nguyen, Hung N.; Hagen, Darren E.

2016-01-01

We report an update of the Hymenoptera Genome Database (HGD) (http://HymenopteraGenome.org), a model organism database for insect species of the order Hymenoptera (ants, bees and wasps). HGD maintains genomic data for 9 bee species, 10 ant species and 1 wasp, including the versions of genome and annotation data sets published by the genome sequencing consortiums and those provided by NCBI. A new data-mining warehouse, HymenopteraMine, based on the InterMine data warehousing system, integrates the genome data with data from external sources and facilitates cross-species analyses based on orthology. New genome browsers and annotation tools based on JBrowse/WebApollo provide easy genome navigation, and viewing of high throughput sequence data sets and can be used for collaborative genome annotation. All of the genomes and annotation data sets are combined into a single BLAST server that allows users to select and combine sequence data sets to search. PMID:26578564

SeMPI: a genome-based secondary metabolite prediction and identification web server.

PubMed

Zierep, Paul F; Padilla, Natàlia; Yonchev, Dimitar G; Telukunta, Kiran K; Klementz, Dennis; Günther, Stefan

2017-07-03

The secondary metabolism of bacteria, fungi and plants yields a vast number of bioactive substances. The constantly increasing amount of published genomic data provides the opportunity for an efficient identification of gene clusters by genome mining. Conversely, for many natural products with resolved structures, the encoding gene clusters have not been identified yet. Even though genome mining tools have become significantly more efficient in the identification of biosynthetic gene clusters, structural elucidation of the actual secondary metabolite is still challenging, especially due to as yet unpredictable post-modifications. Here, we introduce SeMPI, a web server providing a prediction and identification pipeline for natural products synthesized by polyketide synthases of type I modular. In order to limit the possible structures of PKS products and to include putative tailoring reactions, a structural comparison with annotated natural products was introduced. Furthermore, a benchmark was designed based on 40 gene clusters with annotated PKS products. The web server of the pipeline (SeMPI) is freely available at: http://www.pharmaceutical-bioinformatics.de/sempi. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Text Mining and Natural Language Processing Approaches for Automatic Categorization of Lay Requests to Web-Based Expert Forums

PubMed Central

Reincke, Ulrich; Michelmann, Hans Wilhelm

2009-01-01

Background Both healthy and sick people increasingly use electronic media to obtain medical information and advice. For example, Internet users may send requests to Web-based expert forums, or so-called “ask the doctor” services. Objective To automatically classify lay requests to an Internet medical expert forum using a combination of different text-mining strategies. Methods We first manually classified a sample of 988 requests directed to a involuntary childlessness forum on the German website “Rund ums Baby” (“Everything about Babies”) into one or more of 38 categories belonging to two dimensions (“subject matter” and “expectations”). After creating start and synonym lists, we calculated the average Cramer’s V statistic for the association of each word with each category. We also used principle component analysis and singular value decomposition as further text-mining strategies. With these measures we trained regression models and determined, on the basis of best regression models, for any request the probability of belonging to each of the 38 different categories, with a cutoff of 50%. Recall and precision of a test sample were calculated as a measure of quality for the automatic classification. Results According to the manual classification of 988 documents, 102 (10%) documents fell into the category “in vitro fertilization (IVF),” 81 (8%) into the category “ovulation,” 79 (8%) into “cycle,” and 57 (6%) into “semen analysis.” These were the four most frequent categories in the subject matter dimension (consisting of 32 categories). The expectation dimension comprised six categories; we classified 533 documents (54%) as “general information” and 351 (36%) as a wish for “treatment recommendations.” The generation of indicator variables based on the chi-square analysis and Cramer’s V proved to be the best approach for automatic classification in about half of the categories. In combination with the two other approaches, 100% precision and 100% recall were realized in 18 (47%) out of the 38 categories in the test sample. For 35 (92%) categories, precision and recall were better than 80%. For some categories, the input variables (ie, “words”) also included variables from other categories, most often with a negative sign. For example, absence of words predictive for “menstruation” was a strong indicator for the category “pregnancy test.” Conclusions Our approach suggests a way of automatically classifying and analyzing unstructured information in Internet expert forums. The technique can perform a preliminary categorization of new requests and help Internet medical experts to better handle the mass of information and to give professional feedback. PMID:19632978

The Comprehensive Microbial Resource.

PubMed

Peterson, J D; Umayam, L A; Dickinson, T; Hickey, E K; White, O

2001-01-01

One challenge presented by large-scale genome sequencing efforts is effective display of uniform information to the scientific community. The Comprehensive Microbial Resource (CMR) contains robust annotation of all complete microbial genomes and allows for a wide variety of data retrievals. The bacterial information has been placed on the Web at http://www.tigr.org/CMR for retrieval using standard web browsing technology. Retrievals can be based on protein properties such as molecular weight or hydrophobicity, GC-content, functional role assignments and taxonomy. The CMR also has special web-based tools to allow data mining using pre-run homology searches, whole genome dot-plots, batch downloading and traversal across genomes using a variety of datatypes.

Text-mining-assisted biocuration workflows in Argo

PubMed Central

Rak, Rafal; Batista-Navarro, Riza Theresa; Rowley, Andrew; Carter, Jacob; Ananiadou, Sophia

2014-01-01

Biocuration activities have been broadly categorized into the selection of relevant documents, the annotation of biological concepts of interest and identification of interactions between the concepts. Text mining has been shown to have a potential to significantly reduce the effort of biocurators in all the three activities, and various semi-automatic methodologies have been integrated into curation pipelines to support them. We investigate the suitability of Argo, a workbench for building text-mining solutions with the use of a rich graphical user interface, for the process of biocuration. Central to Argo are customizable workflows that users compose by arranging available elementary analytics to form task-specific processing units. A built-in manual annotation editor is the single most used biocuration tool of the workbench, as it allows users to create annotations directly in text, as well as modify or delete annotations created by automatic processing components. Apart from syntactic and semantic analytics, the ever-growing library of components includes several data readers and consumers that support well-established as well as emerging data interchange formats such as XMI, RDF and BioC, which facilitate the interoperability of Argo with other platforms or resources. To validate the suitability of Argo for curation activities, we participated in the BioCreative IV challenge whose purpose was to evaluate Web-based systems addressing user-defined biocuration tasks. Argo proved to have the edge over other systems in terms of flexibility of defining biocuration tasks. As expected, the versatility of the workbench inevitably lengthened the time the curators spent on learning the system before taking on the task, which may have affected the usability of Argo. The participation in the challenge gave us an opportunity to gather valuable feedback and identify areas of improvement, some of which have already been introduced. Database URL: http://argo.nactem.ac.uk PMID:25037308

Resource Management Scheme Based on Ubiquitous Data Analysis

PubMed Central

Lee, Heung Ki; Jung, Jaehee

2014-01-01

Resource management of the main memory and process handler is critical to enhancing the system performance of a web server. Owing to the transaction delay time that affects incoming requests from web clients, web server systems utilize several web processes to anticipate future requests. This procedure is able to decrease the web generation time because there are enough processes to handle the incoming requests from web browsers. However, inefficient process management results in low service quality for the web server system. Proper pregenerated process mechanisms are required for dealing with the clients' requests. Unfortunately, it is difficult to predict how many requests a web server system is going to receive. If a web server system builds too many web processes, it wastes a considerable amount of memory space, and thus performance is reduced. We propose an adaptive web process manager scheme based on the analysis of web log mining. In the proposed scheme, the number of web processes is controlled through prediction of incoming requests, and accordingly, the web process management scheme consumes the least possible web transaction resources. In experiments, real web trace data were used to prove the improved performance of the proposed scheme. PMID:25197692

Extracting semantically enriched events from biomedical literature

PubMed Central

2012-01-01

Background Research into event-based text mining from the biomedical literature has been growing in popularity to facilitate the development of advanced biomedical text mining systems. Such technology permits advanced search, which goes beyond document or sentence-based retrieval. However, existing event-based systems typically ignore additional information within the textual context of events that can determine, amongst other things, whether an event represents a fact, hypothesis, experimental result or analysis of results, whether it describes new or previously reported knowledge, and whether it is speculated or negated. We refer to such contextual information as meta-knowledge. The automatic recognition of such information can permit the training of systems allowing finer-grained searching of events according to the meta-knowledge that is associated with them. Results Based on a corpus of 1,000 MEDLINE abstracts, fully manually annotated with both events and associated meta-knowledge, we have constructed a machine learning-based system that automatically assigns meta-knowledge information to events. This system has been integrated into EventMine, a state-of-the-art event extraction system, in order to create a more advanced system (EventMine-MK) that not only extracts events from text automatically, but also assigns five different types of meta-knowledge to these events. The meta-knowledge assignment module of EventMine-MK performs with macro-averaged F-scores in the range of 57-87% on the BioNLP’09 Shared Task corpus. EventMine-MK has been evaluated on the BioNLP’09 Shared Task subtask of detecting negated and speculated events. Our results show that EventMine-MK can outperform other state-of-the-art systems that participated in this task. Conclusions We have constructed the first practical system that extracts both events and associated, detailed meta-knowledge information from biomedical literature. The automatically assigned meta-knowledge information can be used to refine search systems, in order to provide an extra search layer beyond entities and assertions, dealing with phenomena such as rhetorical intent, speculations, contradictions and negations. This finer grained search functionality can assist in several important tasks, e.g., database curation (by locating new experimental knowledge) and pathway enrichment (by providing information for inference). To allow easy integration into text mining systems, EventMine-MK is provided as a UIMA component that can be used in the interoperable text mining infrastructure, U-Compare. PMID:22621266

Extracting semantically enriched events from biomedical literature.

PubMed

Miwa, Makoto; Thompson, Paul; McNaught, John; Kell, Douglas B; Ananiadou, Sophia

2012-05-23

Research into event-based text mining from the biomedical literature has been growing in popularity to facilitate the development of advanced biomedical text mining systems. Such technology permits advanced search, which goes beyond document or sentence-based retrieval. However, existing event-based systems typically ignore additional information within the textual context of events that can determine, amongst other things, whether an event represents a fact, hypothesis, experimental result or analysis of results, whether it describes new or previously reported knowledge, and whether it is speculated or negated. We refer to such contextual information as meta-knowledge. The automatic recognition of such information can permit the training of systems allowing finer-grained searching of events according to the meta-knowledge that is associated with them. Based on a corpus of 1,000 MEDLINE abstracts, fully manually annotated with both events and associated meta-knowledge, we have constructed a machine learning-based system that automatically assigns meta-knowledge information to events. This system has been integrated into EventMine, a state-of-the-art event extraction system, in order to create a more advanced system (EventMine-MK) that not only extracts events from text automatically, but also assigns five different types of meta-knowledge to these events. The meta-knowledge assignment module of EventMine-MK performs with macro-averaged F-scores in the range of 57-87% on the BioNLP'09 Shared Task corpus. EventMine-MK has been evaluated on the BioNLP'09 Shared Task subtask of detecting negated and speculated events. Our results show that EventMine-MK can outperform other state-of-the-art systems that participated in this task. We have constructed the first practical system that extracts both events and associated, detailed meta-knowledge information from biomedical literature. The automatically assigned meta-knowledge information can be used to refine search systems, in order to provide an extra search layer beyond entities and assertions, dealing with phenomena such as rhetorical intent, speculations, contradictions and negations. This finer grained search functionality can assist in several important tasks, e.g., database curation (by locating new experimental knowledge) and pathway enrichment (by providing information for inference). To allow easy integration into text mining systems, EventMine-MK is provided as a UIMA component that can be used in the interoperable text mining infrastructure, U-Compare.

Detection and Evaluation of Cheating on College Exams Using Supervised Classification

ERIC Educational Resources Information Center

Cavalcanti, Elmano Ramalho; Pires, Carlos Eduardo; Cavalcanti, Elmano Pontes; Pires, Vládia Freire

2012-01-01

Text mining has been used for various purposes, such as document classification and extraction of domain-specific information from text. In this paper we present a study in which text mining methodology and algorithms were properly employed for academic dishonesty (cheating) detection and evaluation on open-ended college exams, based on document…

Graph Mining Meets the Semantic Web

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, Sangkeun; Sukumar, Sreenivas R; Lim, Seung-Hwan

The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today, data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. We address that need through implementation of three popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, and PageRank). We implement these algorithms as SPARQL queries, wrapped within Python scripts. We evaluatemore » the performance of our implementation on 6 real world data sets and show graph mining algorithms (that have a linear-algebra formulation) can indeed be unleashed on data represented as RDF graphs using the SPARQL query interface.« less

Mining Longitudinal Web Queries: Trends and Patterns.

ERIC Educational Resources Information Center

Wang, Peiling; Berry, Michael W.; Yang, Yiheng

2003-01-01

Analyzed user queries submitted to an academic Web site during a four-year period, using a relational database, to examine users' query behavior, to identify problems they encounter, and to develop techniques for optimizing query analysis and mining. Linguistic analyses focus on query structures, lexicon, and word associations using statistical…

An Expertise Recommender using Web Mining

NASA Technical Reports Server (NTRS)

Joshi, Anupam; Chandrasekaran, Purnima; ShuYang, Michelle; Ramakrishnan, Ramya

2001-01-01

This report explored techniques to mine web pages of scientists to extract information regarding their expertise, build expertise chains and referral webs, and semi automatically combine this information with directory information services to create a recommender system that permits query by expertise. The approach included experimenting with existing techniques that have been reported in research literature in recent past , and adapted them as needed. In addition, software tools were developed to capture and use this information.

Visualization of usability and functionality of a professional website through web-mining.

PubMed

Jones, Josette F; Mahoui, Malika; Gopa, Venkata Devi Pragna

2007-10-11

Functional interface design requires understanding of the information system structure and the user. Web logs record user interactions with the interface, and thus provide some insight into user search behavior and efficiency of the search process. The present study uses a data-mining approach with techniques such as association rules, clustering and classification, to visualize the usability and functionality of a digital library through in depth analyses of web logs.

A Feature Mining Based Approach for the Classification of Text Documents into Disjoint Classes.

ERIC Educational Resources Information Center

Nieto Sanchez, Salvador; Triantaphyllou, Evangelos; Kraft, Donald

2002-01-01

Proposes a new approach for classifying text documents into two disjoint classes. Highlights include a brief overview of document clustering; a data mining approach called the One Clause at a Time (OCAT) algorithm which is based on mathematical logic; vector space model (VSM); and comparing the OCAT to the VSM. (Author/LRW)

A Semantic Web-based System for Mining Genetic Mutations in Cancer Clinical Trials.

PubMed

Priya, Sambhawa; Jiang, Guoqian; Dasari, Surendra; Zimmermann, Michael T; Wang, Chen; Heflin, Jeff; Chute, Christopher G

2015-01-01

Textual eligibility criteria in clinical trial protocols contain important information about potential clinically relevant pharmacogenomic events. Manual curation for harvesting this evidence is intractable as it is error prone and time consuming. In this paper, we develop and evaluate a Semantic Web-based system that captures and manages mutation evidences and related contextual information from cancer clinical trials. The system has 2 main components: an NLP-based annotator and a Semantic Web ontology-based annotation manager. We evaluated the performance of the annotator in terms of precision and recall. We demonstrated the usefulness of the system by conducting case studies in retrieving relevant clinical trials using a collection of mutations identified from TCGA Leukemia patients and Atlas of Genetics and Cytogenetics in Oncology and Haematology. In conclusion, our system using Semantic Web technologies provides an effective framework for extraction, annotation, standardization and management of genetic mutations in cancer clinical trials.

The DBCLS BioHackathon: standardization and interoperability for bioinformatics web services and workflows. The DBCLS BioHackathon Consortium*.

PubMed

Katayama, Toshiaki; Arakawa, Kazuharu; Nakao, Mitsuteru; Ono, Keiichiro; Aoki-Kinoshita, Kiyoko F; Yamamoto, Yasunori; Yamaguchi, Atsuko; Kawashima, Shuichi; Chun, Hong-Woo; Aerts, Jan; Aranda, Bruno; Barboza, Lord Hendrix; Bonnal, Raoul Jp; Bruskiewich, Richard; Bryne, Jan C; Fernández, José M; Funahashi, Akira; Gordon, Paul Mk; Goto, Naohisa; Groscurth, Andreas; Gutteridge, Alex; Holland, Richard; Kano, Yoshinobu; Kawas, Edward A; Kerhornou, Arnaud; Kibukawa, Eri; Kinjo, Akira R; Kuhn, Michael; Lapp, Hilmar; Lehvaslaiho, Heikki; Nakamura, Hiroyuki; Nakamura, Yasukazu; Nishizawa, Tatsuya; Nobata, Chikashi; Noguchi, Tamotsu; Oinn, Thomas M; Okamoto, Shinobu; Owen, Stuart; Pafilis, Evangelos; Pocock, Matthew; Prins, Pjotr; Ranzinger, René; Reisinger, Florian; Salwinski, Lukasz; Schreiber, Mark; Senger, Martin; Shigemoto, Yasumasa; Standley, Daron M; Sugawara, Hideaki; Tashiro, Toshiyuki; Trelles, Oswaldo; Vos, Rutger A; Wilkinson, Mark D; York, William; Zmasek, Christian M; Asai, Kiyoshi; Takagi, Toshihisa

2010-08-21

Web services have become a key technology for bioinformatics, since life science databases are globally decentralized and the exponential increase in the amount of available data demands for efficient systems without the need to transfer entire databases for every step of an analysis. However, various incompatibilities among database resources and analysis services make it difficult to connect and integrate these into interoperable workflows. To resolve this situation, we invited domain specialists from web service providers, client software developers, Open Bio* projects, the BioMoby project and researchers of emerging areas where a standard exchange data format is not well established, for an intensive collaboration entitled the BioHackathon 2008. The meeting was hosted by the Database Center for Life Science (DBCLS) and Computational Biology Research Center (CBRC) and was held in Tokyo from February 11th to 15th, 2008. In this report we highlight the work accomplished and the common issues arisen from this event, including the standardization of data exchange formats and services in the emerging fields of glycoinformatics, biological interaction networks, text mining, and phyloinformatics. In addition, common shared object development based on BioSQL, as well as technical challenges in large data management, asynchronous services, and security are discussed. Consequently, we improved interoperability of web services in several fields, however, further cooperation among major database centers and continued collaborative efforts between service providers and software developers are still necessary for an effective advance in bioinformatics web service technologies.

The DBCLS BioHackathon: standardization and interoperability for bioinformatics web services and workflows. The DBCLS BioHackathon Consortium*

PubMed Central

2010-01-01

Web services have become a key technology for bioinformatics, since life science databases are globally decentralized and the exponential increase in the amount of available data demands for efficient systems without the need to transfer entire databases for every step of an analysis. However, various incompatibilities among database resources and analysis services make it difficult to connect and integrate these into interoperable workflows. To resolve this situation, we invited domain specialists from web service providers, client software developers, Open Bio* projects, the BioMoby project and researchers of emerging areas where a standard exchange data format is not well established, for an intensive collaboration entitled the BioHackathon 2008. The meeting was hosted by the Database Center for Life Science (DBCLS) and Computational Biology Research Center (CBRC) and was held in Tokyo from February 11th to 15th, 2008. In this report we highlight the work accomplished and the common issues arisen from this event, including the standardization of data exchange formats and services in the emerging fields of glycoinformatics, biological interaction networks, text mining, and phyloinformatics. In addition, common shared object development based on BioSQL, as well as technical challenges in large data management, asynchronous services, and security are discussed. Consequently, we improved interoperability of web services in several fields, however, further cooperation among major database centers and continued collaborative efforts between service providers and software developers are still necessary for an effective advance in bioinformatics web service technologies. PMID:20727200

Automatic target validation based on neuroscientific literature mining for tractography

PubMed Central

Vasques, Xavier; Richardet, Renaud; Hill, Sean L.; Slater, David; Chappelier, Jean-Cedric; Pralong, Etienne; Bloch, Jocelyne; Draganski, Bogdan; Cif, Laura

2015-01-01

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/. PMID:26074781

GNormPlus: An Integrative Approach for Tagging Genes, Gene Families, and Protein Domains

PubMed Central

Lu, Zhiyong

2015-01-01

The automatic recognition of gene names and their associated database identifiers from biomedical text has been widely studied in recent years, as these tasks play an important role in many downstream text-mining applications. Despite significant previous research, only a small number of tools are publicly available and these tools are typically restricted to detecting only mention level gene names or only document level gene identifiers. In this work, we report GNormPlus: an end-to-end and open source system that handles both gene mention and identifier detection. We created a new corpus of 694 PubMed articles to support our development of GNormPlus, containing manual annotations for not only gene names and their identifiers, but also closely related concepts useful for gene name disambiguation, such as gene families and protein domains. GNormPlus integrates several advanced text-mining techniques, including SimConcept for resolving composite gene names. As a result, GNormPlus compares favorably to other state-of-the-art methods when evaluated on two widely used public benchmarking datasets, achieving 86.7% F1-score on the BioCreative II Gene Normalization task dataset and 50.1% F1-score on the BioCreative III Gene Normalization task dataset. The GNormPlus source code and its annotated corpus are freely available, and the results of applying GNormPlus to the entire PubMed are freely accessible through our web-based tool PubTator. PMID:26380306

Rating prediction using textual reviews

NASA Astrophysics Data System (ADS)

NithyaKalyani, A.; Ushasukhanya, S.; Nagamalleswari, TYJ; Girija, S.

2018-04-01

Information today is present in the form of opinions. Two & a half quintillion bytes are exchanged today in Internet everyday and a large amount consists of people’s speculation and reflection over an issue. It is the need of the hour to be able to mine this information that is presented to us. Sentimental analysis refers to mining of this raw information to make sense. The discipline of opinion mining has seen a lot of encouragement in the past few years augmented by involvement of social media like Instagram, Facebook, and twitter. The hidden message in this web of information is useful in several fields such as marketing, political polls, product review, forecast market movement, Identifying detractor and promoter. In this endeavor, we introduced sentiment rating system for a particular text or paragraph to determine the opinions polarity. Firstly we resolve the searching problem, tokenization, classification, and reliable content identification. Secondly we extract probability for given text or paragraph for both positive & negative sentiment value using naive bayes classifier. At last we use sentiment dictionary (SD), sentiment degree dictionary (SDD) and negation dictionary (ND) for more accuracy. Later we blend all above mentioned factor into given formula to find the rating for the review.

Text mining patents for biomedical knowledge.

PubMed

Rodriguez-Esteban, Raul; Bundschus, Markus

2016-06-01

Biomedical text mining of scientific knowledge bases, such as Medline, has received much attention in recent years. Given that text mining is able to automatically extract biomedical facts that revolve around entities such as genes, proteins, and drugs, from unstructured text sources, it is seen as a major enabler to foster biomedical research and drug discovery. In contrast to the biomedical literature, research into the mining of biomedical patents has not reached the same level of maturity. Here, we review existing work and highlight the associated technical challenges that emerge from automatically extracting facts from patents. We conclude by outlining potential future directions in this domain that could help drive biomedical research and drug discovery. Copyright © 2016 Elsevier Ltd. All rights reserved.

Product Recommendation System Based on Personal Preference Model Using CAM

NASA Astrophysics Data System (ADS)

Murakami, Tomoko; Yoshioka, Nobukazu; Orihara, Ryohei; Furukawa, Koichi

Product recommendation system is realized by applying business rules acquired by data maining techniques. Business rules such as demographical patterns of purchase, are able to cover the groups of users that have a tendency to purchase products, but it is difficult to recommend products adaptive to various personal preferences only by utilizing them. In addition to that, it is very costly to gather the large volume of high quality survey data, which is necessary for good recommendation based on personal preference model. A method collecting kansei information automatically without questionnaire survey is required. The constructing personal preference model from less favor data is also necessary, since it is costly for the user to input favor data. In this paper, we propose product recommendation system based on kansei information extracted by text mining and user's preference model constructed by Category-guided Adaptive Modeling, CAM for short. CAM is a feature construction method that can generate new features constructing the space where same labeled examples are close and different labeled examples are far away from some labeled examples. It is possible to construct personal preference model by CAM despite less information of likes and dislikes categories. In the system, retrieval agent gathers the products' specification and user agent manages preference model, user's likes and dislikes. Kansei information of the products is gained by applying text mining technique to the reputation documents about the products on the web site. We carry out some experimental studies to make sure that prefrence model obtained by our method performs effectively.

Data Access and Web Services at the EarthScope Plate Boundary Observatory

NASA Astrophysics Data System (ADS)

Matykiewicz, J.; Anderson, G.; Henderson, D.; Hodgkinson, K.; Hoyt, B.; Lee, E.; Persson, E.; Torrez, D.; Smith, J.; Wright, J.; Jackson, M.

2007-12-01

The EarthScope Plate Boundary Observatory (PBO) at UNAVCO, Inc., part of the NSF-funded EarthScope project, is designed to study the three-dimensional strain field resulting from deformation across the active boundary zone between the Pacific and North American plates in the western United States. To meet these goals, PBO will install 880 continuous GPS stations, 103 borehole strainmeter stations, and five laser strainmeters, as well as manage data for 209 previously existing continuous GPS stations and one previously existing laser strainmeter. UNAVCO provides access to data products from these stations, as well as general information about the PBO project, via the PBO web site (http://pboweb.unavco.org). GPS and strainmeter data products can be found using a variety of access methods, incuding map searches, text searches, and station specific data retrieval. In addition, the PBO construction status is available via multiple mapping interfaces, including custom web based map widgets and Google Earth. Additional construction details can be accessed from PBO operational pages and station specific home pages. The current state of health for the PBO network is available with the statistical snap-shot, full map interfaces, tabular web based reports, and automatic data mining and alerts. UNAVCO is currently working to enhance the community access to this information by developing a web service framework for the discovery of data products, interfacing with operational engineers, and exposing data services to third party participants. In addition, UNAVCO, through the PBO project, provides advanced data management and monitoring systems for use by the community in operating geodetic networks in the United States and beyond. We will demonstrate these systems during the AGU meeting, and we welcome inquiries from the community at any time.

The Plate Boundary Observatory: Community Focused Web Services

NASA Astrophysics Data System (ADS)

Matykiewicz, J.; Anderson, G.; Lee, E.; Hoyt, B.; Hodgkinson, K.; Persson, E.; Wright, J.; Torrez, D.; Jackson, M.

2006-12-01

The Plate Boundary Observatory (PBO), part of the NSF-funded EarthScope project, is designed to study the three-dimensional strain field resulting from deformation across the active boundary zone between the Pacific and North American plates in the western United States. To meet these goals, PBO will install 852 continuous GPS stations, 103 borehole strainmeter stations, 28 tiltmeters, and five laser strainmeters, as well as manage data for 209 previously existing continuous GPS stations. UNAVCO provides access to data products from these stations, as well as general information about the PBO project, via the PBO web site (http://pboweb.unavco.org). GPS and strainmeter data products can be found using a variety of channels, including map searches, text searches, and station specific data retrieval. In addition, the PBO construction status is available via multiple mapping interfaces, including custom web based map widgets and Google Earth. Additional construction details can be accessed from PBO operational pages and station specific home pages. The current state of health for the PBO network is available with the statistical snap-shot, full map interfaces, tabular web based reports, and automatic data mining and alerts. UNAVCO is currently working to enhance the community access to this information by developing a web service framework for the discovery of data products, interfacing with operational engineers, and exposing data services to third party participants. In addition, UNAVCO, through the PBO project, provides advanced data management and monitoring systems for use by the community in operating geodetic networks in the United States and beyond. We will demonstrate these systems during the AGU meeting, and we welcome inquiries from the community at any time.

Automatic Recommendations for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval

ERIC Educational Resources Information Center

Khribi, Mohamed Koutheair; Jemni, Mohamed; Nasraoui, Olfa

2009-01-01

In this paper, we describe an automatic personalization approach aiming to provide online automatic recommendations for active learners without requiring their explicit feedback. Recommended learning resources are computed based on the current learner's recent navigation history, as well as exploiting similarities and dissimilarities among…

Mercury flow through an Asian rice-based food web.

PubMed

Abeysinghe, Kasun S; Qiu, Guangle; Goodale, Eben; Anderson, Christopher W N; Bishop, Kevin; Evers, David C; Goodale, Morgan W; Hintelmann, Holger; Liu, Shengjie; Mammides, Christos; Quan, Rui-Chang; Wang, Jin; Wu, Pianpian; Xu, Xiao-Hang; Yang, Xiao-Dong; Feng, Xinbin

2017-10-01

Mercury (Hg) is a globally-distributed pollutant, toxic to humans and animals. Emissions are particularly high in Asia, and the source of exposure for humans there may also be different from other regions, including rice as well as fish consumption, particularly in contaminated areas. Yet the threats Asian wildlife face in rice-based ecosystems are as yet unclear. We sought to understand how Hg flows through rice-based food webs in historic mining and non-mining regions of Guizhou, China. We measured total Hg (THg) and methylmercury (MeHg) in soil, rice, 38 animal species (27 for MeHg) spanning multiple trophic levels, and examined the relationship between stable isotopes and Hg concentrations. Our results confirm biomagnification of THg/MeHg, with a high trophic magnification slope. Invertivorous songbirds had concentrations of THg in their feathers that were 15x and 3x the concentration reported to significantly impair reproduction, at mining and non-mining sites, respectively. High concentrations in specialist rice consumers and in granivorous birds, the later as high as in piscivorous birds, suggest rice is a primary source of exposure. Spiders had the highest THg concentrations among invertebrates and may represent a vector through which Hg is passed to vertebrates, especially songbirds. Our findings suggest there could be significant population level health effects and consequent biodiversity loss in sensitive ecosystems, like agricultural wetlands, across Asia, and invertivorous songbirds would be good subjects for further studies investigating this possibility. Copyright © 2017 Elsevier Ltd. All rights reserved.

NCBI GEO: mining millions of expression profiles--database and tools.

PubMed

Barrett, Tanya; Suzek, Tugba O; Troup, Dennis B; Wilhite, Stephen E; Ngau, Wing-Chi; Ledoux, Pierre; Rudnev, Dmitry; Lash, Alex E; Fujibuchi, Wataru; Edgar, Ron

2005-01-01

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) is the largest fully public repository for high-throughput molecular abundance data, primarily gene expression data. The database has a flexible and open design that allows the submission, storage and retrieval of many data types. These data include microarray-based experiments measuring the abundance of mRNA, genomic DNA and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. GEO currently holds over 30,000 submissions representing approximately half a billion individual molecular abundance measurements, for over 100 organisms. Here, we describe recent database developments that facilitate effective mining and visualization of these data. Features are provided to examine data from both experiment- and gene-centric perspectives using user-friendly Web-based interfaces accessible to those without computational or microarray-related analytical expertise. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

Binary Coded Web Access Pattern Tree in Education Domain

ERIC Educational Resources Information Center

Gomathi, C.; Moorthi, M.; Duraiswamy, K.

2008-01-01

Web Access Pattern (WAP), which is the sequence of accesses pursued by users frequently, is a kind of interesting and useful knowledge in practice. Sequential Pattern mining is the process of applying data mining techniques to a sequential database for the purposes of discovering the correlation relationships that exist among an ordered list of…

Mechanism-based Pharmacovigilance over the Life Sciences Linked Open Data Cloud.

PubMed

Kamdar, Maulik R; Musen, Mark A

2017-01-01

Adverse drug reactions (ADR) result in significant morbidity and mortality in patients, and a substantial proportion of these ADRs are caused by drug-drug interactions (DDIs). Pharmacovigilance methods are used to detect unanticipated DDIs and ADRs by mining Spontaneous Reporting Systems, such as the US FDA Adverse Event Reporting System (FAERS). However, these methods do not provide mechanistic explanations for the discovered drug-ADR associations in a systematic manner. In this paper, we present a systems pharmacology-based approach to perform mechanism-based pharmacovigilance. We integrate data and knowledge from four different sources using Semantic Web Technologies and Linked Data principles to generate a systems network. We present a network-based Apriori algorithm for association mining in FAERS reports. We evaluate our method against existing pharmacovigilance methods for three different validation sets. Our method has AUROC statistics of 0.7-0.8, similar to current methods, and event-specific thresholds generate AUROC statistics greater than 0.75 for certain ADRs. Finally, we discuss the benefits of using Semantic Web technologies to attain the objectives for mechanism-based pharmacovigilance.

Data-mining to build a knowledge representation store for clinical decision support. Studies on curation and validation based on machine performance in multiple choice medical licensing examinations.

PubMed

Robson, Barry; Boray, Srinidhi

2016-06-01

Extracting medical knowledge by structured data mining of many medical records and from unstructured data mining of natural language source text on the Internet will become increasingly important for clinical decision support. Output from these sources can be transformed into large numbers of elements of knowledge in a Knowledge Representation Store (KRS), here using the notation and to some extent the algebraic principles of the Q-UEL Web-based universal exchange and inference language described previously, rooted in Dirac notation from quantum mechanics and linguistic theory. In a KRS, semantic structures or statements about the world of interest to medicine are analogous to natural language sentences seen as formed from noun phrases separated by verbs, prepositions and other descriptions of relationships. A convenient method of testing and better curating these elements of knowledge is by having the computer use them to take the test of a multiple choice medical licensing examination. It is a venture which perhaps tells us almost as much about the reasoning of students and examiners as it does about the requirements for Artificial Intelligence as employed in clinical decision making. It emphasizes the role of context and of contextual probabilities as opposed to the more familiar intrinsic probabilities, and of a preliminary form of logic that we call presyllogistic reasoning. Copyright © 2016 Elsevier Ltd. All rights reserved.

String Mining in Bioinformatics

NASA Astrophysics Data System (ADS)

Abouelhoda, Mohamed; Ghanem, Moustafa

Sequence analysis is a major area in bioinformatics encompassing the methods and techniques for studying the biological sequences, DNA, RNA, and proteins, on the linear structure level. The focus of this area is generally on the identification of intra- and inter-molecular similarities. Identifying intra-molecular similarities boils down to detecting repeated segments within a given sequence, while identifying inter-molecular similarities amounts to spotting common segments among two or multiple sequences. From a data mining point of view, sequence analysis is nothing but string- or pattern mining specific to biological strings. For a long time, this point of view, however, has not been explicitly embraced neither in the data mining nor in the sequence analysis text books, which may be attributed to the co-evolution of the two apparently independent fields. In other words, although the word “data-mining” is almost missing in the sequence analysis literature, its basic concepts have been implicitly applied. Interestingly, recent research in biological sequence analysis introduced efficient solutions to many problems in data mining, such as querying and analyzing time series [49,53], extracting information from web pages [20], fighting spam mails [50], detecting plagiarism [22], and spotting duplications in software systems [14].

Proactive Supply Chain Performance Management with Predictive Analytics

PubMed Central

Stefanovic, Nenad

2014-01-01

Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment. PMID:25386605

Proactive supply chain performance management with predictive analytics.

PubMed

Stefanovic, Nenad

2014-01-01

Today's business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic business intelligence (BI) model which encapsulates data sources and business rules and includes the data warehouse model with specific supply chain dimensions, measures, and KPIs (key performance indicators). Next, the paper describes two generic approaches for designing the KPI predictive data mining models based on the BI semantic model. KPI predictive models were trained and tested with a real-world data set. Finally, a specialized analytical web portal which offers collaborative performance monitoring and decision making is presented. The results show that these models give very accurate KPI projections and provide valuable insights into newly emerging trends, opportunities, and problems. This should lead to more intelligent, predictive, and responsive supply chains capable of adapting to future business environment.

The Comprehensive Microbial Resource

PubMed Central

Peterson, Jeremy D.; Umayam, Lowell A.; Dickinson, Tanja; Hickey, Erin K.; White, Owen

2001-01-01

One challenge presented by large-scale genome sequencing efforts is effective display of uniform information to the scientific community. The Comprehensive Microbial Resource (CMR) contains robust annotation of all complete microbial genomes and allows for a wide variety of data retrievals. The bacterial information has been placed on the Web at http://www.tigr.org/CMR for retrieval using standard web browsing technology. Retrievals can be based on protein properties such as molecular weight or hydrophobicity, GC-content, functional role assignments and taxonomy. The CMR also has special web-based tools to allow data mining using pre-run homology searches, whole genome dot-plots, batch downloading and traversal across genomes using a variety of datatypes. PMID:11125067

PLAN: a web platform for automating high-throughput BLAST searches and for managing and mining results.

PubMed

He, Ji; Dai, Xinbin; Zhao, Xuechun

2007-02-09

BLAST searches are widely used for sequence alignment. The search results are commonly adopted for various functional and comparative genomics tasks such as annotating unknown sequences, investigating gene models and comparing two sequence sets. Advances in sequencing technologies pose challenges for high-throughput analysis of large-scale sequence data. A number of programs and hardware solutions exist for efficient BLAST searching, but there is a lack of generic software solutions for mining and personalized management of the results. Systematically reviewing the results and identifying information of interest remains tedious and time-consuming. Personal BLAST Navigator (PLAN) is a versatile web platform that helps users to carry out various personalized pre- and post-BLAST tasks, including: (1) query and target sequence database management, (2) automated high-throughput BLAST searching, (3) indexing and searching of results, (4) filtering results online, (5) managing results of personal interest in favorite categories, (6) automated sequence annotation (such as NCBI NR and ontology-based annotation). PLAN integrates, by default, the Decypher hardware-based BLAST solution provided by Active Motif Inc. with a greatly improved efficiency over conventional BLAST software. BLAST results are visualized by spreadsheets and graphs and are full-text searchable. BLAST results and sequence annotations can be exported, in part or in full, in various formats including Microsoft Excel and FASTA. Sequences and BLAST results are organized in projects, the data publication levels of which are controlled by the registered project owners. In addition, all analytical functions are provided to public users without registration. PLAN has proved a valuable addition to the community for automated high-throughput BLAST searches, and, more importantly, for knowledge discovery, management and sharing based on sequence alignment results. The PLAN web interface is platform-independent, easily configurable and capable of comprehensive expansion, and user-intuitive. PLAN is freely available to academic users at http://bioinfo.noble.org/plan/. The source code for local deployment is provided under free license. Full support on system utilization, installation, configuration and customization are provided to academic users.

PLAN: a web platform for automating high-throughput BLAST searches and for managing and mining results

PubMed Central

He, Ji; Dai, Xinbin; Zhao, Xuechun

2007-01-01

Background BLAST searches are widely used for sequence alignment. The search results are commonly adopted for various functional and comparative genomics tasks such as annotating unknown sequences, investigating gene models and comparing two sequence sets. Advances in sequencing technologies pose challenges for high-throughput analysis of large-scale sequence data. A number of programs and hardware solutions exist for efficient BLAST searching, but there is a lack of generic software solutions for mining and personalized management of the results. Systematically reviewing the results and identifying information of interest remains tedious and time-consuming. Results Personal BLAST Navigator (PLAN) is a versatile web platform that helps users to carry out various personalized pre- and post-BLAST tasks, including: (1) query and target sequence database management, (2) automated high-throughput BLAST searching, (3) indexing and searching of results, (4) filtering results online, (5) managing results of personal interest in favorite categories, (6) automated sequence annotation (such as NCBI NR and ontology-based annotation). PLAN integrates, by default, the Decypher hardware-based BLAST solution provided by Active Motif Inc. with a greatly improved efficiency over conventional BLAST software. BLAST results are visualized by spreadsheets and graphs and are full-text searchable. BLAST results and sequence annotations can be exported, in part or in full, in various formats including Microsoft Excel and FASTA. Sequences and BLAST results are organized in projects, the data publication levels of which are controlled by the registered project owners. In addition, all analytical functions are provided to public users without registration. Conclusion PLAN has proved a valuable addition to the community for automated high-throughput BLAST searches, and, more importantly, for knowledge discovery, management and sharing based on sequence alignment results. The PLAN web interface is platform-independent, easily configurable and capable of comprehensive expansion, and user-intuitive. PLAN is freely available to academic users at . The source code for local deployment is provided under free license. Full support on system utilization, installation, configuration and customization are provided to academic users. PMID:17291345

Web-Based Text Structure Strategy Instruction Improves Seventh Graders' Content Area Reading Comprehension

ERIC Educational Resources Information Center

Wijekumar, Kausalai; Meyer, Bonnie J. F.; Lei, Puiwa

2017-01-01

Reading comprehension in the content areas is a challenge for many middle grade students. Text structure-based instruction has yielded positive outcomes in reading comprehension at all grade levels in small and large studies. The text structure strategy delivered via the web, called Intelligent Tutoring System for the Text Structure Strategy…

Development of Database for Accident Analysis in Indian Mines

NASA Astrophysics Data System (ADS)

Tripathy, Debi Prasad; Guru Raghavendra Reddy, K.

2016-10-01

Mining is a hazardous industry and high accident rates associated with underground mining is a cause of deep concern. Technological developments notwithstanding, rate of fatal accidents and reportable incidents have not shown corresponding levels of decline. This paper argues that adoption of appropriate safety standards by both mine management and the government may result in appreciable reduction in accident frequency. This can be achieved by using the technology in improving the working conditions, sensitising workers and managers about causes and prevention of accidents. Inputs required for a detailed analysis of an accident include information on location, time, type, cost of accident, victim, nature of injury, personal and environmental factors etc. Such information can be generated from data available in the standard coded accident report form. This paper presents a web based application for accident analysis in Indian mines during 2001-2013. An accident database (SafeStat) prototype based on Intranet of the TCP/IP agreement, as developed by the authors, is also discussed.

Research on Classification of Chinese Text Data Based on SVM

NASA Astrophysics Data System (ADS)

Lin, Yuan; Yu, Hongzhi; Wan, Fucheng; Xu, Tao

2017-09-01

Data Mining has important application value in today’s industry and academia. Text classification is a very important technology in data mining. At present, there are many mature algorithms for text classification. KNN, NB, AB, SVM, decision tree and other classification methods all show good classification performance. Support Vector Machine’ (SVM) classification method is a good classifier in machine learning research. This paper will study the classification effect based on the SVM method in the Chinese text data, and use the support vector machine method in the chinese text to achieve the classify chinese text, and to able to combination of academia and practical application.

Rare disease diagnosis: A review of web search, social media and large-scale data-mining approaches.

PubMed

Svenstrup, Dan; Jørgensen, Henrik L; Winther, Ole

2015-01-01

Physicians and the general public are increasingly using web-based tools to find answers to medical questions. The field of rare diseases is especially challenging and important as shown by the long delay and many mistakes associated with diagnoses. In this paper we review recent initiatives on the use of web search, social media and data mining in data repositories for medical diagnosis. We compare the retrieval accuracy on 56 rare disease cases with known diagnosis for the web search tools google.com, pubmed.gov, omim.org and our own search tool findzebra.com. We give a detailed description of IBM's Watson system and make a rough comparison between findzebra.com and Watson on subsets of the Doctor's dilemma dataset. The recall@10 and recall@20 (fraction of cases where the correct result appears in top 10 and top 20) for the 56 cases are found to be be 29%, 16%, 27% and 59% and 32%, 18%, 34% and 64%, respectively. Thus, FindZebra has a significantly (p < 0.01) higher recall than the other 3 search engines. When tested under the same conditions, Watson and FindZebra showed similar recall@10 accuracy. However, the tests were performed on different subsets of Doctors dilemma questions. Advances in technology and access to high quality data have opened new possibilities for aiding the diagnostic process. Specialized search engines, data mining tools and social media are some of the areas that hold promise.

Rare disease diagnosis: A review of web search, social media and large-scale data-mining approaches

PubMed Central

Svenstrup, Dan; Jørgensen, Henrik L; Winther, Ole

2015-01-01

Physicians and the general public are increasingly using web-based tools to find answers to medical questions. The field of rare diseases is especially challenging and important as shown by the long delay and many mistakes associated with diagnoses. In this paper we review recent initiatives on the use of web search, social media and data mining in data repositories for medical diagnosis. We compare the retrieval accuracy on 56 rare disease cases with known diagnosis for the web search tools google.com, pubmed.gov, omim.org and our own search tool findzebra.com. We give a detailed description of IBM's Watson system and make a rough comparison between findzebra.com and Watson on subsets of the Doctor's dilemma dataset. The recall@10 and recall@20 (fraction of cases where the correct result appears in top 10 and top 20) for the 56 cases are found to be be 29%, 16%, 27% and 59% and 32%, 18%, 34% and 64%, respectively. Thus, FindZebra has a significantly (p < 0.01) higher recall than the other 3 search engines. When tested under the same conditions, Watson and FindZebra showed similar recall@10 accuracy. However, the tests were performed on different subsets of Doctors dilemma questions. Advances in technology and access to high quality data have opened new possibilities for aiding the diagnostic process. Specialized search engines, data mining tools and social media are some of the areas that hold promise. PMID:26442199

Text Mining Improves Prediction of Protein Functional Sites

PubMed Central

Cohn, Judith D.; Ravikumar, Komandur E.

2012-01-01

We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions. PMID:22393388

The BEL information extraction workflow (BELIEF): evaluation in the BioCreative V BEL and IAT track

PubMed Central

Madan, Sumit; Hodapp, Sven; Senger, Philipp; Ansari, Sam; Szostak, Justyna; Hoeng, Julia; Peitsch, Manuel; Fluck, Juliane

2016-01-01

Network-based approaches have become extremely important in systems biology to achieve a better understanding of biological mechanisms. For network representation, the Biological Expression Language (BEL) is well designed to collate findings from the scientific literature into biological network models. To facilitate encoding and biocuration of such findings in BEL, a BEL Information Extraction Workflow (BELIEF) was developed. BELIEF provides a web-based curation interface, the BELIEF Dashboard, that incorporates text mining techniques to support the biocurator in the generation of BEL networks. The underlying UIMA-based text mining pipeline (BELIEF Pipeline) uses several named entity recognition processes and relationship extraction methods to detect concepts and BEL relationships in literature. The BELIEF Dashboard allows easy curation of the automatically generated BEL statements and their context annotations. Resulting BEL statements and their context annotations can be syntactically and semantically verified to ensure consistency in the BEL network. In summary, the workflow supports experts in different stages of systems biology network building. Based on the BioCreative V BEL track evaluation, we show that the BELIEF Pipeline automatically extracts relationships with an F-score of 36.4% and fully correct statements can be obtained with an F-score of 30.8%. Participation in the BioCreative V Interactive task (IAT) track with BELIEF revealed a systems usability scale (SUS) of 67. Considering the complexity of the task for new users—learning BEL, working with a completely new interface, and performing complex curation—a score so close to the overall SUS average highlights the usability of BELIEF. Database URL: BELIEF is available at http://www.scaiview.com/belief/ PMID:27694210

The BEL information extraction workflow (BELIEF): evaluation in the BioCreative V BEL and IAT track.

PubMed

Madan, Sumit; Hodapp, Sven; Senger, Philipp; Ansari, Sam; Szostak, Justyna; Hoeng, Julia; Peitsch, Manuel; Fluck, Juliane

2016-01-01

Network-based approaches have become extremely important in systems biology to achieve a better understanding of biological mechanisms. For network representation, the Biological Expression Language (BEL) is well designed to collate findings from the scientific literature into biological network models. To facilitate encoding and biocuration of such findings in BEL, a BEL Information Extraction Workflow (BELIEF) was developed. BELIEF provides a web-based curation interface, the BELIEF Dashboard, that incorporates text mining techniques to support the biocurator in the generation of BEL networks. The underlying UIMA-based text mining pipeline (BELIEF Pipeline) uses several named entity recognition processes and relationship extraction methods to detect concepts and BEL relationships in literature. The BELIEF Dashboard allows easy curation of the automatically generated BEL statements and their context annotations. Resulting BEL statements and their context annotations can be syntactically and semantically verified to ensure consistency in the BEL network. In summary, the workflow supports experts in different stages of systems biology network building. Based on the BioCreative V BEL track evaluation, we show that the BELIEF Pipeline automatically extracts relationships with an F-score of 36.4% and fully correct statements can be obtained with an F-score of 30.8%. Participation in the BioCreative V Interactive task (IAT) track with BELIEF revealed a systems usability scale (SUS) of 67. Considering the complexity of the task for new users-learning BEL, working with a completely new interface, and performing complex curation-a score so close to the overall SUS average highlights the usability of BELIEF.Database URL: BELIEF is available at http://www.scaiview.com/belief/. © The Author(s) 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

PREDOSE: A Semantic Web Platform for Drug Abuse Epidemiology using Social Media

PubMed Central

Cameron, Delroy; Smith, Gary A.; Daniulaityte, Raminta; Sheth, Amit P.; Dave, Drashti; Chen, Lu; Anand, Gaurish; Carlson, Robert; Watkins, Kera Z.; Falck, Russel

2013-01-01

Objectives The role of social media in biomedical knowledge mining, including clinical, medical and healthcare informatics, prescription drug abuse epidemiology and drug pharmacology, has become increasingly significant in recent years. Social media offers opportunities for people to share opinions and experiences freely in online communities, which may contribute information beyond the knowledge of domain professionals. This paper describes the development of a novel Semantic Web platform called PREDOSE (PREscription Drug abuse Online Surveillance and Epidemiology), which is designed to facilitate the epidemiologic study of prescription (and related) drug abuse practices using social media. PREDOSE uses web forum posts and domain knowledge, modeled in a manually created Drug Abuse Ontology (DAO) (pronounced dow), to facilitate the extraction of semantic information from User Generated Content (UGC). A combination of lexical, pattern-based and semantics-based techniques is used together with the domain knowledge to extract fine-grained semantic information from UGC. In a previous study, PREDOSE was used to obtain the datasets from which new knowledge in drug abuse research was derived. Here, we report on various platform enhancements, including an updated DAO, new components for relationship and triple extraction, and tools for content analysis, trend detection and emerging patterns exploration, which enhance the capabilities of the PREDOSE platform. Given these enhancements, PREDOSE is now more equipped to impact drug abuse research by alleviating traditional labor-intensive content analysis tasks. Methods Using custom web crawlers that scrape UGC from publicly available web forums, PREDOSE first automates the collection of web-based social media content for subsequent semantic annotation. The annotation scheme is modeled in the DAO, and includes domain specific knowledge such as prescription (and related) drugs, methods of preparation, side effects, routes of administration, etc. The DAO is also used to help recognize three types of data, namely: 1) entities, 2) relationships and 3) triples. PREDOSE then uses a combination of lexical and semantic-based techniques to extract entities and relationships from the scraped content, and a top-down approach for triple extraction that uses patterns expressed in the DAO. In addition, PREDOSE uses publicly available lexicons to identify initial sentiment expressions in text, and then a probabilistic optimization algorithm (from related research) to extract the final sentiment expressions. Together, these techniques enable the capture of fine-grained semantic information from UGC, and querying, search, trend analysis and overall content analysis of social media related to prescription drug abuse. Moreover, extracted data are also made available to domain experts for the creation of training and test sets for use in evaluation and refinements in information extraction techniques. Results A recent evaluation of the information extraction techniques applied in the PREDOSE platform indicates 85% precision and 72% recall in entity identification, on a manually created gold standard dataset. In another study, PREDOSE achieved 36% precision in relationship identification and 33% precision in triple extraction, through manual evaluation by domain experts. Given the complexity of the relationship and triple extraction tasks and the abstruse nature of social media texts, we interpret these as favorable initial results. Extracted semantic information is currently in use in an online discovery support system, by prescription drug abuse researchers at the Center for Interventions, Treatment and Addictions Research (CITAR) at Wright State University. Conclusion A comprehensive platform for entity, relationship, triple and sentiment extraction from such abstruse texts has never been developed for drug abuse research. PREDOSE has already demonstrated the importance of mining social media by providing data from which new findings in drug abuse research were uncovered. Given the recent platform enhancements, including the refined DAO, components for relationship and triple extraction, and tools for content, trend and emerging pattern analysis, it is expected that PREDOSE will play a significant role in advancing drug abuse epidemiology in future. PMID:23892295

PREDOSE: a semantic web platform for drug abuse epidemiology using social media.

PubMed

Cameron, Delroy; Smith, Gary A; Daniulaityte, Raminta; Sheth, Amit P; Dave, Drashti; Chen, Lu; Anand, Gaurish; Carlson, Robert; Watkins, Kera Z; Falck, Russel

2013-12-01

The role of social media in biomedical knowledge mining, including clinical, medical and healthcare informatics, prescription drug abuse epidemiology and drug pharmacology, has become increasingly significant in recent years. Social media offers opportunities for people to share opinions and experiences freely in online communities, which may contribute information beyond the knowledge of domain professionals. This paper describes the development of a novel semantic web platform called PREDOSE (PREscription Drug abuse Online Surveillance and Epidemiology), which is designed to facilitate the epidemiologic study of prescription (and related) drug abuse practices using social media. PREDOSE uses web forum posts and domain knowledge, modeled in a manually created Drug Abuse Ontology (DAO--pronounced dow), to facilitate the extraction of semantic information from User Generated Content (UGC), through combination of lexical, pattern-based and semantics-based techniques. In a previous study, PREDOSE was used to obtain the datasets from which new knowledge in drug abuse research was derived. Here, we report on various platform enhancements, including an updated DAO, new components for relationship and triple extraction, and tools for content analysis, trend detection and emerging patterns exploration, which enhance the capabilities of the PREDOSE platform. Given these enhancements, PREDOSE is now more equipped to impact drug abuse research by alleviating traditional labor-intensive content analysis tasks. Using custom web crawlers that scrape UGC from publicly available web forums, PREDOSE first automates the collection of web-based social media content for subsequent semantic annotation. The annotation scheme is modeled in the DAO, and includes domain specific knowledge such as prescription (and related) drugs, methods of preparation, side effects, and routes of administration. The DAO is also used to help recognize three types of data, namely: (1) entities, (2) relationships and (3) triples. PREDOSE then uses a combination of lexical and semantic-based techniques to extract entities and relationships from the scraped content, and a top-down approach for triple extraction that uses patterns expressed in the DAO. In addition, PREDOSE uses publicly available lexicons to identify initial sentiment expressions in text, and then a probabilistic optimization algorithm (from related research) to extract the final sentiment expressions. Together, these techniques enable the capture of fine-grained semantic information, which facilitate search, trend analysis and overall content analysis using social media on prescription drug abuse. Moreover, extracted data are also made available to domain experts for the creation of training and test sets for use in evaluation and refinements in information extraction techniques. A recent evaluation of the information extraction techniques applied in the PREDOSE platform indicates 85% precision and 72% recall in entity identification, on a manually created gold standard dataset. In another study, PREDOSE achieved 36% precision in relationship identification and 33% precision in triple extraction, through manual evaluation by domain experts. Given the complexity of the relationship and triple extraction tasks and the abstruse nature of social media texts, we interpret these as favorable initial results. Extracted semantic information is currently in use in an online discovery support system, by prescription drug abuse researchers at the Center for Interventions, Treatment and Addictions Research (CITAR) at Wright State University. A comprehensive platform for entity, relationship, triple and sentiment extraction from such abstruse texts has never been developed for drug abuse research. PREDOSE has already demonstrated the importance of mining social media by providing data from which new findings in drug abuse research were uncovered. Given the recent platform enhancements, including the refined DAO, components for relationship and triple extraction, and tools for content, trend and emerging pattern analysis, it is expected that PREDOSE will play a significant role in advancing drug abuse epidemiology in future. Copyright © 2013 Elsevier Inc. All rights reserved.

Information system for preserving culture heritage in areas affected by heavy industry and mining

NASA Astrophysics Data System (ADS)

Pacina, Jan; Kopecký, Jiří; Bedrníková, Lenka; Handrychová, Barbora; Švarcová, Martina; Holá, Markéta; Pončíková, Edita

2014-05-01

The natural development of the Ústí region (North-West Bohemia, the Czech Republic) has been affected by the human activity during the past hundred years. The heavy industrialization and the brown coal mining have completely changed the land-use in the region. The open-pit coal mines are completely destroying the surrounding landscape, including settlement, communications, hydrological network and the over-all natural development of the region. The other factor affecting the natural development of the landscape, land-use and settlement was the political situation in 1945 (end of the 2nd World War) when the borderland was depopulated. All these factors caused vanishing of more than two hundreds of colonies, villages and towns during this period of time. The task of this project is to prepare and offer for public use a comprehensive information system preserving the cultural heritage in the form of processed old maps, aerial imagery, land-use and georelief reconstructions, local studies, text and photo documents covering the extinct landscape and settlement. Wide range of various maps was used for this area - Müller's map of Bohemia (ca. 1720) followed by the 1st, 2nd and 3rd Military survey of Habsburg empire (1792, 1894, 1938), maps of Stabile cadaster (ca. 1840) and State map derived in the scale 1:5000 (1953, 1972, 1981). All the maps were processed, georeferenced, hand digitized and are further used as base layers for visualization and analysis. The historical aerial imagery was processed in standard ways of photogrammetry and is covering the year 1938, 1953 and the current state. The other important task covered by this project is the georelief reconstruction. We use the old maps and aerial imagery to reconstruct the complete time-line of the georelief development. This time-line is covering the period since 1938 until now. The derived digital terrain models and further on analyzed and printed on a 3D printer. Other reconstruction task are performed using the processed old maps - here we are studying the land-use change, settlement development and the industrialization and brown coal mining effect on the hydrological network structure. The processed data (old maps, aerial photographs, land-use and georelief reconstructions) are published as a web-mapping application built using the ArcGIS API for Flex technology. The application is offering visualization and overlay tools so the user can perform basic landscape and land-use development analyses. The resulting information system will consist of three parts - the web-mapping application, database containing the text and photo information about the vanished towns and villages (spatially linked to the web-mapping application) and other local studies performed on single sites in the region. The local studies are focused on application of data collection methods as UAV (Unmanned Aerial Vehicle), KAP (Kite Aerial Photography) and LIDAR.

Automatic document classification of biological literature

PubMed Central

Chen, David; Müller, Hans-Michael; Sternberg, Paul W

2006-01-01

Background Document classification is a wide-spread problem with many applications, from organizing search engine snippets to spam filtering. We previously described Textpresso, a text-mining system for biological literature, which marks up full text according to a shallow ontology that includes terms of biological interest. This project investigates document classification in the context of biological literature, making use of the Textpresso markup of a corpus of Caenorhabditis elegans literature. Results We present a two-step text categorization algorithm to classify a corpus of C. elegans papers. Our classification method first uses a support vector machine-trained classifier, followed by a novel, phrase-based clustering algorithm. This clustering step autonomously creates cluster labels that are descriptive and understandable by humans. This clustering engine performed better on a standard test-set (Reuters 21578) compared to previously published results (F-value of 0.55 vs. 0.49), while producing cluster descriptions that appear more useful. A web interface allows researchers to quickly navigate through the hierarchy and look for documents that belong to a specific concept. Conclusion We have demonstrated a simple method to classify biological documents that embodies an improvement over current methods. While the classification results are currently optimized for Caenorhabditis elegans papers by human-created rules, the classification engine can be adapted to different types of documents. We have demonstrated this by presenting a web interface that allows researchers to quickly navigate through the hierarchy and look for documents that belong to a specific concept. PMID:16893465

Using Web Crawler Technology for Text Analysis of Geo-Events: A Case Study of the Huangyan Island Incident

NASA Astrophysics Data System (ADS)

Hu, H.; Ge, Y. J.

2013-11-01

With the social networking and network socialisation have brought more text information and social relationships into our daily lives, the question of whether big data can be fully used to study the phenomenon and discipline of natural sciences has prompted many specialists and scholars to innovate their research. Though politics were integrally involved in the hyperlinked word issues since 1990s, automatic assembly of different geospatial web and distributed geospatial information systems utilizing service chaining have explored and built recently, the information collection and data visualisation of geo-events have always faced the bottleneck of traditional manual analysis because of the sensibility, complexity, relativity, timeliness and unexpected characteristics of political events. Based on the framework of Heritrix and the analysis of web-based text, word frequency, sentiment tendency and dissemination path of the Huangyan Island incident is studied here by combining web crawler technology and the text analysis method. The results indicate that tag cloud, frequency map, attitudes pie, individual mention ratios and dissemination flow graph based on the data collection and processing not only highlight the subject and theme vocabularies of related topics but also certain issues and problems behind it. Being able to express the time-space relationship of text information and to disseminate the information regarding geo-events, the text analysis of network information based on focused web crawler technology can be a tool for understanding the formation and diffusion of web-based public opinions in political events.

Do sign language videos improve Web navigation for Deaf Signer users?

PubMed

Fajardo, Inmaculada; Parra, Elena; Cañas, José J

2010-01-01

The efficacy of video-based sign language (SL) navigation aids to improve Web search for Deaf Signers was tested by two experiments. Experiment 1 compared 2 navigation aids based on text hyperlinks linked to embedded SL videos, which differed in the spatial contiguity between the text hyperlink and SL video (contiguous vs. distant). Deaf Signers' performance was similar in Web search using both aids, but a positive correlation between their word categorization abilities and search efficiency appeared in the distant condition. In Experiment 2, the contiguous condition was compared with a text-only hyperlink condition. Deaf Signers became less disorientated (used shorter paths to find the target) in the text plus SL condition than in the text-only condition. In addition, the positive correlation between word categorization abilities and search only appeared in the text-only condition. These findings suggest that SL videos added to text hyperlinks improve Web search efficiency for Deaf Signers.

Lexical Link Analysis Application: Improving Web Service to Acquisition Visibility Portal Phase II

DTIC Science & Technology

2014-04-30

bäÉîÉåíÜ=^ååì~ä=^Åèìáëáíáçå= oÉëÉ~êÅÜ=póãéçëáìã= qÜìêëÇ~ó=pÉëëáçåë= sçäìãÉ=ff= = Lexical Link Analysis Application: Improving Web Service to...DATE 30 APR 2014 2. REPORT TYPE 3. DATES COVERED 00-00-2014 to 00-00-2014 4. TITLE AND SUBTITLE Lexical Link Analysis Application: Improving...vocabulary or lexicon, to describe the attributes and surrounding environment of the system. Lexical Link Analysis (LLA) is a form of text mining in which

Hymenoptera Genome Database: integrating genome annotations in HymenopteraMine.

PubMed

Elsik, Christine G; Tayal, Aditi; Diesh, Colin M; Unni, Deepak R; Emery, Marianne L; Nguyen, Hung N; Hagen, Darren E

2016-01-04

We report an update of the Hymenoptera Genome Database (HGD) (http://HymenopteraGenome.org), a model organism database for insect species of the order Hymenoptera (ants, bees and wasps). HGD maintains genomic data for 9 bee species, 10 ant species and 1 wasp, including the versions of genome and annotation data sets published by the genome sequencing consortiums and those provided by NCBI. A new data-mining warehouse, HymenopteraMine, based on the InterMine data warehousing system, integrates the genome data with data from external sources and facilitates cross-species analyses based on orthology. New genome browsers and annotation tools based on JBrowse/WebApollo provide easy genome navigation, and viewing of high throughput sequence data sets and can be used for collaborative genome annotation. All of the genomes and annotation data sets are combined into a single BLAST server that allows users to select and combine sequence data sets to search. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Leveraging Pattern Semantics for Extracting Entities in Enterprises

PubMed Central

Tao, Fangbo; Zhao, Bo; Fuxman, Ariel; Li, Yang; Han, Jiawei

2015-01-01

Entity Extraction is a process of identifying meaningful entities from text documents. In enterprises, extracting entities improves enterprise efficiency by facilitating numerous applications, including search, recommendation, etc. However, the problem is particularly challenging on enterprise domains due to several reasons. First, the lack of redundancy of enterprise entities makes previous web-based systems like NELL and OpenIE not effective, since using only high-precision/low-recall patterns like those systems would miss the majority of sparse enterprise entities, while using more low-precision patterns in sparse setting also introduces noise drastically. Second, semantic drift is common in enterprises (“Blue” refers to “Windows Blue”), such that public signals from the web cannot be directly applied on entities. Moreover, many internal entities never appear on the web. Sparse internal signals are the only source for discovering them. To address these challenges, we propose an end-to-end framework for extracting entities in enterprises, taking the input of enterprise corpus and limited seeds to generate a high-quality entity collection as output. We introduce the novel concept of Semantic Pattern Graph to leverage public signals to understand the underlying semantics of lexical patterns, reinforce pattern evaluation using mined semantics, and yield more accurate and complete entities. Experiments on Microsoft enterprise data show the effectiveness of our approach. PMID:26705540

Leveraging Pattern Semantics for Extracting Entities in Enterprises.

PubMed

Tao, Fangbo; Zhao, Bo; Fuxman, Ariel; Li, Yang; Han, Jiawei

2015-05-01

Entity Extraction is a process of identifying meaningful entities from text documents. In enterprises, extracting entities improves enterprise efficiency by facilitating numerous applications, including search, recommendation, etc. However, the problem is particularly challenging on enterprise domains due to several reasons. First, the lack of redundancy of enterprise entities makes previous web-based systems like NELL and OpenIE not effective, since using only high-precision/low-recall patterns like those systems would miss the majority of sparse enterprise entities, while using more low-precision patterns in sparse setting also introduces noise drastically. Second, semantic drift is common in enterprises ("Blue" refers to "Windows Blue"), such that public signals from the web cannot be directly applied on entities. Moreover, many internal entities never appear on the web. Sparse internal signals are the only source for discovering them. To address these challenges, we propose an end-to-end framework for extracting entities in enterprises, taking the input of enterprise corpus and limited seeds to generate a high-quality entity collection as output. We introduce the novel concept of Semantic Pattern Graph to leverage public signals to understand the underlying semantics of lexical patterns, reinforce pattern evaluation using mined semantics, and yield more accurate and complete entities. Experiments on Microsoft enterprise data show the effectiveness of our approach.

Illuminate Knowledge Elements in Geoscience Literature

NASA Astrophysics Data System (ADS)

Ma, X.; Zheng, J. G.; Wang, H.; Fox, P. A.

2015-12-01

There are numerous dark data hidden in geoscience literature. Efficient retrieval and reuse of those data will greatly benefit geoscience researches of nowadays. Among the works of data rescue, a topic of interest is illuminating the knowledge framework, i.e. entities and relationships, embedded in documents. Entity recognition and linking have received extensive attention in news and social media analysis, as well as in bioinformatics. In the domain of geoscience, however, such works are limited. We will present our work on how to use knowledge bases on the Web, such as ontologies and vocabularies, to facilitate entity recognition and linking in geoscience literature. The work deploys an un-supervised collective inference approach [1] to link entity mentions in unstructured texts to a knowledge base, which leverages the meaningful information and structures in ontologies and vocabularies for similarity computation and entity ranking. Our work is still in the initial stage towards the detection of knowledge frameworks in literature, and we have been collecting geoscience ontologies and vocabularies in order to build a comprehensive geoscience knowledge base [2]. We hope the work will initiate new ideas and collaborations on dark data rescue, as well as on the synthesis of data and knowledge from geoscience literature. References: 1. Zheng, J., Howsmon, D., Zhang, B., Hahn, J., McGuinness, D.L., Hendler, J., and Ji, H. 2014. Entity linking for biomedical literature. In Proceedings of ACM 8th International Workshop on Data and Text Mining in Bioinformatics, Shanghai, China. 2. Ma, X. Zheng, J., 2015. Linking geoscience entity mentions to the Web of Data. ESIP 2015 Summer Meeting, Pacific Grove, CA.

Integrated pathway-based transcription regulation network mining and visualization based on gene expression profiles.

PubMed

Kibinge, Nelson; Ono, Naoaki; Horie, Masafumi; Sato, Tetsuo; Sugiura, Tadao; Altaf-Ul-Amin, Md; Saito, Akira; Kanaya, Shigehiko

2016-06-01

Conventionally, workflows examining transcription regulation networks from gene expression data involve distinct analytical steps. There is a need for pipelines that unify data mining and inference deduction into a singular framework to enhance interpretation and hypotheses generation. We propose a workflow that merges network construction with gene expression data mining focusing on regulation processes in the context of transcription factor driven gene regulation. The pipeline implements pathway-based modularization of expression profiles into functional units to improve biological interpretation. The integrated workflow was implemented as a web application software (TransReguloNet) with functions that enable pathway visualization and comparison of transcription factor activity between sample conditions defined in the experimental design. The pipeline merges differential expression, network construction, pathway-based abstraction, clustering and visualization. The framework was applied in analysis of actual expression datasets related to lung, breast and prostrate cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

Mobile Formative Assessment Tool Based on Data Mining Techniques for Supporting Web-Based Learning

ERIC Educational Resources Information Center

Chen, Chih-Ming; Chen, Ming-Chuan

2009-01-01

Current trends clearly indicate that online learning has become an important learning mode. However, no effective assessment mechanism for learning performance yet exists for e-learning systems. Learning performance assessment aims to evaluate what learners learned during the learning process. Traditional summative evaluation only considers final…

A New MI-Based Visualization Aided Validation Index for Mining Big Longitudinal Web Trial Data

PubMed Central

Zhang, Zhaoyang; Fang, Hua; Wang, Honggang

2016-01-01

Web-delivered clinical trials generate big complex data. To help untangle the heterogeneity of treatment effects, unsupervised learning methods have been widely applied. However, identifying valid patterns is a priority but challenging issue for these methods. This paper, built upon our previous research on multiple imputation (MI)-based fuzzy clustering and validation, proposes a new MI-based Visualization-aided validation index (MIVOOS) to determine the optimal number of clusters for big incomplete longitudinal Web-trial data with inflated zeros. Different from a recently developed fuzzy clustering validation index, MIVOOS uses a more suitable overlap and separation measures for Web-trial data but does not depend on the choice of fuzzifiers as the widely used Xie and Beni (XB) index. Through optimizing the view angles of 3-D projections using Sammon mapping, the optimal 2-D projection-guided MIVOOS is obtained to better visualize and verify the patterns in conjunction with trajectory patterns. Compared with XB and VOS, our newly proposed MIVOOS shows its robustness in validating big Web-trial data under different missing data mechanisms using real and simulated Web-trial data. PMID:27482473

Mining of Business-Oriented Conversations at a Call Center

NASA Astrophysics Data System (ADS)

Takeuchi, Hironori; Nasukawa, Tetsuya; Watanabe, Hideo

Recently it has become feasible to transcribe textual records from telephone conversations at call centers by using automatic speech recognition. In this research, we extended a text mining system for call summary records and constructed a conversation mining system for the business-oriented conversations at the call center. To acquire useful business insights from the conversational data through the text mining system, it is critical to identify appropriate textual segments and expressions as the viewpoints to focus on. In the analysis of call summary data using a text mining system, some experts defined the viewpoints for the analysis by looking at some sample records and by preparing the dictionaries based on frequent keywords in the sample dataset. However with conversations it is difficult to identify such viewpoints manually and in advance because the target data consists of complete transcripts that are often lengthy and redundant. In this research, we defined a model of the business-oriented conversations and proposed a mining method to identify segments that have impacts on the outcomes of the conversations and can then extract useful expressions in each of these identified segments. In the experiment, we processed the real datasets from a car rental service center and constructed a mining system. With this system, we show the effectiveness of the method based on the defined conversation model.

Using text mining for study identification in systematic reviews: a systematic review of current approaches.

PubMed

O'Mara-Eves, Alison; Thomas, James; McNaught, John; Miwa, Makoto; Ananiadou, Sophia

2015-01-14

The large and growing number of published studies, and their increasing rate of publication, makes the task of identifying relevant studies in an unbiased way for inclusion in systematic reviews both complex and time consuming. Text mining has been offered as a potential solution: through automating some of the screening process, reviewer time can be saved. The evidence base around the use of text mining for screening has not yet been pulled together systematically; this systematic review fills that research gap. Focusing mainly on non-technical issues, the review aims to increase awareness of the potential of these technologies and promote further collaborative research between the computer science and systematic review communities. Five research questions led our review: what is the state of the evidence base; how has workload reduction been evaluated; what are the purposes of semi-automation and how effective are they; how have key contextual problems of applying text mining to the systematic review field been addressed; and what challenges to implementation have emerged? We answered these questions using standard systematic review methods: systematic and exhaustive searching, quality-assured data extraction and a narrative synthesis to synthesise findings. The evidence base is active and diverse; there is almost no replication between studies or collaboration between research teams and, whilst it is difficult to establish any overall conclusions about best approaches, it is clear that efficiencies and reductions in workload are potentially achievable. On the whole, most suggested that a saving in workload of between 30% and 70% might be possible, though sometimes the saving in workload is accompanied by the loss of 5% of relevant studies (i.e. a 95% recall). Using text mining to prioritise the order in which items are screened should be considered safe and ready for use in 'live' reviews. The use of text mining as a 'second screener' may also be used cautiously. The use of text mining to eliminate studies automatically should be considered promising, but not yet fully proven. In highly technical/clinical areas, it may be used with a high degree of confidence; but more developmental and evaluative work is needed in other disciplines.

EXTRACT: interactive extraction of environment metadata and term suggestion for metagenomic sample annotation.

PubMed

Pafilis, Evangelos; Buttigieg, Pier Luigi; Ferrell, Barbra; Pereira, Emiliano; Schnetzer, Julia; Arvanitidis, Christos; Jensen, Lars Juhl

2016-01-01

The microbial and molecular ecology research communities have made substantial progress on developing standards for annotating samples with environment metadata. However, sample manual annotation is a highly labor intensive process and requires familiarity with the terminologies used. We have therefore developed an interactive annotation tool, EXTRACT, which helps curators identify and extract standard-compliant terms for annotation of metagenomic records and other samples. Behind its web-based user interface, the system combines published methods for named entity recognition of environment, organism, tissue and disease terms. The evaluators in the BioCreative V Interactive Annotation Task found the system to be intuitive, useful, well documented and sufficiently accurate to be helpful in spotting relevant text passages and extracting organism and environment terms. Comparison of fully manual and text-mining-assisted curation revealed that EXTRACT speeds up annotation by 15-25% and helps curators to detect terms that would otherwise have been missed. Database URL: https://extract.hcmr.gr/. © The Author(s) 2016. Published by Oxford University Press.

EXTRACT: Interactive extraction of environment metadata and term suggestion for metagenomic sample annotation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pafilis, Evangelos; Buttigieg, Pier Luigi; Ferrell, Barbra

The microbial and molecular ecology research communities have made substantial progress on developing standards for annotating samples with environment metadata. However, sample manual annotation is a highly labor intensive process and requires familiarity with the terminologies used. We have therefore developed an interactive annotation tool, EXTRACT, which helps curators identify and extract standard-compliant terms for annotation of metagenomic records and other samples. Behind its web-based user interface, the system combines published methods for named entity recognition of environment, organism, tissue and disease terms. The evaluators in the BioCreative V Interactive Annotation Task found the system to be intuitive, useful, wellmore » documented and sufficiently accurate to be helpful in spotting relevant text passages and extracting organism and environment terms. Here the comparison of fully manual and text-mining-assisted curation revealed that EXTRACT speeds up annotation by 15–25% and helps curators to detect terms that would otherwise have been missed.« less

EXTRACT: Interactive extraction of environment metadata and term suggestion for metagenomic sample annotation

DOE PAGES

Pafilis, Evangelos; Buttigieg, Pier Luigi; Ferrell, Barbra; ...

2016-01-01

The microbial and molecular ecology research communities have made substantial progress on developing standards for annotating samples with environment metadata. However, sample manual annotation is a highly labor intensive process and requires familiarity with the terminologies used. We have therefore developed an interactive annotation tool, EXTRACT, which helps curators identify and extract standard-compliant terms for annotation of metagenomic records and other samples. Behind its web-based user interface, the system combines published methods for named entity recognition of environment, organism, tissue and disease terms. The evaluators in the BioCreative V Interactive Annotation Task found the system to be intuitive, useful, wellmore » documented and sufficiently accurate to be helpful in spotting relevant text passages and extracting organism and environment terms. Here the comparison of fully manual and text-mining-assisted curation revealed that EXTRACT speeds up annotation by 15–25% and helps curators to detect terms that would otherwise have been missed.« less

[Text mining, a method for computer-assisted analysis of scientific texts, demonstrated by an analysis of author networks].

PubMed

Hahn, P; Dullweber, F; Unglaub, F; Spies, C K

2014-06-01

Searching for relevant publications is becoming more difficult with the increasing number of scientific articles. Text mining as a specific form of computer-based data analysis may be helpful in this context. Highlighting relations between authors and finding relevant publications concerning a specific subject using text analysis programs are illustrated graphically by 2 performed examples. © Georg Thieme Verlag KG Stuttgart · New York.

Biocuration workflows and text mining: overview of the BioCreative 2012 Workshop Track II.

PubMed

Lu, Zhiyong; Hirschman, Lynette

2012-01-01

Manual curation of data from the biomedical literature is a rate-limiting factor for many expert curated databases. Despite the continuing advances in biomedical text mining and the pressing needs of biocurators for better tools, few existing text-mining tools have been successfully integrated into production literature curation systems such as those used by the expert curated databases. To close this gap and better understand all aspects of literature curation, we invited submissions of written descriptions of curation workflows from expert curated databases for the BioCreative 2012 Workshop Track II. We received seven qualified contributions, primarily from model organism databases. Based on these descriptions, we identified commonalities and differences across the workflows, the common ontologies and controlled vocabularies used and the current and desired uses of text mining for biocuration. Compared to a survey done in 2009, our 2012 results show that many more databases are now using text mining in parts of their curation workflows. In addition, the workshop participants identified text-mining aids for finding gene names and symbols (gene indexing), prioritization of documents for curation (document triage) and ontology concept assignment as those most desired by the biocurators. DATABASE URL: http://www.biocreative.org/tasks/bc-workshop-2012/workflow/.

Redundancy in electronic health record corpora: analysis, impact on text mining performance and mitigation strategies.

PubMed

Cohen, Raphael; Elhadad, Michael; Elhadad, Noémie

2013-01-16

The increasing availability of Electronic Health Record (EHR) data and specifically free-text patient notes presents opportunities for phenotype extraction. Text-mining methods in particular can help disease modeling by mapping named-entities mentions to terminologies and clustering semantically related terms. EHR corpora, however, exhibit specific statistical and linguistic characteristics when compared with corpora in the biomedical literature domain. We focus on copy-and-paste redundancy: clinicians typically copy and paste information from previous notes when documenting a current patient encounter. Thus, within a longitudinal patient record, one expects to observe heavy redundancy. In this paper, we ask three research questions: (i) How can redundancy be quantified in large-scale text corpora? (ii) Conventional wisdom is that larger corpora yield better results in text mining. But how does the observed EHR redundancy affect text mining? Does such redundancy introduce a bias that distorts learned models? Or does the redundancy introduce benefits by highlighting stable and important subsets of the corpus? (iii) How can one mitigate the impact of redundancy on text mining? We analyze a large-scale EHR corpus and quantify redundancy both in terms of word and semantic concept repetition. We observe redundancy levels of about 30% and non-standard distribution of both words and concepts. We measure the impact of redundancy on two standard text-mining applications: collocation identification and topic modeling. We compare the results of these methods on synthetic data with controlled levels of redundancy and observe significant performance variation. Finally, we compare two mitigation strategies to avoid redundancy-induced bias: (i) a baseline strategy, keeping only the last note for each patient in the corpus; (ii) removing redundant notes with an efficient fingerprinting-based algorithm. (a)For text mining, preprocessing the EHR corpus with fingerprinting yields significantly better results. Before applying text-mining techniques, one must pay careful attention to the structure of the analyzed corpora. While the importance of data cleaning has been known for low-level text characteristics (e.g., encoding and spelling), high-level and difficult-to-quantify corpus characteristics, such as naturally occurring redundancy, can also hurt text mining. Fingerprinting enables text-mining techniques to leverage available data in the EHR corpus, while avoiding the bias introduced by redundancy.

Near-line Archive Data Mining at the Goddard Distributed Active Archive Center

NASA Astrophysics Data System (ADS)

Pham, L.; Mack, R.; Eng, E.; Lynnes, C.

2002-12-01

NASA's Earth Observing System (EOS) is generating immense volumes of data, in some cases too much to provide to users with data-intensive needs. As an alternative to moving the data to the user and his/her research algorithms, we are providing a means to move the algorithms to the data. The Near-line Archive Data Mining (NADM) system is the Goddard Earth Sciences Distributed Active Archive Center's (GES DAAC) web data mining portal to the EOS Data and Information System (EOSDIS) data pool, a 50-TB online disk cache. The NADM web portal enables registered users to submit and execute data mining algorithm codes on the data in the EOSDIS data pool. A web interface allows the user to access the NADM system. The users first develops personalized data mining code on their home platform and then uploads them to the NADM system. The C, FORTRAN and IDL languages are currently supported. The user developed code is automatically audited for any potential security problems before it is installed within the NADM system and made available to the user. Once the code has been installed the user is provided a test environment where he/she can test the execution of the software against data sets of the user's choosing. When the user is satisfied with the results, he/she can promote their code to the "operational" environment. From here the user can interactively run his/her code on the data available in the EOSDIS data pool. The user can also set up a processing subscription. The subscription will automatically process new data as it becomes available in the EOSDIS data pool. The generated mined data products are then made available for FTP pickup. The NADM system uses the GES DAAC-developed Simple Scalable Script-based Science Processor (S4P) to automate tasks and perform the actual data processing. Users will also have the option of selecting a DAAC-provided data mining algorithm and using it to process the data of their choice.

World Wide Web Based Image Search Engine Using Text and Image Content Features

NASA Astrophysics Data System (ADS)

Luo, Bo; Wang, Xiaogang; Tang, Xiaoou

2003-01-01

Using both text and image content features, a hybrid image retrieval system for Word Wide Web is developed in this paper. We first use a text-based image meta-search engine to retrieve images from the Web based on the text information on the image host pages to provide an initial image set. Because of the high-speed and low cost nature of the text-based approach, we can easily retrieve a broad coverage of images with a high recall rate and a relatively low precision. An image content based ordering is then performed on the initial image set. All the images are clustered into different folders based on the image content features. In addition, the images can be re-ranked by the content features according to the user feedback. Such a design makes it truly practical to use both text and image content for image retrieval over the Internet. Experimental results confirm the efficiency of the system.

Conceptual biology, hypothesis discovery, and text mining: Swanson's legacy.

PubMed

Bekhuis, Tanja

2006-04-03

Innovative biomedical librarians and information specialists who want to expand their roles as expert searchers need to know about profound changes in biology and parallel trends in text mining. In recent years, conceptual biology has emerged as a complement to empirical biology. This is partly in response to the availability of massive digital resources such as the network of databases for molecular biologists at the National Center for Biotechnology Information. Developments in text mining and hypothesis discovery systems based on the early work of Swanson, a mathematician and information scientist, are coincident with the emergence of conceptual biology. Very little has been written to introduce biomedical digital librarians to these new trends. In this paper, background for data and text mining, as well as for knowledge discovery in databases (KDD) and in text (KDT) is presented, then a brief review of Swanson's ideas, followed by a discussion of recent approaches to hypothesis discovery and testing. 'Testing' in the context of text mining involves partially automated methods for finding evidence in the literature to support hypothetical relationships. Concluding remarks follow regarding (a) the limits of current strategies for evaluation of hypothesis discovery systems and (b) the role of literature-based discovery in concert with empirical research. Report of an informatics-driven literature review for biomarkers of systemic lupus erythematosus is mentioned. Swanson's vision of the hidden value in the literature of science and, by extension, in biomedical digital databases, is still remarkably generative for information scientists, biologists, and physicians.

DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes.

PubMed

Piñero, Janet; Queralt-Rosinach, Núria; Bravo, Àlex; Deu-Pons, Jordi; Bauer-Mehren, Anna; Baron, Martin; Sanz, Ferran; Furlong, Laura I

2015-01-01

DisGeNET is a comprehensive discovery platform designed to address a variety of questions concerning the genetic underpinning of human diseases. DisGeNET contains over 380,000 associations between >16,000 genes and 13,000 diseases, which makes it one of the largest repositories currently available of its kind. DisGeNET integrates expert-curated databases with text-mined data, covers information on Mendelian and complex diseases, and includes data from animal disease models. It features a score based on the supporting evidence to prioritize gene-disease associations. It is an open access resource available through a web interface, a Cytoscape plugin and as a Semantic Web resource. The web interface supports user-friendly data exploration and navigation. DisGeNET data can also be analysed via the DisGeNET Cytoscape plugin, and enriched with the annotations of other plugins of this popular network analysis software suite. Finally, the information contained in DisGeNET can be expanded and complemented using Semantic Web technologies and linked to a variety of resources already present in the Linked Data cloud. Hence, DisGeNET offers one of the most comprehensive collections of human gene-disease associations and a valuable set of tools for investigating the molecular mechanisms underlying diseases of genetic origin, designed to fulfill the needs of different user profiles, including bioinformaticians, biologists and health-care practitioners. Database URL: http://www.disgenet.org/ © The Author(s) 2015. Published by Oxford University Press.

76 FR 60474 - Intent To Prepare a Draft Environmental Impact Statement (DEIS) for the Haile Gold Mine in...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-29

...--County on January 28, 2011. The public notice is available on Charleston District's public Web site at... eight open mining pits over a twelve-year period, with pit depths ranging from 110 to 840 feet deep. The... of January 28, 2011, and are available on Charleston District's public Web site at http://www.sac...

Graphics-based intelligent search and abstracting using Data Modeling

NASA Astrophysics Data System (ADS)

Jaenisch, Holger M.; Handley, James W.; Case, Carl T.; Songy, Claude G.

2002-11-01

This paper presents an autonomous text and context-mining algorithm that converts text documents into point clouds for visual search cues. This algorithm is applied to the task of data-mining a scriptural database comprised of the Old and New Testaments from the Bible and the Book of Mormon, Doctrine and Covenants, and the Pearl of Great Price. Results are generated which graphically show the scripture that represents the average concept of the database and the mining of the documents down to the verse level.

Mining protein function from text using term-based support vector machines

PubMed Central

Rice, Simon B; Nenadic, Goran; Stapley, Benjamin J

2005-01-01

Background Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents. Results The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent. Conclusion A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2. PMID:15960835

Developing a semantic web model for medical differential diagnosis recommendation.

PubMed

Mohammed, Osama; Benlamri, Rachid

2014-10-01

In this paper we describe a novel model for differential diagnosis designed to make recommendations by utilizing semantic web technologies. The model is a response to a number of requirements, ranging from incorporating essential clinical diagnostic semantics to the integration of data mining for the process of identifying candidate diseases that best explain a set of clinical features. We introduce two major components, which we find essential to the construction of an integral differential diagnosis recommendation model: the evidence-based recommender component and the proximity-based recommender component. Both approaches are driven by disease diagnosis ontologies designed specifically to enable the process of generating diagnostic recommendations. These ontologies are the disease symptom ontology and the patient ontology. The evidence-based diagnosis process develops dynamic rules based on standardized clinical pathways. The proximity-based component employs data mining to provide clinicians with diagnosis predictions, as well as generates new diagnosis rules from provided training datasets. This article describes the integration between these two components along with the developed diagnosis ontologies to form a novel medical differential diagnosis recommendation model. This article also provides test cases from the implementation of the overall model, which shows quite promising diagnostic recommendation results.

Biodiversity data mining from Argus-eyed citizens: the first illegal introduction record of Lepomis macrochirus macrochirus Rafinesque, 1819 in Japan based on Twitter information.

PubMed

Miyazaki, Yusuke; Teramura, Akinori; Senou, Hiroshi

2016-01-01

An apparent illegal introduction of Lepomis macrochirus macrochirus from Yokohama City, Kanagawa Prefecture, Japan, is reported based on a juvenile specimen and a photograph of two adults collected on 14 June 2015 and deposited in the Kangawa Prefectural Museum of Natural History. The specimens and photographs were initially reported on the internet-based social networking site, Twitter. Two specimens of Carassius auratus, including an aquarium form, were also reported at the same locality and date, suggesting that the illegal introductions originated from an aquarium release. Our report demonstrates an example of web data mining in the discipline of Citizen Science.

Annotating images by mining image search results.

PubMed

Wang, Xin-Jing; Zhang, Lei; Li, Xirong; Ma, Wei-Ying

2008-11-01

Although it has been studied for years by the computer vision and machine learning communities, image annotation is still far from practical. In this paper, we propose a novel attempt at model-free image annotation, which is a data-driven approach that annotates images by mining their search results. Some 2.4 million images with their surrounding text are collected from a few photo forums to support this approach. The entire process is formulated in a divide-and-conquer framework where a query keyword is provided along with the uncaptioned image to improve both the effectiveness and efficiency. This is helpful when the collected data set is not dense everywhere. In this sense, our approach contains three steps: 1) the search process to discover visually and semantically similar search results, 2) the mining process to identify salient terms from textual descriptions of the search results, and 3) the annotation rejection process to filter out noisy terms yielded by Step 2. To ensure real-time annotation, two key techniques are leveraged-one is to map the high-dimensional image visual features into hash codes, the other is to implement it as a distributed system, of which the search and mining processes are provided as Web services. As a typical result, the entire process finishes in less than 1 second. Since no training data set is required, our approach enables annotating with unlimited vocabulary and is highly scalable and robust to outliers. Experimental results on both real Web images and a benchmark image data set show the effectiveness and efficiency of the proposed algorithm. It is also worth noting that, although the entire approach is illustrated within the divide-and conquer framework, a query keyword is not crucial to our current implementation. We provide experimental results to prove this.

Development of a Google-based search engine for data mining radiology reports.

PubMed

Erinjeri, Joseph P; Picus, Daniel; Prior, Fred W; Rubin, David A; Koppel, Paul

2009-08-01

The aim of this study is to develop a secure, Google-based data-mining tool for radiology reports using free and open source technologies and to explore its use within an academic radiology department. A Health Insurance Portability and Accountability Act (HIPAA)-compliant data repository, search engine and user interface were created to facilitate treatment, operations, and reviews preparatory to research. The Institutional Review Board waived review of the project, and informed consent was not required. Comprising 7.9 GB of disk space, 2.9 million text reports were downloaded from our radiology information system to a fileserver. Extensible markup language (XML) representations of the reports were indexed using Google Desktop Enterprise search engine software. A hypertext markup language (HTML) form allowed users to submit queries to Google Desktop, and Google's XML response was interpreted by a practical extraction and report language (PERL) script, presenting ranked results in a web browser window. The query, reason for search, results, and documents visited were logged to maintain HIPAA compliance. Indexing averaged approximately 25,000 reports per hour. Keyword search of a common term like "pneumothorax" yielded the first ten most relevant results of 705,550 total results in 1.36 s. Keyword search of a rare term like "hemangioendothelioma" yielded the first ten most relevant results of 167 total results in 0.23 s; retrieval of all 167 results took 0.26 s. Data mining tools for radiology reports will improve the productivity of academic radiologists in clinical, educational, research, and administrative tasks. By leveraging existing knowledge of Google's interface, radiologists can quickly perform useful searches.

Text Mining for Neuroscience

NASA Astrophysics Data System (ADS)

Tirupattur, Naveen; Lapish, Christopher C.; Mukhopadhyay, Snehasis

2011-06-01

Text mining, sometimes alternately referred to as text analytics, refers to the process of extracting high-quality knowledge from the analysis of textual data. Text mining has wide variety of applications in areas such as biomedical science, news analysis, and homeland security. In this paper, we describe an approach and some relatively small-scale experiments which apply text mining to neuroscience research literature to find novel associations among a diverse set of entities. Neuroscience is a discipline which encompasses an exceptionally wide range of experimental approaches and rapidly growing interest. This combination results in an overwhelmingly large and often diffuse literature which makes a comprehensive synthesis difficult. Understanding the relations or associations among the entities appearing in the literature not only improves the researchers current understanding of recent advances in their field, but also provides an important computational tool to formulate novel hypotheses and thereby assist in scientific discoveries. We describe a methodology to automatically mine the literature and form novel associations through direct analysis of published texts. The method first retrieves a set of documents from databases such as PubMed using a set of relevant domain terms. In the current study these terms yielded a set of documents ranging from 160,909 to 367,214 documents. Each document is then represented in a numerical vector form from which an Association Graph is computed which represents relationships between all pairs of domain terms, based on co-occurrence. Association graphs can then be subjected to various graph theoretic algorithms such as transitive closure and cycle (circuit) detection to derive additional information, and can also be visually presented to a human researcher for understanding. In this paper, we present three relatively small-scale problem-specific case studies to demonstrate that such an approach is very successful in replicating a neuroscience expert's mental model of object-object associations entirely by means of text mining. These preliminary results provide the confidence that this type of text mining based research approach provides an extremely powerful tool to better understand the literature and drive novel discovery for the neuroscience community.

Multiple-Feature Extracting Modules Based Leak Mining System Design

PubMed Central

Cho, Ying-Chiang; Pan, Jen-Yi

2013-01-01

Over the years, human dependence on the Internet has increased dramatically. A large amount of information is placed on the Internet and retrieved from it daily, which makes web security in terms of online information a major concern. In recent years, the most problematic issues in web security have been e-mail address leakage and SQL injection attacks. There are many possible causes of information leakage, such as inadequate precautions during the programming process, which lead to the leakage of e-mail addresses entered online or insufficient protection of database information, a loophole that enables malicious users to steal online content. In this paper, we implement a crawler mining system that is equipped with SQL injection vulnerability detection, by means of an algorithm developed for the web crawler. In addition, we analyze portal sites of the governments of various countries or regions in order to investigate the information leaking status of each site. Subsequently, we analyze the database structure and content of each site, using the data collected. Thus, we make use of practical verification in order to focus on information security and privacy through black-box testing. PMID:24453892

Multiple-feature extracting modules based leak mining system design.

PubMed

Cho, Ying-Chiang; Pan, Jen-Yi

2013-01-01

Over the years, human dependence on the Internet has increased dramatically. A large amount of information is placed on the Internet and retrieved from it daily, which makes web security in terms of online information a major concern. In recent years, the most problematic issues in web security have been e-mail address leakage and SQL injection attacks. There are many possible causes of information leakage, such as inadequate precautions during the programming process, which lead to the leakage of e-mail addresses entered online or insufficient protection of database information, a loophole that enables malicious users to steal online content. In this paper, we implement a crawler mining system that is equipped with SQL injection vulnerability detection, by means of an algorithm developed for the web crawler. In addition, we analyze portal sites of the governments of various countries or regions in order to investigate the information leaking status of each site. Subsequently, we analyze the database structure and content of each site, using the data collected. Thus, we make use of practical verification in order to focus on information security and privacy through black-box testing.

Web usage mining at an academic health sciences library: an exploratory study.

PubMed

Bracke, Paul J

2004-10-01

This paper explores the potential of multinomial logistic regression analysis to perform Web usage mining for an academic health sciences library Website. Usage of database-driven resource gateway pages was logged for a six-month period, including information about users' network addresses, referring uniform resource locators (URLs), and types of resource accessed. It was found that referring URL did vary significantly by two factors: whether a user was on-campus and what type of resource was accessed. Although the data available for analysis are limited by the nature of the Web and concerns for privacy, this method demonstrates the potential for gaining insight into Web usage that supplements Web log analysis. It can be used to improve the design of static and dynamic Websites today and could be used in the design of more advanced Web systems in the future.

Omics AnalySIs System for PRecision Oncology (OASISPRO): A Web-based Omics Analysis Tool for Clinical Phenotype Prediction.

PubMed

Yu, Kun-Hsing; Fitzpatrick, Michael R; Pappas, Luke; Chan, Warren; Kung, Jessica; Snyder, Michael

2017-09-12

Precision oncology is an approach that accounts for individual differences to guide cancer management. Omics signatures have been shown to predict clinical traits for cancer patients. However, the vast amount of omics information poses an informatics challenge in systematically identifying patterns associated with health outcomes, and no general-purpose data-mining tool exists for physicians, medical researchers, and citizen scientists without significant training in programming and bioinformatics. To bridge this gap, we built the Omics AnalySIs System for PRecision Oncology (OASISPRO), a web-based system to mine the quantitative omics information from The Cancer Genome Atlas (TCGA). This system effectively visualizes patients' clinical profiles, executes machine-learning algorithms of choice on the omics data, and evaluates the prediction performance using held-out test sets. With this tool, we successfully identified genes strongly associated with tumor stage, and accurately predicted patients' survival outcomes in many cancer types, including mesothelioma and adrenocortical carcinoma. By identifying the links between omics and clinical phenotypes, this system will facilitate omics studies on precision cancer medicine and contribute to establishing personalized cancer treatment plans. This web-based tool is available at http://tinyurl.com/oasispro ;source codes are available at http://tinyurl.com/oasisproSourceCode . © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Application of text mining for customer evaluations in commercial banking

NASA Astrophysics Data System (ADS)

Tan, Jing; Du, Xiaojiang; Hao, Pengpeng; Wang, Yanbo J.

2015-07-01

Nowadays customer attrition is increasingly serious in commercial banks. To combat this problem roundly, mining customer evaluation texts is as important as mining customer structured data. In order to extract hidden information from customer evaluations, Textual Feature Selection, Classification and Association Rule Mining are necessary techniques. This paper presents all three techniques by using Chinese Word Segmentation, C5.0 and Apriori, and a set of experiments were run based on a collection of real textual data that includes 823 customer evaluations taken from a Chinese commercial bank. Results, consequent solutions, some advice for the commercial bank are given in this paper.

Advanced Query and Data Mining Capabilities for MaROS

NASA Technical Reports Server (NTRS)

Wang, Paul; Wallick, Michael N.; Allard, Daniel A.; Gladden, Roy E.; Hy, Franklin H.

2013-01-01

The Mars Relay Operational Service (MaROS) comprises a number of tools to coordinate, plan, and visualize various aspects of the Mars Relay network. These levels include a Web-based user interface, a back-end "ReSTlet" built in Java, and databases that store the data as it is received from the network. As part of MaROS, the innovators have developed and implemented a feature set that operates on several levels of the software architecture. This new feature is an advanced querying capability through either the Web-based user interface, or through a back-end REST interface to access all of the data gathered from the network. This software is not meant to replace the REST interface, but to augment and expand the range of available data. The current REST interface provides specific data that is used by the MaROS Web application to display and visualize the information; however, the returned information from the REST interface has typically been pre-processed to return only a subset of the entire information within the repository, particularly only the information that is of interest to the GUI (graphical user interface). The new, advanced query and data mining capabilities allow users to retrieve the raw data and/or to perform their own data processing. The query language used to access the repository is a restricted subset of the structured query language (SQL) that can be built safely from the Web user interface, or entered as freeform SQL by a user. The results are returned in a CSV (Comma Separated Values) format for easy exporting to third party tools and applications that can be used for data mining or user-defined visualization and interpretation. This is the first time that a service is capable of providing access to all cross-project relay data from a single Web resource. Because MaROS contains the data for a variety of missions from the Mars network, which span both NASA and ESA, the software also establishes an access control list (ACL) on each data record in the database repository to enforce user access permissions through a multilayered approach.

Text mining approach to predict hospital admissions using early medical records from the emergency department.

PubMed

Lucini, Filipe R; S Fogliatto, Flavio; C da Silveira, Giovani J; L Neyeloff, Jeruza; Anzanello, Michel J; de S Kuchenbecker, Ricardo; D Schaan, Beatriz

2017-04-01

Emergency department (ED) overcrowding is a serious issue for hospitals. Early information on short-term inward bed demand from patients receiving care at the ED may reduce the overcrowding problem, and optimize the use of hospital resources. In this study, we use text mining methods to process data from early ED patient records using the SOAP framework, and predict future hospitalizations and discharges. We try different approaches for pre-processing of text records and to predict hospitalization. Sets-of-words are obtained via binary representation, term frequency, and term frequency-inverse document frequency. Unigrams, bigrams and trigrams are tested for feature formation. Feature selection is based on χ 2 and F-score metrics. In the prediction module, eight text mining methods are tested: Decision Tree, Random Forest, Extremely Randomized Tree, AdaBoost, Logistic Regression, Multinomial Naïve Bayes, Support Vector Machine (Kernel linear) and Nu-Support Vector Machine (Kernel linear). Prediction performance is evaluated by F1-scores. Precision and Recall values are also informed for all text mining methods tested. Nu-Support Vector Machine was the text mining method with the best overall performance. Its average F1-score in predicting hospitalization was 77.70%, with a standard deviation (SD) of 0.66%. The method could be used to manage daily routines in EDs such as capacity planning and resource allocation. Text mining could provide valuable information and facilitate decision-making by inward bed management teams. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Assessing semantic similarity of texts - Methods and algorithms

NASA Astrophysics Data System (ADS)

Rozeva, Anna; Zerkova, Silvia

2017-12-01

Assessing the semantic similarity of texts is an important part of different text-related applications like educational systems, information retrieval, text summarization, etc. This task is performed by sophisticated analysis, which implements text-mining techniques. Text mining involves several pre-processing steps, which provide for obtaining structured representative model of the documents in a corpus by means of extracting and selecting the features, characterizing their content. Generally the model is vector-based and enables further analysis with knowledge discovery approaches. Algorithms and measures are used for assessing texts at syntactical and semantic level. An important text-mining method and similarity measure is latent semantic analysis (LSA). It provides for reducing the dimensionality of the document vector space and better capturing the text semantics. The mathematical background of LSA for deriving the meaning of the words in a given text by exploring their co-occurrence is examined. The algorithm for obtaining the vector representation of words and their corresponding latent concepts in a reduced multidimensional space as well as similarity calculation are presented.

CottonGen: a genomics, genetics and breeding database for cotton research

USDA-ARS?s Scientific Manuscript database

CottonGen (http://www.cottongen.org) is a curated and integrated web-based relational database providing access to publicly available genomic, genetic and breeding data for cotton. CottonGen supercedes CottonDB and the Cotton Marker Database, with enhanced tools for easier data sharing, mining, vis...

Web-Based Learning: Cognitive Styles and Instructional Strategies

ERIC Educational Resources Information Center

Alomyan, Hesham Raji

2016-01-01

This paper reports a study, which investigated whether different instructional strategies might interact with individual's cognitive style in learning. A web-based learning package was designed employing three strategies, Interactive Concept Maps, Illustration with Embedded Text and Text-Only. Group Embedded Figure Test was administered to 178…

Text Mining in Biomedical Domain with Emphasis on Document Clustering.

PubMed

Renganathan, Vinaitheerthan

2017-07-01

With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail. Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise.

Keynote Talk: Mining the Web 2.0 for Improved Image Search

NASA Astrophysics Data System (ADS)

Baeza-Yates, Ricardo

There are several semantic sources that can be found in the Web that are either explicit, e.g. Wikipedia, or implicit, e.g. derived from Web usage data. Most of them are related to user generated content (UGC) or what is called today the Web 2.0. In this talk we show how to use these sources of evidence in Flickr, such as tags, visual annotations or clicks, which represent the the wisdom of crowds behind UGC, to improve image search. These results are the work of the multimedia retrieval team at Yahoo! Research Barcelona and they are already being used in Yahoo! image search. This work is part of a larger effort to produce a virtuous data feedback circuit based on the right combination many different technologies to leverage the Web itself.

Ecogeochemistry of the subsurface food web at pH 0-2.5 in Iron Mountain, California, U.S.A.

USGS Publications Warehouse

Robbins, E.I.; Rodgers, T.M.; Alpers, Charles N.; Nordstrom, D. Kirk

2000-01-01

Pyrite oxidation in the underground mining environment of Iron Mountain, California, has created the most acidic pH values ever reported in aquatic systems. Sulfate values as high as 120 000 mg l-1 and iron as high as 27 600 mg l-1 have been measured in the mine water, which also carries abundant other dissolved metals including Al, Zn, Cu, Cd, Mn, Sb and Pb. Extreme acidity and high metal concentrations apparently do not preclude the presence of an underground acidophilic food web, which has developed with bacterial biomass at the base and heliozoans as top predators. Slimes, oil-like films, flexible and inflexible stalactites, sediments, water and precipitates were found to have distinctive communities. A variety of filamentous and non-filamentous bacteria grew in slimes in water having pH values < 1.0. Fungal hyphae colonize stalactites dripping pH 1.0 water; they may help to form these drip structures. Motile hypotrichous ciliates and bdelloid rotifers are particularly abundant in slimes having a pH of 1.5. Holdfasts of the iron bacterium Leptothrix discophora attach to biofilms covering pools of standing water having a pH of 2.5 in the mine. The mine is not a closed environment - people, forced air flow and massive flushing during high intensity rainfall provide intermittent contact between the surface and underground habitats, so the mine ecosystem probably is not a restricted one.

Ecogeochemistry of the subsurface food web at pH 0–2.5 in Iron Mountain, California, U.S.A.

USGS Publications Warehouse

Robbins, Eleanora I.; Rodgers , Teresa M.; Alpers, Charles N.; Nordstrom, D. Kirk

2000-01-01

Pyrite oxidation in the underground mining environment of Iron Mountain, California, has created the most acidic pH values ever reported in aquatic systems. Sulfate values as high as 120 000 mg l−1 and iron as high as 27 600 mg l−1 have been measured in the mine water, which also carries abundant other dissolved metals including Al, Zn, Cu, Cd, Mn, Sb and Pb. Extreme acidity and high metal concentrations apparently do not preclude the presence of an underground acidophilic food web, which has developed with bacterial biomass at the base and heliozoans as top predators. Slimes, oil-like films, flexible and inflexible stalactites, sediments, water and precipitates were found to have distinctive communities. A variety of filamentous and non-filamentous bacteria grew in slimes in water having pH values <1.0. Fungal hyphae colonize stalactites dripping pH 1.0 water; they may help to form these drip structures. Motile hypotrichous ciliates and bdelloid rotifers are particularly abundant in slimes having a pH of 1.5. Holdfasts of the iron bacterium Leptothrix discophora attach to biofilms covering pools of standing water having a pH of 2.5 in the mine. The mine is not a closed environment – people, forced air flow and massive flushing during high intensity rainfall provide intermittent contact between the surface and underground habitats, so the mine ecosystem probably is not a restricted one.

SU-F-P-10: A Web-Based Radiation Safety Relational Database Module for Regulatory Compliance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rosen, C; Ramsay, B; Konerth, S

Purpose: Maintaining compliance with Radioactive Materials Licenses is inherently a time-consuming task requiring focus and attention to detail. Staff tasked with these responsibilities, such as the Radiation Safety Officer and associated personnel must retain disparate records for eventual placement into one or more annual reports. Entering results and records in a relational database using a web browser as the interface, and storing that data in a cloud-based storage site, removes procedural barriers. The data becomes more adaptable for mining and sharing. Methods: Web-based code was written utilizing the web framework Django, written in Python. Additionally, the application utilizes JavaScript formore » front-end interaction, SQL, HTML and CSS. Quality assurance code testing is performed in a sequential style, and new code is only added after the successful testing of the previous goals. Separate sections of the module include data entry and analysis for audits, surveys, quality management, and continuous quality improvement. Data elements can be adapted for quarterly and annual reporting, and for immediate notification of user determined alarm settings. Results: Current advances are focusing on user interface issues, and determining the simplest manner by which to teach the user to build query forms. One solution has been to prepare library documents that a user can select or edit in place of creation a new document. Forms are being developed based upon Nuclear Regulatory Commission federal code, and will be expanded to include State Regulations. Conclusion: Establishing a secure website to act as the portal for data entry, storage and manipulation can lead to added efficiencies for a Radiation Safety Program. Access to multiple databases can lead to mining for big data programs, and for determining safety issues before they occur. Overcoming web programming challenges, a category that includes mathematical handling, is providing challenges that are being overcome.« less

Anthropogenic and natural sources of acidity and metals and their influence on the structure of stream food webs.

PubMed

Hogsden, Kristy L; Harding, Jon S

2012-03-01

We compared food web structure in 20 streams with either anthropogenic or natural sources of acidity and metals or circumneutral water chemistry in New Zealand. Community and diet analysis indicated that mining streams receiving anthropogenic inputs of acidic and metal-rich drainage had much simpler food webs (fewer species, shorter food chains, less links) than those in naturally acidic, naturally high metal, and circumneutral streams. Food webs of naturally high metal streams were structurally similar to those in mining streams, lacking fish predators and having few species. Whereas, webs in naturally acidic streams differed very little from those in circumneutral streams due to strong similarities in community composition and diets of secondary and top consumers. The combined negative effects of acidity and metals on stream food webs are clear. However, elevated metal concentrations, regardless of source, appear to play a more important role than acidity in driving food web structure. Copyright © 2011 Elsevier Ltd. All rights reserved.

Social Web mining and exploitation for serious applications: Technosocial Predictive Analytics and related technologies for public health, environmental and national security surveillance.

PubMed

Kamel Boulos, Maged N; Sanfilippo, Antonio P; Corley, Courtney D; Wheeler, Steve

2010-10-01

This paper explores Technosocial Predictive Analytics (TPA) and related methods for Web "data mining" where users' posts and queries are garnered from Social Web ("Web 2.0") tools such as blogs, micro-blogging and social networking sites to form coherent representations of real-time health events. The paper includes a brief introduction to commonly used Social Web tools such as mashups and aggregators, and maps their exponential growth as an open architecture of participation for the masses and an emerging way to gain insight about people's collective health status of whole populations. Several health related tool examples are described and demonstrated as practical means through which health professionals might create clear location specific pictures of epidemiological data such as flu outbreaks. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

Big data in multiple sclerosis: development of a web-based longitudinal study viewer in an imaging informatics-based eFolder system for complex data analysis and management

NASA Astrophysics Data System (ADS)

Ma, Kevin; Wang, Ximing; Lerner, Alex; Shiroishi, Mark; Amezcua, Lilyana; Liu, Brent

2015-03-01

In the past, we have developed and displayed a multiple sclerosis eFolder system for patient data storage, image viewing, and automatic lesion quantification results stored in DICOM-SR format. The web-based system aims to be integrated in DICOM-compliant clinical and research environments to aid clinicians in patient treatments and disease tracking. This year, we have further developed the eFolder system to handle big data analysis and data mining in today's medical imaging field. The database has been updated to allow data mining and data look-up from DICOM-SR lesion analysis contents. Longitudinal studies are tracked, and any changes in lesion volumes and brain parenchyma volumes are calculated and shown on the webbased user interface as graphical representations. Longitudinal lesion characteristic changes are compared with patients' disease history, including treatments, symptom progressions, and any other changes in the disease profile. The image viewer is updated such that imaging studies can be viewed side-by-side to allow visual comparisons. We aim to use the web-based medical imaging informatics eFolder system to demonstrate big data analysis in medical imaging, and use the analysis results to predict MS disease trends and patterns in Hispanic and Caucasian populations in our pilot study. The discovery of disease patterns among the two ethnicities is a big data analysis result that will help lead to personalized patient care and treatment planning.

Informatics in radiology (infoRAD): HTML and Web site design for the radiologist: a primer.

PubMed

Ryan, Anthony G; Louis, Luck J; Yee, William C

2005-01-01

A Web site has enormous potential as a medium for the radiologist to store, present, and share information in the form of text, images, and video clips. With a modest amount of tutoring and effort, designing a site can be as painless as preparing a Microsoft PowerPoint presentation. The site can then be used as a hub for the development of further offshoots (eg, Web-based tutorials, storage for a teaching library, publication of information about one's practice, and information gathering from a wide variety of sources). By learning the basics of hypertext markup language (HTML), the reader will be able to produce a simple and effective Web page that permits display of text, images, and multimedia files. The process of constructing a Web page can be divided into five steps: (a) creating a basic template with formatted text, (b) adding color, (c) importing images and multimedia files, (d) creating hyperlinks, and (e) uploading one's page to the Internet. This Web page may be used as the basis for a Web-based tutorial comprising text documents and image files already in one's possession. Finally, there are many commercially available packages for Web page design that require no knowledge of HTML.

Benchmarking infrastructure for mutation text mining

PubMed Central

2014-01-01

Background Experimental research on the automatic extraction of information about mutations from texts is greatly hindered by the lack of consensus evaluation infrastructure for the testing and benchmarking of mutation text mining systems. Results We propose a community-oriented annotation and benchmarking infrastructure to support development, testing, benchmarking, and comparison of mutation text mining systems. The design is based on semantic standards, where RDF is used to represent annotations, an OWL ontology provides an extensible schema for the data and SPARQL is used to compute various performance metrics, so that in many cases no programming is needed to analyze results from a text mining system. While large benchmark corpora for biological entity and relation extraction are focused mostly on genes, proteins, diseases, and species, our benchmarking infrastructure fills the gap for mutation information. The core infrastructure comprises (1) an ontology for modelling annotations, (2) SPARQL queries for computing performance metrics, and (3) a sizeable collection of manually curated documents, that can support mutation grounding and mutation impact extraction experiments. Conclusion We have developed the principal infrastructure for the benchmarking of mutation text mining tasks. The use of RDF and OWL as the representation for corpora ensures extensibility. The infrastructure is suitable for out-of-the-box use in several important scenarios and is ready, in its current state, for initial community adoption. PMID:24568600

Benchmarking infrastructure for mutation text mining.

PubMed

Klein, Artjom; Riazanov, Alexandre; Hindle, Matthew M; Baker, Christopher Jo

2014-02-25

Experimental research on the automatic extraction of information about mutations from texts is greatly hindered by the lack of consensus evaluation infrastructure for the testing and benchmarking of mutation text mining systems. We propose a community-oriented annotation and benchmarking infrastructure to support development, testing, benchmarking, and comparison of mutation text mining systems. The design is based on semantic standards, where RDF is used to represent annotations, an OWL ontology provides an extensible schema for the data and SPARQL is used to compute various performance metrics, so that in many cases no programming is needed to analyze results from a text mining system. While large benchmark corpora for biological entity and relation extraction are focused mostly on genes, proteins, diseases, and species, our benchmarking infrastructure fills the gap for mutation information. The core infrastructure comprises (1) an ontology for modelling annotations, (2) SPARQL queries for computing performance metrics, and (3) a sizeable collection of manually curated documents, that can support mutation grounding and mutation impact extraction experiments. We have developed the principal infrastructure for the benchmarking of mutation text mining tasks. The use of RDF and OWL as the representation for corpora ensures extensibility. The infrastructure is suitable for out-of-the-box use in several important scenarios and is ready, in its current state, for initial community adoption.

The utility of web mining for epidemiological research: studying the association between parity and cancer risk

PubMed Central

Yoon, Hong-Jun; Xu, Songhua; Han, Xuesong

2016-01-01

Background The World Wide Web has emerged as a powerful data source for epidemiological studies related to infectious disease surveillance. However, its potential for cancer-related epidemiological discoveries is largely unexplored. Methods Using advanced web crawling and tailored information extraction procedures, the authors automatically collected and analyzed the text content of 79 394 online obituary articles published between 1998 and 2014. The collected data included 51 911 cancer (27 330 breast; 9470 lung; 6496 pancreatic; 6342 ovarian; 2273 colon) and 27 483 non-cancer cases. With the derived information, the authors replicated a case-control study design to investigate the association between parity (i.e., childbearing) and cancer risk. Age-adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were calculated for each cancer type and compared to those reported in large-scale epidemiological studies. Results Parity was found to be associated with a significantly reduced risk of breast cancer (OR = 0.78, 95% CI, 0.75-0.82), pancreatic cancer (OR = 0.78, 95% CI, 0.72-0.83), colon cancer (OR = 0.67, 95% CI, 0.60-0.74), and ovarian cancer (OR = 0.58, 95% CI, 0.54-0.62). Marginal association was found for lung cancer risk (OR = 0.87, 95% CI, 0.81-0.92). The linear trend between increased parity and reduced cancer risk was dramatically more pronounced for breast and ovarian cancer than the other cancers included in the analysis. Conclusion This large web-mining study on parity and cancer risk produced findings very similar to those reported with traditional observational studies. It may be used as a promising strategy to generate study hypotheses for guiding and prioritizing future epidemiological studies. PMID:26615183

DTMiner: identification of potential disease targets through biomedical literature mining

PubMed Central

Xu, Dong; Zhang, Meizhuo; Xie, Yanping; Wang, Fan; Chen, Ming; Zhu, Kenny Q.; Wei, Jia

2016-01-01

Motivation: Biomedical researchers often search through massive catalogues of literature to look for potential relationships between genes and diseases. Given the rapid growth of biomedical literature, automatic relation extraction, a crucial technology in biomedical literature mining, has shown great potential to support research of gene-related diseases. Existing work in this field has produced datasets that are limited both in scale and accuracy. Results: In this study, we propose a reliable and efficient framework that takes large biomedical literature repositories as inputs, identifies credible relationships between diseases and genes, and presents possible genes related to a given disease and possible diseases related to a given gene. The framework incorporates name entity recognition (NER), which identifies occurrences of genes and diseases in texts, association detection whereby we extract and evaluate features from gene–disease pairs, and ranking algorithms that estimate how closely the pairs are related. The F1-score of the NER phase is 0.87, which is higher than existing studies. The association detection phase takes drastically less time than previous work while maintaining a comparable F1-score of 0.86. The end-to-end result achieves a 0.259 F1-score for the top 50 genes associated with a disease, which performs better than previous work. In addition, we released a web service for public use of the dataset. Availability and Implementation: The implementation of the proposed algorithms is publicly available at http://gdr-web.rwebox.com/public_html/index.php?page=download.php. The web service is available at http://gdr-web.rwebox.com/public_html/index.php. Contact: jenny.wei@astrazeneca.com or kzhu@cs.sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27506226

DTMiner: identification of potential disease targets through biomedical literature mining.

PubMed

Xu, Dong; Zhang, Meizhuo; Xie, Yanping; Wang, Fan; Chen, Ming; Zhu, Kenny Q; Wei, Jia

2016-12-01

Biomedical researchers often search through massive catalogues of literature to look for potential relationships between genes and diseases. Given the rapid growth of biomedical literature, automatic relation extraction, a crucial technology in biomedical literature mining, has shown great potential to support research of gene-related diseases. Existing work in this field has produced datasets that are limited both in scale and accuracy. In this study, we propose a reliable and efficient framework that takes large biomedical literature repositories as inputs, identifies credible relationships between diseases and genes, and presents possible genes related to a given disease and possible diseases related to a given gene. The framework incorporates name entity recognition (NER), which identifies occurrences of genes and diseases in texts, association detection whereby we extract and evaluate features from gene-disease pairs, and ranking algorithms that estimate how closely the pairs are related. The F1-score of the NER phase is 0.87, which is higher than existing studies. The association detection phase takes drastically less time than previous work while maintaining a comparable F1-score of 0.86. The end-to-end result achieves a 0.259 F1-score for the top 50 genes associated with a disease, which performs better than previous work. In addition, we released a web service for public use of the dataset. The implementation of the proposed algorithms is publicly available at http://gdr-web.rwebox.com/public_html/index.php?page=download.php The web service is available at http://gdr-web.rwebox.com/public_html/index.php CONTACT: jenny.wei@astrazeneca.com or kzhu@cs.sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Text Mining in Biomedical Domain with Emphasis on Document Clustering

PubMed Central

2017-01-01

Objectives With the exponential increase in the number of articles published every year in the biomedical domain, there is a need to build automated systems to extract unknown information from the articles published. Text mining techniques enable the extraction of unknown knowledge from unstructured documents. Methods This paper reviews text mining processes in detail and the software tools available to carry out text mining. It also reviews the roles and applications of text mining in the biomedical domain. Results Text mining processes, such as search and retrieval of documents, pre-processing of documents, natural language processing, methods for text clustering, and methods for text classification are described in detail. Conclusions Text mining techniques can facilitate the mining of vast amounts of knowledge on a given topic from published biomedical research articles and draw meaningful conclusions that are not possible otherwise. PMID:28875048

Text mining, a race against time? An attempt to quantify possible variations in text corpora of medical publications throughout the years.

PubMed

Wagner, Mathias; Vicinus, Benjamin; Muthra, Sherieda T; Richards, Tereza A; Linder, Roland; Frick, Vilma Oliveira; Groh, Andreas; Rubie, Claudia; Weichert, Frank

2016-06-01

The continuous growth of medical sciences literature indicates the need for automated text analysis. Scientific writing which is neither unitary, transcending social situation nor defined by a timeless idea is subject to constant change as it develops in response to evolving knowledge, aims at different goals, and embodies different assumptions about nature and communication. The objective of this study was to evaluate whether publication dates should be considered when performing text mining. A search of PUBMED for combined references to chemokine identifiers and particular cancer related terms was conducted to detect changes over the past 36 years. Text analyses were performed using freeware available from the World Wide Web. TOEFL Scores of territories hosting institutional affiliations as well as various readability indices were investigated. Further assessment was conducted using Principal Component Analysis. Laboratory examination was performed to evaluate the quality of attempts to extract content from the examined linguistic features. The PUBMED search yielded a total of 14,420 abstracts (3,190,219 words). The range of findings in laboratory experimentation were coherent with the variability of the results described in the analyzed body of literature. Increased concurrence of chemokine identifiers together with cancer related terms was found at the abstract and sentence level, whereas complexity of sentences remained fairly stable. The findings of the present study indicate that concurrent references to chemokines and cancer increased over time whereas text complexity remained stable. Copyright © 2016 Elsevier Ltd. All rights reserved.

Fuzzy Document Clustering Approach using WordNet Lexical Categories

NASA Astrophysics Data System (ADS)

Gharib, Tarek F.; Fouad, Mohammed M.; Aref, Mostafa M.

Text mining refers generally to the process of extracting interesting information and knowledge from unstructured text. This area is growing rapidly mainly because of the strong need for analysing the huge and large amount of textual data that reside on internal file systems and the Web. Text document clustering provides an effective navigation mechanism to organize this large amount of data by grouping their documents into a small number of meaningful classes. In this paper we proposed a fuzzy text document clustering approach using WordNet lexical categories and Fuzzy c-Means algorithm. Some experiments are performed to compare efficiency of the proposed approach with the recently reported approaches. Experimental results show that Fuzzy clustering leads to great performance results. Fuzzy c-means algorithm overcomes other classical clustering algorithms like k-means and bisecting k-means in both clustering quality and running time efficiency.

Informal Learning through Expertise Mining in the Social Web

ERIC Educational Resources Information Center

Valencia-Garcia, Rafael; Garcia-Sanchez, Francisco; Casado-Lumbreras, Cristina; Castellanos-Nieves, Dagoberto; Fernandez-Breis, Jesualdo Tomas

2012-01-01

The advent of Web 2.0, also called the Social Web, has changed the way people interact with the Web. Assisted by the technologies associated with this new trend, users now play a much more active role as content providers. This Web paradigm shift has also changed how companies operate and interact with their employees, partners and customers. The…

Web-based Visual Analytics for Extreme Scale Climate Science

DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad A; Evans, Katherine J; Harney, John F

In this paper, we introduce a Web-based visual analytics framework for democratizing advanced visualization and analysis capabilities pertinent to large-scale earth system simulations. We address significant limitations of present climate data analysis tools such as tightly coupled dependencies, ineffi- cient data movements, complex user interfaces, and static visualizations. Our Web-based visual analytics framework removes critical barriers to the widespread accessibility and adoption of advanced scientific techniques. Using distributed connections to back-end diagnostics, we minimize data movements and leverage HPC platforms. We also mitigate system dependency issues by employing a RESTful interface. Our framework embraces the visual analytics paradigm via newmore » visual navigation techniques for hierarchical parameter spaces, multi-scale representations, and interactive spatio-temporal data mining methods that retain details. Although generalizable to other science domains, the current work focuses on improving exploratory analysis of large-scale Community Land Model (CLM) and Community Atmosphere Model (CAM) simulations.« less

SalanderMaps: A rapid overview about felt earthquakes through data mining of web-accesses

NASA Astrophysics Data System (ADS)

Kradolfer, Urs

2013-04-01

While seismological observatories detect and locate earthquakes based on measurements of the ground motion, they neither know a priori whether an earthquake has been felt by the public nor is it known, where it has been felt. Such information is usually gathered by evaluating feedback reported by the public through on-line forms on the web. However, after a felt earthquake in Switzerland, many people visit the webpages of the Swiss Seismological Service (SED) at the ETH Zurich and each such visit leaves traces in the logfiles on our web-servers. Data mining techniques, applied to these logfiles and mining publicly available data bases on the internet open possibilities to obtain previously unknown information about our virtual visitors. In order to provide precise information to authorities and the media, it would be desirable to rapidly know from which locations these web-accesses origin. The method 'Salander' (Seismic Activitiy Linked to Area codes - Nimble Detection of Earthquake Rumbles) will be introduced and it will be explained, how the IP-addresses (each computer or router directly connected to the internet has a unique IP-address; an example would be 129.132.53.5) of a sufficient amount of our virtual visitors were linked to their geographical area. This allows us to unprecedentedly quickly know whether and where an earthquake was felt in Switzerland. It will also be explained, why the method Salander is superior to commercial so-called geolocation products. The corresponding products of the Salander method, animated SalanderMaps, which are routinely generated after each earthquake with a magnitude of M>2 in Switzerland (http://www.seismo.ethz.ch/prod/salandermaps/, available after March 2013), demonstrate how the wavefield of earthquakes propagates through Switzerland and where it was felt. Often, such information is available within less than 60 seconds after origin time, and we always get a clear picture within already five minutes after origin time. Furthermore, the method allows to detect earthquakes solely on the analysis of accesses to our web-servers. Analyzing more than 170 million web-accesses since 2003, all seismic events within or near Switzerland with magnitudes M>4 and most felt events with magnitudes between 3 and 4 were detected. The current system is very robust, as we only had one false alarm while re-processing the web-access logfiles of the past almost 10 years. We anticipate that this method will produce even faster results in the future as the number of both commercial and private internet users is - according to the statistics of our logfiles - still increasing.

Gene Prioritization of Resistant Rice Gene against Xanthomas oryzae pv. oryzae by Using Text Mining Technologies

PubMed Central

Xia, Jingbo; Zhang, Xing; Yuan, Daojun; Chen, Lingling; Webster, Jonathan; Fang, Alex Chengyu

2013-01-01

To effectively assess the possibility of the unknown rice protein resistant to Xanthomonas oryzae pv. oryzae, a hybrid strategy is proposed to enhance gene prioritization by combining text mining technologies with a sequence-based approach. The text mining technique of term frequency inverse document frequency is used to measure the importance of distinguished terms which reflect biomedical activity in rice before candidate genes are screened and vital terms are produced. Afterwards, a built-in classifier under the chaos games representation algorithm is used to sieve the best possible candidate gene. Our experiment results show that the combination of these two methods achieves enhanced gene prioritization. PMID:24371834

Gene prioritization of resistant rice gene against Xanthomas oryzae pv. oryzae by using text mining technologies.

PubMed

Xia, Jingbo; Zhang, Xing; Yuan, Daojun; Chen, Lingling; Webster, Jonathan; Fang, Alex Chengyu

2013-01-01

To effectively assess the possibility of the unknown rice protein resistant to Xanthomonas oryzae pv. oryzae, a hybrid strategy is proposed to enhance gene prioritization by combining text mining technologies with a sequence-based approach. The text mining technique of term frequency inverse document frequency is used to measure the importance of distinguished terms which reflect biomedical activity in rice before candidate genes are screened and vital terms are produced. Afterwards, a built-in classifier under the chaos games representation algorithm is used to sieve the best possible candidate gene. Our experiment results show that the combination of these two methods achieves enhanced gene prioritization.

Automatically classifying sentences in full-text biomedical articles into Introduction, Methods, Results and Discussion.

PubMed

Agarwal, Shashank; Yu, Hong

2009-12-01

Biomedical texts can be typically represented by four rhetorical categories: Introduction, Methods, Results and Discussion (IMRAD). Classifying sentences into these categories can benefit many other text-mining tasks. Although many studies have applied different approaches for automatically classifying sentences in MEDLINE abstracts into the IMRAD categories, few have explored the classification of sentences that appear in full-text biomedical articles. We first evaluated whether sentences in full-text biomedical articles could be reliably annotated into the IMRAD format and then explored different approaches for automatically classifying these sentences into the IMRAD categories. Our results show an overall annotation agreement of 82.14% with a Kappa score of 0.756. The best classification system is a multinomial naïve Bayes classifier trained on manually annotated data that achieved 91.95% accuracy and an average F-score of 91.55%, which is significantly higher than baseline systems. A web version of this system is available online at-http://wood.ims.uwm.edu/full_text_classifier/.

Mining Specific and General Features in Both Positive and Negative Relevance Feedback. QUT E-Discovery Lab at the TREC󈧍 Relevance Feedback Track

DTIC Science & Technology

2009-11-01

relevance feedback algo- rithm. Four methods, εMap [1], MapA , P10A, and StatAP [2], were used in the track to measure the performance of Phase 2 runs...εMap and StatAP were applied to the runs us- ing the testing set of only ClueWeb09 Category-B, whereas MapA and P10A were applied to those using the...whole ClueWeb09 English set. Because our experiments were based on only ClueWeb09 Category-B, measuring our per- formance by MapA and P10A might not

Data-Rich Astronomy: Mining Sky Surveys with PhotoRApToR

NASA Astrophysics Data System (ADS)

Cavuoti, Stefano; Brescia, Massimo; Longo, Giuseppe

2014-05-01

In the last decade a new generation of telescopes and sensors has allowed the production of a very large amount of data and astronomy has become a data-rich science. New automatic methods largely based on machine learning are needed to cope with such data tsunami. We present some results in the fields of photometric redshifts and galaxy classification, obtained using the MLPQNA algorithm available in the DAMEWARE (Data Mining and Web Application Resource) for the SDSS galaxies (DR9 and DR10). We present PhotoRApToR (Photometric Research Application To Redshift): a Java based desktop application capable to solve regression and classification problems and specialized for photo-z estimation.

Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges

PubMed Central

Singhal, Ayush; Leaman, Robert; Catlett, Natalie; Lemberger, Thomas; McEntyre, Johanna; Polson, Shawn; Xenarios, Ioannis; Arighi, Cecilia; Lu, Zhiyong

2016-01-01

Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system ‘accuracy’ remains a challenge and identify several additional common difficulties and potential research directions including (i) the ‘scalability’ issue due to the increasing need of mining information from millions of full-text articles, (ii) the ‘interoperability’ issue of integrating various text-mining systems into existing curation workflows and (iii) the ‘reusability’ issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. Finally, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators. PMID:28025348

Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges

DOE PAGES

Singhal, Ayush; Leaman, Robert; Catlett, Natalie; ...

2016-12-26

Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system ‘accuracy’ remains a challenge and identify several additional common difficulties and potential research directions including (i) the ‘scalability’ issue due to themore » increasing need of mining information from millions of full-text articles, (ii) the ‘interoperability’ issue of integrating various text-mining systems into existing curation workflows and (iii) the ‘reusability’ issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. In conclusion, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators.« less

Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges

DOE Office of Scientific and Technical Information (OSTI.GOV)

Singhal, Ayush; Leaman, Robert; Catlett, Natalie

Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system ‘accuracy’ remains a challenge and identify several additional common difficulties and potential research directions including (i) the ‘scalability’ issue due to themore » increasing need of mining information from millions of full-text articles, (ii) the ‘interoperability’ issue of integrating various text-mining systems into existing curation workflows and (iii) the ‘reusability’ issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. In conclusion, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators.« less

Upper Animas Mining District

EPA Pesticide Factsheets

Web page provides narrative of What's New?, Site Description, Site Risk, Cleanup Progress, Community Involvement, Next Steps, Site Documents, FAQ, Contacts and LInks for the Upper Animas Mining District site in San Juan County, Colorado.

A New Framework for Textual Information Mining over Parse Trees. CRESST Report 805

ERIC Educational Resources Information Center

Mousavi, Hamid; Kerr, Deirdre; Iseli, Markus R.

2011-01-01

Textual information mining is a challenging problem that has resulted in the creation of many different rule-based linguistic query languages. However, these languages generally are not optimized for the purpose of text mining. In other words, they usually consider queries as individuals and only return raw results for each query. Moreover they…

Cardiac catheterization laboratory inpatient forecast tool: a prospective evaluation

PubMed Central

Flanagan, Eleni; Siddiqui, Sauleh; Appelbaum, Jeff; Kasper, Edward K; Levin, Scott

2016-01-01

Objective To develop and prospectively evaluate a web-based tool that forecasts the daily bed need for admissions from the cardiac catheterization laboratory using routinely available clinical data within electronic medical records (EMRs). Methods The forecast model was derived using a 13-month retrospective cohort of 6384 catheterization patients. Predictor variables such as demographics, scheduled procedures, and clinical indicators mined from free-text notes were input to a multivariable logistic regression model that predicted the probability of inpatient admission. The model was embedded into a web-based application connected to the local EMR system and used to support bed management decisions. After implementation, the tool was prospectively evaluated for accuracy on a 13-month test cohort of 7029 catheterization patients. Results The forecast model predicted admission with an area under the receiver operating characteristic curve of 0.722. Daily aggregate forecasts were accurate to within one bed for 70.3% of days and within three beds for 97.5% of days during the prospective evaluation period. The web-based application housing the forecast model was used by cardiology providers in practice to estimate daily admissions from the catheterization laboratory. Discussion The forecast model identified older age, male gender, invasive procedures, coronary artery bypass grafts, and a history of congestive heart failure as qualities indicating a patient was at increased risk for admission. Diagnostic procedures and less acute clinical indicators decreased patients’ risk of admission. Despite the site-specific limitations of the model, these findings were supported by the literature. Conclusion Data-driven predictive analytics may be used to accurately forecast daily demand for inpatient beds for cardiac catheterization patients. Connecting these analytics to EMR data sources has the potential to provide advanced operational decision support. PMID:26342217

DNorm: disease name normalization with pairwise learning to rank.

PubMed

Leaman, Robert; Islamaj Dogan, Rezarta; Lu, Zhiyong

2013-11-15

Despite the central role of diseases in biomedical research, there have been much fewer attempts to automatically determine which diseases are mentioned in a text-the task of disease name normalization (DNorm)-compared with other normalization tasks in biomedical text mining research. In this article we introduce the first machine learning approach for DNorm, using the NCBI disease corpus and the MEDIC vocabulary, which combines MeSH® and OMIM. Our method is a high-performing and mathematically principled framework for learning similarities between mentions and concept names directly from training data. The technique is based on pairwise learning to rank, which has not previously been applied to the normalization task but has proven successful in large optimization problems for information retrieval. We compare our method with several techniques based on lexical normalization and matching, MetaMap and Lucene. Our algorithm achieves 0.782 micro-averaged F-measure and 0.809 macro-averaged F-measure, an increase over the highest performing baseline method of 0.121 and 0.098, respectively. The source code for DNorm is available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/DNorm, along with a web-based demonstration and links to the NCBI disease corpus. Results on PubMed abstracts are available in PubTator: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator .

Prospecting for New Collaborations: Mining Syllabi for Library Service Opportunities

ERIC Educational Resources Information Center

Williams, Lisa M.; Cody, Sue Ann; Parnell, Jerry

2004-01-01

Online course syllabi provide a convenient source of information about library use. This article discusses the strategies used to retrieve syllabi, analyze library use, and develop new opportunities to collaborate with faculty. A new Web-based service was developed to pull course- and library-related materials into a convenient package.

A Method of Cross-Level Frequent Pattern Mining for Web-Based Instruction

ERIC Educational Resources Information Center

Huang, Yueh-Min; Chen, Juei-Nan; Cheng, Shu-Chen

2007-01-01

Due to the rise of e-Learning, more and more useful learning materials are open to public access. Therefore, an appropriate learning suggestion mechanism is an important tool to enable learners to work more efficiently. A smoother learning process increases the learning effect, avoiding unnecessarily difficult concepts and disorientation during…

Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two-mode affiliation network

NASA Astrophysics Data System (ADS)

Li, Huajiao; An, Haizhong; Wang, Yue; Huang, Jiachen; Gao, Xiangyun

2016-05-01

Keeping abreast of trends in the articles and rapidly grasping a body of article's key points and relationship from a holistic perspective is a new challenge in both literature research and text mining. As the important component, keywords can present the core idea of the academic article. Usually, articles on a single theme or area could share one or some same keywords, and we can analyze topological features and evolution of the articles co-keyword networks and keywords co-occurrence networks to realize the in-depth analysis of the articles. This paper seeks to integrate statistics, text mining, complex networks and visualization to analyze all of the academic articles on one given theme, complex network(s). All 5944 ;complex networks; articles that were published between 1990 and 2013 and are available on the Web of Science are extracted. Based on the two-mode affiliation network theory, a new frontier of complex networks, we constructed two different networks, one taking the articles as nodes, the co-keyword relationships as edges and the quantity of co-keywords as the weight to construct articles co-keyword network, and another taking the articles' keywords as nodes, the co-occurrence relationships as edges and the quantity of simultaneous co-occurrences as the weight to construct keyword co-occurrence network. An integrated method for analyzing the topological features and evolution of the articles co-keyword network and keywords co-occurrence networks is proposed, and we also defined a new function to measure the innovation coefficient of the articles in annual level. This paper provides a useful tool and process for successfully achieving in-depth analysis and rapid understanding of the trends and relationships of articles in a holistic perspective.

Data Mining of Extremely Large Ad-Hoc Data Sets to Produce Reverse Web-Link Graphs

DTIC Science & Technology

2017-03-01

in most of the MR cases. From these studies , we also learned that computing -optimized instances should be chosen for serialized/compressed input data...maximum 200 words) Data mining can be a valuable tool, particularly in the acquisition of military intelligence. As the second study within a larger Naval...open web crawler data set Common Crawl. Similar to previous studies , this research employs MapReduce (MR) for sorting and categorizing output value

Thai Language Sentence Similarity Computation Based on Syntactic Structure and Semantic Vector

NASA Astrophysics Data System (ADS)

Wang, Hongbin; Feng, Yinhan; Cheng, Liang

2018-03-01

Sentence similarity computation plays an increasingly important role in text mining, Web page retrieval, machine translation, speech recognition and question answering systems. Thai language as a kind of resources scarce language, it is not like Chinese language with HowNet and CiLin resources. So the Thai sentence similarity research faces some challenges. In order to solve this problem of the Thai language sentence similarity computation. This paper proposes a novel method to compute the similarity of Thai language sentence based on syntactic structure and semantic vector. This method firstly uses the Part-of-Speech (POS) dependency to calculate two sentences syntactic structure similarity, and then through the word vector to calculate two sentences semantic similarity. Finally, we combine the two methods to calculate two Thai language sentences similarity. The proposed method not only considers semantic, but also considers the sentence syntactic structure. The experiment result shows that this method in Thai language sentence similarity computation is feasible.

Ion Channel ElectroPhysiology Ontology (ICEPO) - a case study of text mining assisted ontology development.

PubMed

Elayavilli, Ravikumar Komandur; Liu, Hongfang

2016-01-01

Computational modeling of biological cascades is of great interest to quantitative biologists. Biomedical text has been a rich source for quantitative information. Gathering quantitative parameters and values from biomedical text is one significant challenge in the early steps of computational modeling as it involves huge manual effort. While automatically extracting such quantitative information from bio-medical text may offer some relief, lack of ontological representation for a subdomain serves as impedance in normalizing textual extractions to a standard representation. This may render textual extractions less meaningful to the domain experts. In this work, we propose a rule-based approach to automatically extract relations involving quantitative data from biomedical text describing ion channel electrophysiology. We further translated the quantitative assertions extracted through text mining to a formal representation that may help in constructing ontology for ion channel events using a rule based approach. We have developed Ion Channel ElectroPhysiology Ontology (ICEPO) by integrating the information represented in closely related ontologies such as, Cell Physiology Ontology (CPO), and Cardiac Electro Physiology Ontology (CPEO) and the knowledge provided by domain experts. The rule-based system achieved an overall F-measure of 68.93% in extracting the quantitative data assertions system on an independently annotated blind data set. We further made an initial attempt in formalizing the quantitative data assertions extracted from the biomedical text into a formal representation that offers potential to facilitate the integration of text mining into ontological workflow, a novel aspect of this study. This work is a case study where we created a platform that provides formal interaction between ontology development and text mining. We have achieved partial success in extracting quantitative assertions from the biomedical text and formalizing them in ontological framework. The ICEPO ontology is available for download at http://openbionlp.org/mutd/supplementarydata/ICEPO/ICEPO.owl.

Analyzing and Predicting User Participations in Online Health Communities: A Social Support Perspective.

PubMed

Wang, Xi; Zhao, Kang; Street, Nick

2017-04-24

Online health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with similar peers to seek, receive, and provide different types of social support, such as informational support, emotional support, and companionship. As active participations in an OHC are beneficial to both the OHC and its users, it is important to understand factors related to users' participations and predict user churn for user retention efforts. This study aimed to analyze OHC users' Web-based interactions, reveal which types of social support activities are related to users' participation, and predict whether and when a user will churn from the OHC. We collected a large-scale dataset from a popular OHC for cancer survivors. We used text mining techniques to decide what kinds of social support each post contained. We illustrated how we built text classifiers for 5 different social support categories: seeking informational support (SIS), providing informational support (PIS), seeking emotional support (SES), providing emotional support (PES), and companionship (COM). We conducted survival analysis to identify types of social support related to users' continued participation. Using supervised machine learning methods, we developed a predictive model for user churn. Users' behaviors to PIS, SES, and COM had hazard ratios significantly lower than 1 (0.948, 0.972, and 0.919, respectively) and were indicative of continued participations in the OHC. The churn prediction model based on social support activities offers accurate predictions on whether and when a user will leave the OHC. Detecting different types of social support activities via text mining contributes to better understanding and prediction of users' participations in an OHC. The outcome of this study can help the management and design of a sustainable OHC via more proactive and effective user retention strategies. ©Xi Wang, Kang Zhao, Nick Street. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.04.2017.

Analyzing and Predicting User Participations in Online Health Communities: A Social Support Perspective

PubMed Central

Wang, Xi; Street, Nick

2017-01-01

Background Online health communities (OHCs) have become a major source of social support for people with health problems. Members of OHCs interact online with similar peers to seek, receive, and provide different types of social support, such as informational support, emotional support, and companionship. As active participations in an OHC are beneficial to both the OHC and its users, it is important to understand factors related to users’ participations and predict user churn for user retention efforts. Objective This study aimed to analyze OHC users’ Web-based interactions, reveal which types of social support activities are related to users’ participation, and predict whether and when a user will churn from the OHC. Methods We collected a large-scale dataset from a popular OHC for cancer survivors. We used text mining techniques to decide what kinds of social support each post contained. We illustrated how we built text classifiers for 5 different social support categories: seeking informational support (SIS), providing informational support (PIS), seeking emotional support (SES), providing emotional support (PES), and companionship (COM). We conducted survival analysis to identify types of social support related to users’ continued participation. Using supervised machine learning methods, we developed a predictive model for user churn. Results Users’ behaviors to PIS, SES, and COM had hazard ratios significantly lower than 1 (0.948, 0.972, and 0.919, respectively) and were indicative of continued participations in the OHC. The churn prediction model based on social support activities offers accurate predictions on whether and when a user will leave the OHC. Conclusions Detecting different types of social support activities via text mining contributes to better understanding and prediction of users’ participations in an OHC. The outcome of this study can help the management and design of a sustainable OHC via more proactive and effective user retention strategies. PMID:28438725

Interaction mining and skill-dependent recommendations for multi-objective team composition

PubMed Central

Dorn, Christoph; Skopik, Florian; Schall, Daniel; Dustdar, Schahram

2011-01-01

Web-based collaboration and virtual environments supported by various Web 2.0 concepts enable the application of numerous monitoring, mining and analysis tools to study human interactions and team formation processes. The composition of an effective team requires a balance between adequate skill fulfillment and sufficient team connectivity. The underlying interaction structure reflects social behavior and relations of individuals and determines to a large degree how well people can be expected to collaborate. In this paper we address an extended team formation problem that does not only require direct interactions to determine team connectivity but additionally uses implicit recommendations of collaboration partners to support even sparsely connected networks. We provide two heuristics based on Genetic Algorithms and Simulated Annealing for discovering efficient team configurations that yield the best trade-off between skill coverage and team connectivity. Our self-adjusting mechanism aims to discover the best combination of direct interactions and recommendations when deriving connectivity. We evaluate our approach based on multiple configurations of a simulated collaboration network that features close resemblance to real world expert networks. We demonstrate that our algorithm successfully identifies efficient team configurations even when removing up to 40% of experts from various social network configurations. PMID:22298939

Beyond Google: The Invisible Web in the Academic Library

ERIC Educational Resources Information Center

Devine, Jane; Egger-Sider, Francine

2004-01-01

This article analyzes the concept of the Invisible Web and its implication for academic librarianship. It offers a guide to tools that can be used to mine the Invisible Web and discusses the benefits of using the Invisible Web to promote interest in library services. In addition, the article includes an expanded definition, a literature review,…

Web-based health services and clinical decision support.

PubMed

Jegelevicius, Darius; Marozas, Vaidotas; Lukosevicius, Arunas; Patasius, Martynas

2004-01-01

The purpose of this study was the development of a Web-based e-health service for comprehensive assistance and clinical decision support. The service structure consists of a Web server, a PHP-based Web interface linked to a clinical SQL database, Java applets for interactive manipulation and visualization of signals and a Matlab server linked with signal and data processing algorithms implemented by Matlab programs. The service ensures diagnostic signal- and image analysis-sbased clinical decision support. By using the discussed methodology, a pilot service for pathology specialists for automatic calculation of the proliferation index has been developed. Physicians use a simple Web interface for uploading the pictures under investigation to the server; subsequently a Java applet interface is used for outlining the region of interest and, after processing on the server, the requested proliferation index value is calculated. There is also an "expert corner", where experts can submit their index estimates and comments on particular images, which is especially important for system developers. These expert evaluations are used for optimization and verification of automatic analysis algorithms. Decision support trials have been conducted for ECG and ophthalmology ultrasonic investigations of intraocular tumor differentiation. Data mining algorithms have been applied and decision support trees constructed. These services are under implementation by a Web-based system too. The study has shown that the Web-based structure ensures more effective, flexible and accessible services compared with standalone programs and is very convenient for biomedical engineers and physicians, especially in the development phase.

Intelligent Information Retrieval and Web Mining Architecture Using SOA

ERIC Educational Resources Information Center

El-Bathy, Naser Ibrahim

2010-01-01

The study of this dissertation provides a solution to a very specific problem instance in the area of data mining, data warehousing, and service-oriented architecture in publishing and newspaper industries. The research question focuses on the integration of data mining and data warehousing. The research problem focuses on the development of…

Cadmium Accumulation in Periphyton from an Abandoned Mining District in the Buffalo National River, Arkansas.

PubMed

McCauley, Jacob R; Bouldin, Jennifer L

2016-06-01

The Rush Mining District along the Buffalo River in Arkansas has a significant history of zinc and lead mining operations. The tails and spoils of these operations deposit heavy amounts of raw ore into streams. One element commonly found in the earth's crust that becomes a minor constituent of the deposition is cadmium. Periphyton samples from Rush Creek and Clabber Creek, two creeks within the Rush Mining District were measured for cadmium as well as two creeks with no history of mining, Spring Creek and Water Creek. Periphyton samples from Rush and Clabber Creek contained mean cadmium concentrations of 436.6 ± 67.3 and 93.38 ± 8.67 µg/kg, respectively. Spring Creek and Water Creek had a mean cadmium concentration of 40.49 ± 3.40 and 41.78 ± 3.99 µg/kg within periphyton. The results indicate increased metal concentrations in algal communities from mined areas. As periphyton is the base of the aquatic food chain, it acts as a conduit for movement of cadmium in the food web.

@Note: a workbench for biomedical text mining.

PubMed

Lourenço, Anália; Carreira, Rafael; Carneiro, Sónia; Maia, Paulo; Glez-Peña, Daniel; Fdez-Riverola, Florentino; Ferreira, Eugénio C; Rocha, Isabel; Rocha, Miguel

2009-08-01

Biomedical Text Mining (BioTM) is providing valuable approaches to the automated curation of scientific literature. However, most efforts have addressed the benchmarking of new algorithms rather than user operational needs. Bridging the gap between BioTM researchers and biologists' needs is crucial to solve real-world problems and promote further research. We present @Note, a platform for BioTM that aims at the effective translation of the advances between three distinct classes of users: biologists, text miners and software developers. Its main functional contributions are the ability to process abstracts and full-texts; an information retrieval module enabling PubMed search and journal crawling; a pre-processing module with PDF-to-text conversion, tokenisation and stopword removal; a semantic annotation schema; a lexicon-based annotator; a user-friendly annotation view that allows to correct annotations and a Text Mining Module supporting dataset preparation and algorithm evaluation. @Note improves the interoperability, modularity and flexibility when integrating in-home and open-source third-party components. Its component-based architecture allows the rapid development of new applications, emphasizing the principles of transparency and simplicity of use. Although it is still on-going, it has already allowed the development of applications that are currently being used.

Literature-based discovery of diabetes- and ROS-related targets

PubMed Central

2010-01-01

Background Reactive oxygen species (ROS) are known mediators of cellular damage in multiple diseases including diabetic complications. Despite its importance, no comprehensive database is currently available for the genes associated with ROS. Methods We present ROS- and diabetes-related targets (genes/proteins) collected from the biomedical literature through a text mining technology. A web-based literature mining tool, SciMiner, was applied to 1,154 biomedical papers indexed with diabetes and ROS by PubMed to identify relevant targets. Over-represented targets in the ROS-diabetes literature were obtained through comparisons against randomly selected literature. The expression levels of nine genes, selected from the top ranked ROS-diabetes set, were measured in the dorsal root ganglia (DRG) of diabetic and non-diabetic DBA/2J mice in order to evaluate the biological relevance of literature-derived targets in the pathogenesis of diabetic neuropathy. Results SciMiner identified 1,026 ROS- and diabetes-related targets from the 1,154 biomedical papers (http://jdrf.neurology.med.umich.edu/ROSDiabetes/). Fifty-three targets were significantly over-represented in the ROS-diabetes literature compared to randomly selected literature. These over-represented targets included well-known members of the oxidative stress response including catalase, the NADPH oxidase family, and the superoxide dismutase family of proteins. Eight of the nine selected genes exhibited significant differential expression between diabetic and non-diabetic mice. For six genes, the direction of expression change in diabetes paralleled enhanced oxidative stress in the DRG. Conclusions Literature mining compiled ROS-diabetes related targets from the biomedical literature and led us to evaluate the biological relevance of selected targets in the pathogenesis of diabetic neuropathy. PMID:20979611

Design and Implementation WebGIS for Improving the Quality of Exploration Decisions at Sin-Quyen Copper Mine, Northern Vietnam

NASA Astrophysics Data System (ADS)

Quang Truong, Xuan; Luan Truong, Xuan; Nguyen, Tuan Anh; Nguyen, Dinh Tuan; Cong Nguyen, Chi

2017-12-01

The objective of this study is to design and implement a WebGIS Decision Support System (WDSS) for reducing uncertainty and supporting to improve the quality of exploration decisions in the Sin-Quyen copper mine, northern Vietnam. The main distinctive feature of the Sin-Quyen deposit is an unusual composition of ores. Computer and software applied to the exploration problem have had a significant impact on the exploration process over the past 25 years, but up until now, no online system has been undertaken. The system was completely built on open source technology and the Open Geospatial Consortium Web Services (OWS). The input data includes remote sensing (RS), Geographical Information System (GIS) and data from drillhole explorations, the drillhole exploration data sets were designed as a geodatabase and stored in PostgreSQL. The WDSS must be able to processed exploration data and support users to access 2-dimensional (2D) or 3-dimensional (3D) cross-sections and map of boreholles exploration data and drill holes. The interface was designed in order to interact with based maps (e.g., Digital Elevation Model, Google Map, OpenStreetMap) and thematic maps (e.g., land use and land cover, administrative map, drillholes exploration map), and to provide GIS functions (such as creating a new map, updating an existing map, querying and statistical charts). In addition, the system provides geological cross-sections of ore bodies based on Inverse Distance Weighting (IDW), nearest neighbour interpolation and Kriging methods (e.g., Simple Kriging, Ordinary Kriging, Indicator Kriging and CoKriging). The results based on data available indicate that the best estimation method (of 23 borehole exploration data sets) for estimating geological cross-sections of ore bodies in Sin-Quyen copper mine is Ordinary Kriging. The WDSS could provide useful information to improve drilling efficiency in mineral exploration and for management decision making.

A Framework of Knowledge Integration and Discovery for Supporting Pharmacogenomics Target Predication of Adverse Drug Events: A Case Study of Drug-Induced Long QT Syndrome.

PubMed

Jiang, Guoqian; Wang, Chen; Zhu, Qian; Chute, Christopher G

2013-01-01

Knowledge-driven text mining is becoming an important research area for identifying pharmacogenomics target genes. However, few of such studies have been focused on the pharmacogenomics targets of adverse drug events (ADEs). The objective of the present study is to build a framework of knowledge integration and discovery that aims to support pharmacogenomics target predication of ADEs. We integrate a semantically annotated literature corpus Semantic MEDLINE with a semantically coded ADE knowledgebase known as ADEpedia using a semantic web based framework. We developed a knowledge discovery approach combining a network analysis of a protein-protein interaction (PPI) network and a gene functional classification approach. We performed a case study of drug-induced long QT syndrome for demonstrating the usefulness of the framework in predicting potential pharmacogenomics targets of ADEs.

Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges.

PubMed

Singhal, Ayush; Leaman, Robert; Catlett, Natalie; Lemberger, Thomas; McEntyre, Johanna; Polson, Shawn; Xenarios, Ioannis; Arighi, Cecilia; Lu, Zhiyong

2016-01-01

Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system 'accuracy' remains a challenge and identify several additional common difficulties and potential research directions including (i) the 'scalability' issue due to the increasing need of mining information from millions of full-text articles, (ii) the 'interoperability' issue of integrating various text-mining systems into existing curation workflows and (iii) the 'reusability' issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. Finally, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.

[Distribution characteristics of soil nematodes in reclaimed land of copper-mine-tailings in different plant associations].

PubMed

Zhu, Yong-heng; Li, Ke-zhong; Zhang, Heng; Han, Fei; Zhou, Ju-hua; Gao, Ting-ting

2015-02-01

A survey was carried out to investigate soil nematode communities in the plant associations of gramineae (Arthraxon lanceolatus, AL; Imperata cylindrica, IC) and leguminosae (Glycine soja, GS) in reclaimed land of copper-mine-tailings and in the plant associations of gramineae (Digitaria chrysoblephara, DC-CK) of peripheral control in Fenghuang Mountain, Tongling City. A total of 1277 nematodes were extracted and sorted into 51 genera. The average individual density of the nematodes was 590 individuals · 100 g(-1) dry soil. In order to analyze the distribution character- istics of soil nematode communities in reclaimed land of copper-mine-tailings, Shannon community diversity index and soil food web structure indices were applied in the research. The results showed that the total number of nematode genus and the Shannon community diversity index of soil nematode in the three plant associations of AL, IC and GS were less than that in the plant associations of DC-CK. Compared with the ecological indices of soil nematode communities among the different plant associations in reclaimed land of copper-mine-tailings and peripheral natural habitat, we found that the structure of soil food web in the plant associations of GS was more mature, with bacterial decomposition being dominant in the soil organic matter decomposition, and that the soil ecosystem in the plant associations of GS was not stable with low interference. This indicated that the soil food web in the plant associations of leguminosae had a greater development potential to improve the ecological stability of the reclaimed land of copper-mine-tailings. On the other hand, the structure of soil food web in the plant associations of AL and IC were relatively stable in a structured state with fungal decomposition being dominant in the decomposition of soil organic matter. This indicated that the soil food web in the plant associations of gramineae was at a poor development level.

Neural networks for data mining electronic text collections

NASA Astrophysics Data System (ADS)

Walker, Nicholas; Truman, Gregory

1997-04-01

The use of neural networks in information retrieval and text analysis has primarily suffered from the issues of adequate document representation, the ability to scale to very large collections, dynamism in the face of new information and the practical difficulties of basing the design on the use of supervised training sets. Perhaps the most important approach to begin solving these problems is the use of `intermediate entities' which reduce the dimensionality of document representations and the size of documents collections to manageable levels coupled with the use of unsupervised neural network paradigms. This paper describes the issues, a fully configured neural network-based text analysis system--dataHARVEST--aimed at data mining text collections which begins this process, along with the remaining difficulties and potential ways forward.

Relax with CouchDB - Into the non-relational DBMS era of Bioinformatics

PubMed Central

Manyam, Ganiraju; Payton, Michelle A.; Roth, Jack A.; Abruzzo, Lynne V.; Coombes, Kevin R.

2012-01-01

With the proliferation of high-throughput technologies, genome-level data analysis has become common in molecular biology. Bioinformaticians are developing extensive resources to annotate and mine biological features from high-throughput data. The underlying database management systems for most bioinformatics software are based on a relational model. Modern non-relational databases offer an alternative that has flexibility, scalability, and a non-rigid design schema. Moreover, with an accelerated development pace, non-relational databases like CouchDB can be ideal tools to construct bioinformatics utilities. We describe CouchDB by presenting three new bioinformatics resources: (a) geneSmash, which collates data from bioinformatics resources and provides automated gene-centric annotations, (b) drugBase, a database of drug-target interactions with a web interface powered by geneSmash, and (c) HapMap-CN, which provides a web interface to query copy number variations from three SNP-chip HapMap datasets. In addition to the web sites, all three systems can be accessed programmatically via web services. PMID:22609849

Text Mining.

ERIC Educational Resources Information Center

Trybula, Walter J.

1999-01-01

Reviews the state of research in text mining, focusing on newer developments. The intent is to describe the disparate investigations currently included under the term text mining and provide a cohesive structure for these efforts. A summary of research identifies key organizations responsible for pushing the development of text mining. A section…

An Enhanced Text-Mining Framework for Extracting Disaster Relevant Data through Social Media and Remote Sensing Data Fusion

NASA Astrophysics Data System (ADS)

Scheele, C. J.; Huang, Q.

2016-12-01

In the past decade, the rise in social media has led to the development of a vast number of social media services and applications. Disaster management represents one of such applications leveraging massive data generated for event detection, response, and recovery. In order to find disaster relevant social media data, current approaches utilize natural language processing (NLP) methods based on keywords, or machine learning algorithms relying on text only. However, these approaches cannot be perfectly accurate due to the variability and uncertainty in language used on social media. To improve current methods, the enhanced text-mining framework is proposed to incorporate location information from social media and authoritative remote sensing datasets for detecting disaster relevant social media posts, which are determined by assessing the textual content using common text mining methods and how the post relates spatiotemporally to the disaster event. To assess the framework, geo-tagged Tweets were collected for three different spatial and temporal disaster events: hurricane, flood, and tornado. Remote sensing data and products for each event were then collected using RealEarthTM. Both Naive Bayes and Logistic Regression classifiers were used to compare the accuracy within the enhanced text-mining framework. Finally, the accuracies from the enhanced text-mining framework were compared to the current text-only methods for each of the case study disaster events. The results from this study address the need for more authoritative data when using social media in disaster management applications.

The SADI Personal Health Lens: A Web Browser-Based System for Identifying Personally Relevant Drug Interactions.

PubMed

Vandervalk, Ben; McCarthy, E Luke; Cruz-Toledo, José; Klein, Artjom; Baker, Christopher J O; Dumontier, Michel; Wilkinson, Mark D

2013-04-05

The Web provides widespread access to vast quantities of health-related information that can improve quality-of-life through better understanding of personal symptoms, medical conditions, and available treatments. Unfortunately, identifying a credible and personally relevant subset of information can be a time-consuming and challenging task for users without a medical background. The objective of the Personal Health Lens system is to aid users when reading health-related webpages by providing warnings about personally relevant drug interactions. More broadly, we wish to present a prototype for a novel, generalizable approach to facilitating interactions between a patient, their practitioner(s), and the Web. We utilized a distributed, Semantic Web-based architecture for recognizing personally dangerous drugs consisting of: (1) a private, local triple store of personal health information, (2) Semantic Web services, following the Semantic Automated Discovery and Integration (SADI) design pattern, for text mining and identifying substance interactions, (3) a bookmarklet to trigger analysis of a webpage and annotate it with personalized warnings, and (4) a semantic query that acts as an abstract template of the analytical workflow to be enacted by the system. A prototype implementation of the system is provided in the form of a Java standalone executable JAR file. The JAR file bundles all components of the system: the personal health database, locally-running versions of the SADI services, and a javascript bookmarklet that triggers analysis of a webpage. In addition, the demonstration includes a hypothetical personal health profile, allowing the system to be used immediately without configuration. Usage instructions are provided. The main strength of the Personal Health Lens system is its ability to organize medical information and to present it to the user in a personalized and contextually relevant manner. While this prototype was limited to a single knowledge domain (drug/drug interactions), the proposed architecture is generalizable, and could act as the foundation for much richer personalized-health-Web clients, while importantly providing a novel and personalizable mechanism for clinical experts to inject their expertise into the browsing experience of their patients in the form of customized semantic queries and ontologies.

The SADI Personal Health Lens: A Web Browser-Based System for Identifying Personally Relevant Drug Interactions

PubMed Central

Vandervalk, Ben; McCarthy, E Luke; Cruz-Toledo, José; Klein, Artjom; Baker, Christopher J O; Dumontier, Michel

2013-01-01

Background The Web provides widespread access to vast quantities of health-related information that can improve quality-of-life through better understanding of personal symptoms, medical conditions, and available treatments. Unfortunately, identifying a credible and personally relevant subset of information can be a time-consuming and challenging task for users without a medical background. Objective The objective of the Personal Health Lens system is to aid users when reading health-related webpages by providing warnings about personally relevant drug interactions. More broadly, we wish to present a prototype for a novel, generalizable approach to facilitating interactions between a patient, their practitioner(s), and the Web. Methods We utilized a distributed, Semantic Web-based architecture for recognizing personally dangerous drugs consisting of: (1) a private, local triple store of personal health information, (2) Semantic Web services, following the Semantic Automated Discovery and Integration (SADI) design pattern, for text mining and identifying substance interactions, (3) a bookmarklet to trigger analysis of a webpage and annotate it with personalized warnings, and (4) a semantic query that acts as an abstract template of the analytical workflow to be enacted by the system. Results A prototype implementation of the system is provided in the form of a Java standalone executable JAR file. The JAR file bundles all components of the system: the personal health database, locally-running versions of the SADI services, and a javascript bookmarklet that triggers analysis of a webpage. In addition, the demonstration includes a hypothetical personal health profile, allowing the system to be used immediately without configuration. Usage instructions are provided. Conclusions The main strength of the Personal Health Lens system is its ability to organize medical information and to present it to the user in a personalized and contextually relevant manner. While this prototype was limited to a single knowledge domain (drug/drug interactions), the proposed architecture is generalizable, and could act as the foundation for much richer personalized-health-Web clients, while importantly providing a novel and personalizable mechanism for clinical experts to inject their expertise into the browsing experience of their patients in the form of customized semantic queries and ontologies. PMID:23612187

A randomised controlled trial of a web-based educational program in child mental health for schoolteachers.

PubMed

Pereira, Celina Andrade; Wen, Chao Lung; Miguel, Eurípedes Constantino; Polanczyk, Guilherme V

2015-08-01

Children affected by mental disorders are largely unrecognised and untreated across the world. Community resources, including the school system and teachers, are important elements in actions directed to promoting child mental health and preventing and treating mental disorders, especially in low- and middle-income countries. We developed a web-based program to educate primary school teachers on mental disorders in childhood and conducted a cluster-randomised controlled trial to test the effectiveness of the web-based program intervention in comparison with the same program based on text and video materials only and to a waiting-list control group. All nine schools of a single city in the state of São Paulo, Brazil, were randomised to the three groups, and teachers completed the educational programs during 3 weeks. Data were analysed according to complete cases and intention-to-treat approaches. In terms of gains of knowledge about mental disorders, the web-based program intervention was superior to the intervention with text and video materials, and to the waiting-list control group. In terms of beliefs and attitudes about mental disorders, the web-based program intervention group presented less stigmatised concepts than the text and video group and more non-stigmatised concepts than the waiting-list group. No differences were detected in terms of teachers' attitudes. This study demonstrated initial data on the effectiveness of a web-based program in educating schoolteachers on child mental disorders. Future studies are necessary to replicate and extend the findings.

Universally Designed Text on the Web: Towards Readability Criteria Based on Anti-Patterns.

PubMed

Eika, Evelyn

2016-01-01

The readability of web texts affects accessibility. The Web Content Accessibility guidelines (WCAG) state that the recommended reading level should match that of someone who has completed basic schooling. However, WCAG does not give advice on what constitutes an appropriate reading level. Web authors need tools to help composing WCAG compliant texts, and specific criteria are needed. Classic readability metrics are generally based on lengths of words and sentences and have been criticized for being over-simplistic. Automatic measures and classifications of texts' reading levels employing more advanced constructs remain an unresolved problem. If such measures were feasible, what should these be? This work examines three language constructs not captured by current readability indices but believed to significantly affect actual readability, namely, relative clauses, garden path sentences, and left-branching structures. The goal is to see whether quantifications of these stylistic features reflect readability and how they correspond to common readability measures. Manual assessments of a set of authentic web texts for such uses were conducted. The results reveal that texts related to narratives such as children's stories, which are given the highest readability value, do not contain these constructs. The structures in question occur more frequently in expository texts that aim at educating or disseminating information such as strategy and journal articles. The results suggest that language anti-patterns hold potential for establishing a set of deeper readability criteria.

Complex Event Processing for Content-Based Text, Image, and Video Retrieval

DTIC Science & Technology

2016-06-01

NY): Wiley- Interscience; 2000. Feldman R, Sanger J. The text mining handbook: advanced approaches in analyzing unstructured data. New York (NY...ARL-TR-7705 ● JUNE 2016 US Army Research Laboratory Complex Event Processing for Content-Based Text , Image, and Video Retrieval...ARL-TR-7705 ● JUNE 2016 US Army Research Laboratory Complex Event Processing for Content-Based Text , Image, and Video Retrieval

Multi-agents and learning: Implications for Webusage mining.

PubMed

Lotfy, Hewayda M S; Khamis, Soheir M S; Aboghazalah, Maie M

2016-03-01

Characterization of user activities is an important issue in the design and maintenance of websites. Server weblog files have abundant information about the user's current interests. This information can be mined and analyzed therefore the administrators may be able to guide the users in their browsing activity so they may obtain relevant information in a shorter span of time to obtain user satisfaction. Web-based technology facilitates the creation of personally meaningful and socially useful knowledge through supportive interactions, communication and collaboration among educators, learners and information. This paper suggests a new methodology based on learning techniques for a Web-based Multiagent-based application to discover the hidden patterns in the user's visited links. It presents a new approach that involves unsupervised, reinforcement learning, and cooperation between agents. It is utilized to discover patterns that represent the user's profiles in a sample website into specific categories of materials using significance percentages. These profiles are used to make recommendations of interesting links and categories to the user. The experimental results of the approach showed successful user pattern recognition, and cooperative learning among agents to obtain user profiles. It indicates that combining different learning algorithms is capable of improving user satisfaction indicated by the percentage of precision, recall, the progressive category weight and F 1-measure.

Multi-agents and learning: Implications for Webusage mining

PubMed Central

Lotfy, Hewayda M.S.; Khamis, Soheir M.S.; Aboghazalah, Maie M.

2015-01-01

Characterization of user activities is an important issue in the design and maintenance of websites. Server weblog files have abundant information about the user’s current interests. This information can be mined and analyzed therefore the administrators may be able to guide the users in their browsing activity so they may obtain relevant information in a shorter span of time to obtain user satisfaction. Web-based technology facilitates the creation of personally meaningful and socially useful knowledge through supportive interactions, communication and collaboration among educators, learners and information. This paper suggests a new methodology based on learning techniques for a Web-based Multiagent-based application to discover the hidden patterns in the user’s visited links. It presents a new approach that involves unsupervised, reinforcement learning, and cooperation between agents. It is utilized to discover patterns that represent the user’s profiles in a sample website into specific categories of materials using significance percentages. These profiles are used to make recommendations of interesting links and categories to the user. The experimental results of the approach showed successful user pattern recognition, and cooperative learning among agents to obtain user profiles. It indicates that combining different learning algorithms is capable of improving user satisfaction indicated by the percentage of precision, recall, the progressive category weight and F1-measure. PMID:26966569

The APA Style Converter: a Web-based interface for converting articles to APA style for publication.

PubMed

Li, Ping; Cunningham, Krystal

2005-05-01

The APA Style Converter is a Web-based tool with which authors may prepare their articles in APA style according to the APA Publication Manual (5th ed.). The Converter provides a user-friendly interface that allows authors to copy and paste text and upload figures through the Web, and it automatically converts all texts, references, and figures to a structured article in APA style. The output is saved in PDF or RTF format, ready for either electronic submission or hardcopy printing.

Informing child welfare policy and practice: using knowledge discovery and data mining technology via a dynamic Web site.

PubMed

Duncan, Dean F; Kum, Hye-Chung; Weigensberg, Elizabeth Caplick; Flair, Kimberly A; Stewart, C Joy

2008-11-01

Proper management and implementation of an effective child welfare agency requires the constant use of information about the experiences and outcomes of children involved in the system, emphasizing the need for comprehensive, timely, and accurate data. In the past 20 years, there have been many advances in technology that can maximize the potential of administrative data to promote better evaluation and management in the field of child welfare. Specifically, this article discusses the use of knowledge discovery and data mining (KDD), which makes it possible to create longitudinal data files from administrative data sources, extract valuable knowledge, and make the information available via a user-friendly public Web site. This article demonstrates a successful project in North Carolina where knowledge discovery and data mining technology was used to develop a comprehensive set of child welfare outcomes available through a public Web site to facilitate information sharing of child welfare data to improve policy and practice.

Chemotext: A Publicly Available Web Server for Mining Drug-Target-Disease Relationships in PubMed.

PubMed

Capuzzi, Stephen J; Thornton, Thomas E; Liu, Kammy; Baker, Nancy; Lam, Wai In; O'Banion, Colin P; Muratov, Eugene N; Pozefsky, Diane; Tropsha, Alexander

2018-02-26

Elucidation of the mechanistic relationships between drugs, their targets, and diseases is at the core of modern drug discovery research. Thousands of studies relevant to the drug-target-disease (DTD) triangle have been published and annotated in the Medline/PubMed database. Mining this database affords rapid identification of all published studies that confirm connections between vertices of this triangle or enable new inferences of such connections. To this end, we describe the development of Chemotext, a publicly available Web server that mines the entire compendium of published literature in PubMed annotated by Medline Subject Heading (MeSH) terms. The goal of Chemotext is to identify all known DTD relationships and infer missing links between vertices of the DTD triangle. As a proof-of-concept, we show that Chemotext could be instrumental in generating new drug repurposing hypotheses or annotating clinical outcomes pathways for known drugs. The Chemotext Web server is freely available at http://chemotext.mml.unc.edu .

Argo: enabling the development of bespoke workflows and services for disease annotation.

PubMed

Batista-Navarro, Riza; Carter, Jacob; Ananiadou, Sophia

2016-01-01

Argo (http://argo.nactem.ac.uk) is a generic text mining workbench that can cater to a variety of use cases, including the semi-automatic annotation of literature. It enables its technical users to build their own customised text mining solutions by providing a wide array of interoperable and configurable elementary components that can be seamlessly integrated into processing workflows. With Argo's graphical annotation interface, domain experts can then make use of the workflows' automatically generated output to curate information of interest.With the continuously rising need to understand the aetiology of diseases as well as the demand for their informed diagnosis and personalised treatment, the curation of disease-relevant information from medical and clinical documents has become an indispensable scientific activity. In the Fifth BioCreative Challenge Evaluation Workshop (BioCreative V), there was substantial interest in the mining of literature for disease-relevant information. Apart from a panel discussion focussed on disease annotations, the chemical-disease relations (CDR) track was also organised to foster the sharing and advancement of disease annotation tools and resources.This article presents the application of Argo's capabilities to the literature-based annotation of diseases. As part of our participation in BioCreative V's User Interactive Track (IAT), we demonstrated and evaluated Argo's suitability to the semi-automatic curation of chronic obstructive pulmonary disease (COPD) phenotypes. Furthermore, the workbench facilitated the development of some of the CDR track's top-performing web services for normalising disease mentions against the Medical Subject Headings (MeSH) database. In this work, we highlight Argo's support for developing various types of bespoke workflows ranging from ones which enabled us to easily incorporate information from various databases, to those which train and apply machine learning-based concept recognition models, through to user-interactive ones which allow human curators to manually provide their corrections to automatically generated annotations. Our participation in the BioCreative V challenges shows Argo's potential as an enabling technology for curating disease and phenotypic information from literature.Database URL: http://argo.nactem.ac.uk. © The Author(s) 2016. Published by Oxford University Press.

Argo: enabling the development of bespoke workflows and services for disease annotation

PubMed Central

Batista-Navarro, Riza; Carter, Jacob; Ananiadou, Sophia

2016-01-01

Argo (http://argo.nactem.ac.uk) is a generic text mining workbench that can cater to a variety of use cases, including the semi-automatic annotation of literature. It enables its technical users to build their own customised text mining solutions by providing a wide array of interoperable and configurable elementary components that can be seamlessly integrated into processing workflows. With Argo's graphical annotation interface, domain experts can then make use of the workflows' automatically generated output to curate information of interest. With the continuously rising need to understand the aetiology of diseases as well as the demand for their informed diagnosis and personalised treatment, the curation of disease-relevant information from medical and clinical documents has become an indispensable scientific activity. In the Fifth BioCreative Challenge Evaluation Workshop (BioCreative V), there was substantial interest in the mining of literature for disease-relevant information. Apart from a panel discussion focussed on disease annotations, the chemical-disease relations (CDR) track was also organised to foster the sharing and advancement of disease annotation tools and resources. This article presents the application of Argo’s capabilities to the literature-based annotation of diseases. As part of our participation in BioCreative V’s User Interactive Track (IAT), we demonstrated and evaluated Argo’s suitability to the semi-automatic curation of chronic obstructive pulmonary disease (COPD) phenotypes. Furthermore, the workbench facilitated the development of some of the CDR track’s top-performing web services for normalising disease mentions against the Medical Subject Headings (MeSH) database. In this work, we highlight Argo’s support for developing various types of bespoke workflows ranging from ones which enabled us to easily incorporate information from various databases, to those which train and apply machine learning-based concept recognition models, through to user-interactive ones which allow human curators to manually provide their corrections to automatically generated annotations. Our participation in the BioCreative V challenges shows Argo’s potential as an enabling technology for curating disease and phenotypic information from literature. Database URL: http://argo.nactem.ac.uk PMID:27189607

A Text-Mining Framework for Supporting Systematic Reviews.

PubMed

Li, Dingcheng; Wang, Zhen; Wang, Liwei; Sohn, Sunghwan; Shen, Feichen; Murad, Mohammad Hassan; Liu, Hongfang

2016-11-01

Systematic reviews (SRs) involve the identification, appraisal, and synthesis of all relevant studies for focused questions in a structured reproducible manner. High-quality SRs follow strict procedures and require significant resources and time. We investigated advanced text-mining approaches to reduce the burden associated with abstract screening in SRs and provide high-level information summary. A text-mining SR supporting framework consisting of three self-defined semantics-based ranking metrics was proposed, including keyword relevance, indexed-term relevance and topic relevance. Keyword relevance is based on the user-defined keyword list used in the search strategy. Indexed-term relevance is derived from indexed vocabulary developed by domain experts used for indexing journal articles and books. Topic relevance is defined as the semantic similarity among retrieved abstracts in terms of topics generated by latent Dirichlet allocation, a Bayesian-based model for discovering topics. We tested the proposed framework using three published SRs addressing a variety of topics (Mass Media Interventions, Rectal Cancer and Influenza Vaccine). The results showed that when 91.8%, 85.7%, and 49.3% of the abstract screening labor was saved, the recalls were as high as 100% for the three cases; respectively. Relevant studies identified manually showed strong topic similarity through topic analysis, which supported the inclusion of topic analysis as relevance metric. It was demonstrated that advanced text mining approaches can significantly reduce the abstract screening labor of SRs and provide an informative summary of relevant studies.

Biomedical text mining and its applications in cancer research.

PubMed

Zhu, Fei; Patumcharoenpol, Preecha; Zhang, Cheng; Yang, Yang; Chan, Jonathan; Meechai, Asawin; Vongsangnak, Wanwipa; Shen, Bairong

2013-04-01

Cancer is a malignant disease that has caused millions of human deaths. Its study has a long history of well over 100years. There have been an enormous number of publications on cancer research. This integrated but unstructured biomedical text is of great value for cancer diagnostics, treatment, and prevention. The immense body and rapid growth of biomedical text on cancer has led to the appearance of a large number of text mining techniques aimed at extracting novel knowledge from scientific text. Biomedical text mining on cancer research is computationally automatic and high-throughput in nature. However, it is error-prone due to the complexity of natural language processing. In this review, we introduce the basic concepts underlying text mining and examine some frequently used algorithms, tools, and data sets, as well as assessing how much these algorithms have been utilized. We then discuss the current state-of-the-art text mining applications in cancer research and we also provide some resources for cancer text mining. With the development of systems biology, researchers tend to understand complex biomedical systems from a systems biology viewpoint. Thus, the full utilization of text mining to facilitate cancer systems biology research is fast becoming a major concern. To address this issue, we describe the general workflow of text mining in cancer systems biology and each phase of the workflow. We hope that this review can (i) provide a useful overview of the current work of this field; (ii) help researchers to choose text mining tools and datasets; and (iii) highlight how to apply text mining to assist cancer systems biology research. Copyright © 2012 Elsevier Inc. All rights reserved.

The Umbra Simulation and Integration Framework Applied to Emergency Response Training

NASA Technical Reports Server (NTRS)

Hamilton, Paul Lawrence; Britain, Robert

2010-01-01

The Mine Emergency Response Interactive Training Simulation (MERITS) is intended to prepare personnel to manage an emergency in an underground coal mine. The creation of an effective training environment required realistic emergent behavior in response to simulation events and trainee interventions, exploratory modification of miner behavior rules, realistic physics, and incorporation of legacy code. It also required the ability to add rich media to the simulation without conflicting with normal desktop security settings. Our Umbra Simulation and Integration Framework facilitated agent-based modeling of miners and rescuers and made it possible to work with subject matter experts to quickly adjust behavior through script editing, rather than through lengthy programming and recompilation. Integration of Umbra code with the WebKit browser engine allowed the use of JavaScript-enabled local web pages for media support. This project greatly extended the capabilities of Umbra in support of training simulations and has implications for simulations that combine human behavior, physics, and rich media.

A web server for mining Comparative Genomic Hybridization (CGH) data

NASA Astrophysics Data System (ADS)

Liu, Jun; Ranka, Sanjay; Kahveci, Tamer

2007-11-01

Advances in cytogenetics and molecular biology has established that chromosomal alterations are critical in the pathogenesis of human cancer. Recurrent chromosomal alterations provide cytological and molecular markers for the diagnosis and prognosis of disease. They also facilitate the identification of genes that are important in carcinogenesis, which in the future may help in the development of targeted therapy. A large amount of publicly available cancer genetic data is now available and it is growing. There is a need for public domain tools that allow users to analyze their data and visualize the results. This chapter describes a web based software tool that will allow researchers to analyze and visualize Comparative Genomic Hybridization (CGH) datasets. It employs novel data mining methodologies for clustering and classification of CGH datasets as well as algorithms for identifying important markers (small set of genomic intervals with aberrations) that are potentially cancer signatures. The developed software will help in understanding the relationships between genomic aberrations and cancer types.

Text Mining Effectively Scores and Ranks the Literature for Improving Chemical-Gene-Disease Curation at the Comparative Toxicogenomics Database

PubMed Central

Johnson, Robin J.; Lay, Jean M.; Lennon-Hopkins, Kelley; Saraceni-Richards, Cynthia; Sciaky, Daniela; Murphy, Cynthia Grondin; Mattingly, Carolyn J.

2013-01-01

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is a public resource that curates interactions between environmental chemicals and gene products, and their relationships to diseases, as a means of understanding the effects of environmental chemicals on human health. CTD provides a triad of core information in the form of chemical-gene, chemical-disease, and gene-disease interactions that are manually curated from scientific articles. To increase the efficiency, productivity, and data coverage of manual curation, we have leveraged text mining to help rank and prioritize the triaged literature. Here, we describe our text-mining process that computes and assigns each article a document relevancy score (DRS), wherein a high DRS suggests that an article is more likely to be relevant for curation at CTD. We evaluated our process by first text mining a corpus of 14,904 articles triaged for seven heavy metals (cadmium, cobalt, copper, lead, manganese, mercury, and nickel). Based upon initial analysis, a representative subset corpus of 3,583 articles was then selected from the 14,094 articles and sent to five CTD biocurators for review. The resulting curation of these 3,583 articles was analyzed for a variety of parameters, including article relevancy, novel data content, interaction yield rate, mean average precision, and biological and toxicological interpretability. We show that for all measured parameters, the DRS is an effective indicator for scoring and improving the ranking of literature for the curation of chemical-gene-disease information at CTD. Here, we demonstrate how fully incorporating text mining-based DRS scoring into our curation pipeline enhances manual curation by prioritizing more relevant articles, thereby increasing data content, productivity, and efficiency. PMID:23613709

Data Processing and Text Mining Technologies on Electronic Medical Records: A Review

PubMed Central

Sun, Wencheng; Li, Yangyang; Liu, Fang; Fang, Shengqun; Wang, Guoyan

2018-01-01

Currently, medical institutes generally use EMR to record patient's condition, including diagnostic information, procedures performed, and treatment results. EMR has been recognized as a valuable resource for large-scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy, and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation, and data reduction. For semistructured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (named-entity recognition) and RE (relation extraction). This paper focuses on the process of EMR processing and emphatically analyzes the key techniques. In addition, we make an in-depth study on the applications developed based on text mining together with the open challenges and research issues for future work. PMID:29849998

What Can Pictures Tell Us About Web Pages? Improving Document Search Using Images.

PubMed

Rodriguez-Vaamonde, Sergio; Torresani, Lorenzo; Fitzgibbon, Andrew W

2015-06-01

Traditional Web search engines do not use the images in the HTML pages to find relevant documents for a given query. Instead, they typically operate by computing a measure of agreement between the keywords provided by the user and only the text portion of each page. In this paper we study whether the content of the pictures appearing in a Web page can be used to enrich the semantic description of an HTML document and consequently boost the performance of a keyword-based search engine. We present a Web-scalable system that exploits a pure text-based search engine to find an initial set of candidate documents for a given query. Then, the candidate set is reranked using visual information extracted from the images contained in the pages. The resulting system retains the computational efficiency of traditional text-based search engines with only a small additional storage cost needed to encode the visual information. We test our approach on one of the TREC Million Query Track benchmarks where we show that the exploitation of visual content yields improvement in accuracies for two distinct text-based search engines, including the system with the best reported performance on this benchmark. We further validate our approach by collecting document relevance judgements on our search results using Amazon Mechanical Turk. The results of this experiment confirm the improvement in accuracy produced by our image-based reranker over a pure text-based system.

Optimising text messaging to improve adherence to web-based smoking cessation treatment: a randomised control trial protocol

PubMed Central

Graham, Amanda L; Jacobs, Megan A; Cohn, Amy M; Cha, Sarah; Abroms, Lorien C; Papandonatos, George D; Whittaker, Robyn

2016-01-01

Introduction Millions of smokers use the Internet for smoking cessation assistance each year; however, most smokers engage minimally with even the best designed websites. The ubiquity of mobile devices and their effectiveness in promoting adherence in other areas of health behaviour change make them a promising tool to address adherence in Internet smoking cessation interventions. Text messaging is used by most adults, and messages can proactively encourage use of a web-based intervention. Text messaging can also be integrated with an Internet intervention to facilitate the use of core Internet intervention components. Methods and analysis We identified four aspects of a text message intervention that may enhance its effectiveness in promoting adherence to a web-based smoking cessation programme: personalisation, integration, dynamic tailoring and message intensity. Phase I will use a two-level full factorial design to test the impact of these four experimental features on adherence to a web-based intervention. The primary outcome is a composite metric of adherence that incorporates general utilisation metrics (eg, logins, page views) and specific feature utilisation shown to predict abstinence. Participants will be N=860 adult smokers who register on an established Internet cessation programme and enrol in its text message programme. Phase II will be a two-arm randomised trial to compare the efficacy of the web-based cessation programme alone and in conjunction with the optimised text messaging intervention on 30-day point prevalence abstinence at 9 months. Phase II participants will be N=600 adult smokers who register to use an established Internet cessation programme and enrol in text messaging. Secondary analyses will explore whether adherence mediates the effect of treatment condition on outcome. Ethics and dissemination This protocol was approved by Chesapeake IRB. We will disseminate study results through peer-reviewed manuscripts and conference presentations related to the methods and design, outcomes and exploratory analyses. Trial registration number NCT02585206. PMID:27029775

Supporting the education evidence portal via text mining

PubMed Central

Ananiadou, Sophia; Thompson, Paul; Thomas, James; Mu, Tingting; Oliver, Sandy; Rickinson, Mark; Sasaki, Yutaka; Weissenbacher, Davy; McNaught, John

2010-01-01

The UK Education Evidence Portal (eep) provides a single, searchable, point of access to the contents of the websites of 33 organizations relating to education, with the aim of revolutionizing work practices for the education community. Use of the portal alleviates the need to spend time searching multiple resources to find relevant information. However, the combined content of the websites of interest is still very large (over 500 000 documents and growing). This means that searches using the portal can produce very large numbers of hits. As users often have limited time, they would benefit from enhanced methods of performing searches and viewing results, allowing them to drill down to information of interest more efficiently, without having to sift through potentially long lists of irrelevant documents. The Joint Information Systems Committee (JISC)-funded ASSIST project has produced a prototype web interface to demonstrate the applicability of integrating a number of text-mining tools and methods into the eep, to facilitate an enhanced searching, browsing and document-viewing experience. New features include automatic classification of documents according to a taxonomy, automatic clustering of search results according to similar document content, and automatic identification and highlighting of key terms within documents. PMID:20643679

A Generic Framework for Extraction of Knowledge from Social Web Sources (Social Networking Websites) for an Online Recommendation System

ERIC Educational Resources Information Center

Sathick, Javubar; Venkat, Jaya

2015-01-01

Mining social web data is a challenging task and finding user interest for personalized and non-personalized recommendation systems is another important task. Knowledge sharing among web users has become crucial in determining usage of web data and personalizing content in various social websites as per the user's wish. This paper aims to design a…

Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure

PubMed Central

Badger, Jonathan; LaRose, Eric; Shirzadi, Ehsan; Mahnke, Andrea; Mayer, John; Ye, Zhan; Page, David; Peissig, Peggy

2017-01-01

Background The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs. Objective The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs. Methods We analyzed the following two data sources: (1) biomedical articles and (2) health-related social media blog posts. We developed an intelligent and scalable text mining solution on big data infrastructures composed of Apache Spark, natural language processing, and machine learning. This was combined with an Elasticsearch No-SQL distributed database to explore and visualize ADEs. Results The accuracy, precision, recall, and area under receiver operating characteristic of the system were 92.7%, 93.6%, 93.0%, and 0.905, respectively, and showed better results in comparison with traditional approaches in the literature. This work not only detected and classified ADE sentences from big data biomedical literature but also scientifically visualized ADE interactions. Conclusions To the best of our knowledge, this work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media. This contribution illustrates possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis. PMID:29222076

Incorporating linguistic knowledge for learning distributed word representations.

PubMed

Wang, Yan; Liu, Zhiyuan; Sun, Maosong

2015-01-01

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations. Experiment results demonstrate that our estimated word representation achieves better performance in task of semantic relatedness ranking. This indicates that our methods can efficiently encode both prior knowledge from knowledge bases and statistical knowledge from large-scale text corpora into a unified word representation model, which will benefit many tasks in text mining.

Incorporating Linguistic Knowledge for Learning Distributed Word Representations

PubMed Central

Wang, Yan; Liu, Zhiyuan; Sun, Maosong

2015-01-01

Combined with neural language models, distributed word representations achieve significant advantages in computational linguistics and text mining. Most existing models estimate distributed word vectors from large-scale data in an unsupervised fashion, which, however, do not take rich linguistic knowledge into consideration. Linguistic knowledge can be represented as either link-based knowledge or preference-based knowledge, and we propose knowledge regularized word representation models (KRWR) to incorporate these prior knowledge for learning distributed word representations. Experiment results demonstrate that our estimated word representation achieves better performance in task of semantic relatedness ranking. This indicates that our methods can efficiently encode both prior knowledge from knowledge bases and statistical knowledge from large-scale text corpora into a unified word representation model, which will benefit many tasks in text mining. PMID:25874581

Compatibility between Text Mining and Qualitative Research in the Perspectives of Grounded Theory, Content Analysis, and Reliability

ERIC Educational Resources Information Center

Yu, Chong Ho; Jannasch-Pennell, Angel; DiGangi, Samuel

2011-01-01

The objective of this article is to illustrate that text mining and qualitative research are epistemologically compatible. First, like many qualitative research approaches, such as grounded theory, text mining encourages open-mindedness and discourages preconceptions. Contrary to the popular belief that text mining is a linear and fully automated…

Text mining meets workflow: linking U-Compare with Taverna

PubMed Central

Kano, Yoshinobu; Dobson, Paul; Nakanishi, Mio; Tsujii, Jun'ichi; Ananiadou, Sophia

2010-01-01

Summary: Text mining from the biomedical literature is of increasing importance, yet it is not easy for the bioinformatics community to create and run text mining workflows due to the lack of accessibility and interoperability of the text mining resources. The U-Compare system provides a wide range of bio text mining resources in a highly interoperable workflow environment where workflows can very easily be created, executed, evaluated and visualized without coding. We have linked U-Compare to Taverna, a generic workflow system, to expose text mining functionality to the bioinformatics community. Availability: http://u-compare.org/taverna.html, http://u-compare.org Contact: kano@is.s.u-tokyo.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20709690

Information Gain Based Dimensionality Selection for Classifying Text Documents

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dumidu Wijayasekara; Milos Manic; Miles McQueen

2013-06-01

Selecting the optimal dimensions for various knowledge extraction applications is an essential component of data mining. Dimensionality selection techniques are utilized in classification applications to increase the classification accuracy and reduce the computational complexity. In text classification, where the dimensionality of the dataset is extremely high, dimensionality selection is even more important. This paper presents a novel, genetic algorithm based methodology, for dimensionality selection in text mining applications that utilizes information gain. The presented methodology uses information gain of each dimension to change the mutation probability of chromosomes dynamically. Since the information gain is calculated a priori, the computational complexitymore » is not affected. The presented method was tested on a specific text classification problem and compared with conventional genetic algorithm based dimensionality selection. The results show an improvement of 3% in the true positives and 1.6% in the true negatives over conventional dimensionality selection methods.« less

Mining Hidden Gems Beneath the Surface: A Look At the Invisible Web.

ERIC Educational Resources Information Center

Carlson, Randal D.; Repman, Judi

2002-01-01

Describes resources for researchers called the Invisible Web that are hidden from the usual search engines and other tools and contrasts them with those resources available on the surface Web. Identifies specialized search tools, databases, and strategies that can be used to locate credible in-depth information. (Author/LRW)

Opinion Integration and Summarization

ERIC Educational Resources Information Center

Lu, Yue

2011-01-01

As Web 2.0 applications become increasingly popular, more and more people express their opinions on the Web in various ways in real time. Such wide coverage of topics and abundance of users make the Web an extremely valuable source for mining people's opinions about all kinds of topics. However, since the opinions are usually expressed as…

Agile Text Mining for the 2014 i2b2/UTHealth Cardiac Risk Factors Challenge

PubMed Central

Cormack, James; Nath, Chinmoy; Milward, David; Raja, Kalpana; Jonnalagadda, Siddhartha R

2016-01-01

This paper describes the use of an agile text mining platform (Linguamatics’ Interactive Information Extraction Platform, I2E) to extract document-level cardiac risk factors in patient records as defined in the i2b2/UTHealth 2014 Challenge. The approach uses a data-driven rule-based methodology with the addition of a simple supervised classifier. We demonstrate that agile text mining allows for rapid optimization of extraction strategies, while post-processing can leverage annotation guidelines, corpus statistics and logic inferred from the gold standard data. We also show how data imbalance in a training set affects performance. Evaluation of this approach on the test data gave an F-Score of 91.7%, one percent behind the top performing system. PMID:26209007

Chemical named entities recognition: a review on approaches and applications.

PubMed

Eltyeb, Safaa; Salim, Naomie

2014-01-01

The rapid increase in the flow rate of published digital information in all disciplines has resulted in a pressing need for techniques that can simplify the use of this information. The chemistry literature is very rich with information about chemical entities. Extracting molecules and their related properties and activities from the scientific literature to "text mine" these extracted data and determine contextual relationships helps research scientists, particularly those in drug development. One of the most important challenges in chemical text mining is the recognition of chemical entities mentioned in the texts. In this review, the authors briefly introduce the fundamental concepts of chemical literature mining, the textual contents of chemical documents, and the methods of naming chemicals in documents. We sketch out dictionary-based, rule-based and machine learning, as well as hybrid chemical named entity recognition approaches with their applied solutions. We end with an outlook on the pros and cons of these approaches and the types of chemical entities extracted.

78 FR 77706 - Notice of Intent To Prepare an Environmental Impact Statement for the Proposed Gemfield Mine...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-24

... gold mine and associated processing and ancillary facilities. The project would be located on public... media, newspapers and the BLM Web site at: http://www.blm.gov/nv/st/en/fo/battle_mountain_field.html... to construct, operate, reclaim, and close an open pit, heap leach, gold mining operation known as the...

Cognition-Based Approaches for High-Precision Text Mining

ERIC Educational Resources Information Center

Shannon, George John

2017-01-01

This research improves the precision of information extraction from free-form text via the use of cognitive-based approaches to natural language processing (NLP). Cognitive-based approaches are an important, and relatively new, area of research in NLP and search, as well as linguistics. Cognitive approaches enable significant improvements in both…

Mining-related metals in terrestrial food webs of the upper Clark Fork River basin

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pastorok, R.A.; LaTier, A.J.; Butcher, M.K.

1994-12-31

Fluvial deposits of tailings and other mining-related waste in selected riparian habitats of the Upper Clark Fork River basin (Montana) have resulted in metals enriched soils. The significance of metals exposure to selected wildlife species was evaluated by measuring tissue residues of metals (arsenic, cadmium, copper, lead, zinc) in key dietary species, including dominant grasses (tufted hair grass and redtop), willows, alfalfa, barley, invertebrates (grasshoppers, spiders, and beetles), and deer mice. Average metals concentrations in grasses, invertebrates, and deer mice collected from tailings-affected sites were elevated relative to reference to reference levels. Soil-tissue bioconcentration factors for grasses and invertebrates weremore » generally lower than expected based on the range of values in the literature, indicating the reduced bioavailability of metals from mining waste. In general, metals concentrations in willows, alfalfa, and barley were not elevated above reference levels. Using these data and plausible assumptions for other exposure parameters for white-tailed deer, red fox, and American kestrel, metals intake was estimated for soil and diet ingestion pathways. Comparisons of exposure estimates with toxicity reference values indicated that the elevated concentrations of metals in key food web species do not pose a significant risk to wildlife.« less

CrosstalkNet: A Visualization Tool for Differential Co-expression Networks and Communities.

PubMed

Manem, Venkata; Adam, George Alexandru; Gruosso, Tina; Gigoux, Mathieu; Bertos, Nicholas; Park, Morag; Haibe-Kains, Benjamin

2018-04-15

Variations in physiological conditions can rewire molecular interactions between biological compartments, which can yield novel insights into gain or loss of interactions specific to perturbations of interest. Networks are a promising tool to elucidate intercellular interactions, yet exploration of these large-scale networks remains a challenge due to their high dimensionality. To retrieve and mine interactions, we developed CrosstalkNet, a user friendly, web-based network visualization tool that provides a statistical framework to infer condition-specific interactions coupled with a community detection algorithm for bipartite graphs to identify significantly dense subnetworks. As a case study, we used CrosstalkNet to mine a set of 54 and 22 gene-expression profiles from breast tumor and normal samples, respectively, with epithelial and stromal compartments extracted via laser microdissection. We show how CrosstalkNet can be used to explore large-scale co-expression networks and to obtain insights into the biological processes that govern cross-talk between different tumor compartments. Significance: This web application enables researchers to mine complex networks and to decipher novel biological processes in tumor epithelial-stroma cross-talk as well as in other studies of intercompartmental interactions. Cancer Res; 78(8); 2140-3. ©2018 AACR . ©2018 American Association for Cancer Research.

Flooded Underground Coal Mines: A Significant Source of Inexpensive Geothermal Energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Watzlaf, G.R.; Ackman, T.E.

2007-04-01

Many mining regions in the United States contain extensive areas of flooded underground mines. The water within these mines represents a significant and widespread opportunity for extracting low-grade, geothermal energy. Based on current energy prices, geothermal heat pump systems using mine water could reduce the annual costs for heating to over 70 percent compared to conventional heating methods (natural gas or heating oil). These same systems could reduce annual cooling costs by up to 50 percent over standard air conditioning in many areas of the country. (Formatted full-text version is released by permission of publisher)

Mining of the social network extraction

NASA Astrophysics Data System (ADS)

Nasution, M. K. M.; Hardi, M.; Syah, R.

2017-01-01

The use of Web as social media is steadily gaining ground in the study of social actor behaviour. However, information in Web can be interpreted in accordance with the ability of the method such as superficial methods for extracting social networks. Each method however has features and drawbacks: it cannot reveal the behaviour of social actors, but it has the hidden information about them. Therefore, this paper aims to reveal such information in the social networks mining. Social behaviour could be expressed through a set of words extracted from the list of snippets.

Survey of Natural Language Processing Techniques in Bioinformatics.

PubMed

Zeng, Zhiqiang; Shi, Hua; Wu, Yun; Hong, Zhiling

2015-01-01

Informatics methods, such as text mining and natural language processing, are always involved in bioinformatics research. In this study, we discuss text mining and natural language processing methods in bioinformatics from two perspectives. First, we aim to search for knowledge on biology, retrieve references using text mining methods, and reconstruct databases. For example, protein-protein interactions and gene-disease relationship can be mined from PubMed. Then, we analyze the applications of text mining and natural language processing techniques in bioinformatics, including predicting protein structure and function, detecting noncoding RNA. Finally, numerous methods and applications, as well as their contributions to bioinformatics, are discussed for future use by text mining and natural language processing researchers.

Development and testing of a text-mining approach to analyse patients' comments on their experiences of colorectal cancer care.

PubMed

Wagland, Richard; Recio-Saucedo, Alejandra; Simon, Michael; Bracher, Michael; Hunt, Katherine; Foster, Claire; Downing, Amy; Glaser, Adam; Corner, Jessica

2016-08-01

Quality of cancer care may greatly impact on patients' health-related quality of life (HRQoL). Free-text responses to patient-reported outcome measures (PROMs) provide rich data but analysis is time and resource-intensive. This study developed and tested a learning-based text-mining approach to facilitate analysis of patients' experiences of care and develop an explanatory model illustrating impact on HRQoL. Respondents to a population-based survey of colorectal cancer survivors provided free-text comments regarding their experience of living with and beyond cancer. An existing coding framework was tested and adapted, which informed learning-based text mining of the data. Machine-learning algorithms were trained to identify comments relating to patients' specific experiences of service quality, which were verified by manual qualitative analysis. Comparisons between coded retrieved comments and a HRQoL measure (EQ5D) were explored. The survey response rate was 63.3% (21 802/34 467), of which 25.8% (n=5634) participants provided free-text comments. Of retrieved comments on experiences of care (n=1688), over half (n=1045, 62%) described positive care experiences. Most negative experiences concerned a lack of post-treatment care (n=191, 11% of retrieved comments) and insufficient information concerning self-management strategies (n=135, 8%) or treatment side effects (n=160, 9%). Associations existed between HRQoL scores and coded algorithm-retrieved comments. Analysis indicated that the mechanism by which service quality impacted on HRQoL was the extent to which services prevented or alleviated challenges associated with disease and treatment burdens. Learning-based text mining techniques were found useful and practical tools to identify specific free-text comments within a large dataset, facilitating resource-efficient qualitative analysis. This method should be considered for future PROM analysis to inform policy and practice. Study findings indicated that perceived care quality directly impacts on HRQoL. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

An Extraction Method of an Informative DOM Node from a Web Page by Using Layout Information

NASA Astrophysics Data System (ADS)

Tsuruta, Masanobu; Masuyama, Shigeru

We propose an informative DOM node extraction method from a Web page for preprocessing of Web content mining. Our proposed method LM uses layout data of DOM nodes generated by a generic Web browser, and the learning set consists of hundreds of Web pages and the annotations of informative DOM nodes of those Web pages. Our method does not require large scale crawling of the whole Web site to which the target Web page belongs. We design LM so that it uses the information of the learning set more efficiently in comparison to the existing method that uses the same learning set. By experiments, we evaluate the methods obtained by combining one that consists of the method for extracting the informative DOM node both the proposed method and the existing methods, and the existing noise elimination methods: Heur removes advertisements and link-lists by some heuristics and CE removes the DOM nodes existing in the Web pages in the same Web site to which the target Web page belongs. Experimental results show that 1) LM outperforms other methods for extracting the informative DOM node, 2) the combination method (LM, {CE(10), Heur}) based on LM (precision: 0.755, recall: 0.826, F-measure: 0.746) outperforms other combination methods.

Text Mining in Organizational Research

PubMed Central

Kobayashi, Vladimer B.; Berkers, Hannah A.; Kismihók, Gábor; Den Hartog, Deanne N.

2017-01-01

Despite the ubiquity of textual data, so far few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically increasing the amount data that can be analyzed. This article aims to acquaint organizational researchers with the fundamental logic underpinning text mining, the analytical stages involved, and contemporary techniques that may be used to achieve different types of objectives. The specific analytical techniques reviewed are (a) dimensionality reduction, (b) distance and similarity computing, (c) clustering, (d) topic modeling, and (e) classification. We describe how text mining may extend contemporary organizational research by allowing the testing of existing or new research questions with data that are likely to be rich, contextualized, and ecologically valid. After an exploration of how evidence for the validity of text mining output may be generated, we conclude the article by illustrating the text mining process in a job analysis setting using a dataset composed of job vacancies. PMID:29881248

Text Mining in Organizational Research.

PubMed

Kobayashi, Vladimer B; Mol, Stefan T; Berkers, Hannah A; Kismihók, Gábor; Den Hartog, Deanne N

2018-07-01

Despite the ubiquity of textual data, so far few researchers have applied text mining to answer organizational research questions. Text mining, which essentially entails a quantitative approach to the analysis of (usually) voluminous textual data, helps accelerate knowledge discovery by radically increasing the amount data that can be analyzed. This article aims to acquaint organizational researchers with the fundamental logic underpinning text mining, the analytical stages involved, and contemporary techniques that may be used to achieve different types of objectives. The specific analytical techniques reviewed are (a) dimensionality reduction, (b) distance and similarity computing, (c) clustering, (d) topic modeling, and (e) classification. We describe how text mining may extend contemporary organizational research by allowing the testing of existing or new research questions with data that are likely to be rich, contextualized, and ecologically valid. After an exploration of how evidence for the validity of text mining output may be generated, we conclude the article by illustrating the text mining process in a job analysis setting using a dataset composed of job vacancies.

A comprehensive review on privacy preserving data mining.

PubMed

Aldeen, Yousra Abdul Alsahib S; Salleh, Mazleena; Razzaque, Mohammad Abdur

2015-01-01

Preservation of privacy in data mining has emerged as an absolute prerequisite for exchanging confidential information in terms of data analysis, validation, and publishing. Ever-escalating internet phishing posed severe threat on widespread propagation of sensitive information over the web. Conversely, the dubious feelings and contentions mediated unwillingness of various information providers towards the reliability protection of data from disclosure often results utter rejection in data sharing or incorrect information sharing. This article provides a panoramic overview on new perspective and systematic interpretation of a list published literatures via their meticulous organization in subcategories. The fundamental notions of the existing privacy preserving data mining methods, their merits, and shortcomings are presented. The current privacy preserving data mining techniques are classified based on distortion, association rule, hide association rule, taxonomy, clustering, associative classification, outsourced data mining, distributed, and k-anonymity, where their notable advantages and disadvantages are emphasized. This careful scrutiny reveals the past development, present research challenges, future trends, the gaps and weaknesses. Further significant enhancements for more robust privacy protection and preservation are affirmed to be mandatory.

WebCSD: the online portal to the Cambridge Structural Database

PubMed Central

Thomas, Ian R.; Bruno, Ian J.; Cole, Jason C.; Macrae, Clare F.; Pidcock, Elna; Wood, Peter A.

2010-01-01

WebCSD, a new web-based application developed by the Cambridge Crystallographic Data Centre, offers fast searching of the Cambridge Structural Database using only a standard internet browser. Search facilities include two-dimensional substructure, molecular similarity, text/numeric and reduced cell searching. Text, chemical diagrams and three-dimensional structural information can all be studied in the results browser using the efficient entry summaries and embedded three-dimensional viewer. PMID:22477776

[The method and application to construct experience recommendation platform of acupuncture ancient books based on data mining technology].

PubMed

Chen, Chuyun; Hong, Jiaming; Zhou, Weilin; Lin, Guohua; Wang, Zhengfei; Zhang, Qufei; Lu, Cuina; Lu, Lihong

2017-07-12

To construct a knowledge platform of acupuncture ancient books based on data mining technology, and to provide retrieval service for users. The Oracle 10 g database was applied and JAVA was selected as development language; based on the standard library and ancient books database established by manual entry, a variety of data mining technologies, including word segmentation, speech tagging, dependency analysis, rule extraction, similarity calculation, ambiguity analysis, supervised classification technology were applied to achieve text automatic extraction of ancient books; in the last, through association mining and decision analysis, the comprehensive and intelligent analysis of disease and symptom, meridians, acupoints, rules of acupuncture and moxibustion in acupuncture ancient books were realized, and retrieval service was provided for users through structure of browser/server (B/S). The platform realized full-text retrieval, word frequency analysis and association analysis; when diseases or acupoints were searched, the frequencies of meridian, acupoints (diseases) and techniques were presented from high to low, meanwhile the support degree and confidence coefficient between disease and acupoints (special acupoint), acupoints and acupoints in prescription, disease or acupoints and technique were presented. The experience platform of acupuncture ancient books based on data mining technology could be used as a reference for selection of disease, meridian and acupoint in clinical treatment and education of acupuncture and moxibustion.

Untangling Topic Threads in Chat-Based Communication: A Case Study

DTIC Science & Technology

2011-08-01

learning techniques such as clustering are very popular for analyzing text for topic identification (Anjewierden,, Kollöffel and Hulshof 2007; Adams...Anjewierden, A., Kollöffel, B., and Hulshof , C. (2007). Towards educational data mining: Using data mining methods for automated chat analysis to

ToxRefDB - Release user-friendly web-based tool for mining ToxRefDB

EPA Science Inventory

The updated URL link is for a table of NCCT ToxCast public datasets. The next to last row of the table has the link for the US EPA ToxCast ToxRefDB Data Release October 2014. ToxRefDB provides detailed chemical toxicity data in a publically accessible searchable format. ToxRefD...

The Comprehensive Phytopathogen Genomics Resource: a web-based resource for data-mining plant pathogen genomes.

PubMed

Hamilton, John P; Neeno-Eckwall, Eric C; Adhikari, Bishwo N; Perna, Nicole T; Tisserat, Ned; Leach, Jan E; Lévesque, C André; Buell, C Robin

2011-01-01

The Comprehensive Phytopathogen Genomics Resource (CPGR) provides a web-based portal for plant pathologists and diagnosticians to view the genome and trancriptome sequence status of 806 bacterial, fungal, oomycete, nematode, viral and viroid plant pathogens. Tools are available to search and analyze annotated genome sequences of 74 bacterial, fungal and oomycete pathogens. Oomycete and fungal genomes are obtained directly from GenBank, whereas bacterial genome sequences are downloaded from the A Systematic Annotation Package (ASAP) database that provides curation of genomes using comparative approaches. Curated lists of bacterial genes relevant to pathogenicity and avirulence are also provided. The Plant Pathogen Transcript Assemblies Database provides annotated assemblies of the transcribed regions of 82 eukaryotic genomes from publicly available single pass Expressed Sequence Tags. Data-mining tools are provided along with tools to create candidate diagnostic markers, an emerging use for genomic sequence data in plant pathology. The Plant Pathogen Ribosomal DNA (rDNA) database is a resource for pathogens that lack genome or transcriptome data sets and contains 131 755 rDNA sequences from GenBank for 17 613 species identified as plant pathogens and related genera. Database URL: http://cpgr.plantbiology.msu.edu.

WWW Motivation Mining: Finding Treasures for Teaching Evaluation Skills, Grades 1-6. Professional Growth Series.

ERIC Educational Resources Information Center

Arnone, Marilyn P.; Small, Ruth V.

Designed for elementary or middle school teachers and library media specialists, this book provides educators with practical, easy-to-use ways of applying motivation assessment techniques when selecting World Wide Web sites for inclusion in their lessons and offers concrete examples of how to use Web evaluation with young learners. WebMAC…

Operating System Support for Shared Hardware Data Structures

DTIC Science & Technology

2013-01-31

Carbon [73] uses hardware queues to improve fine-grained multitasking for Recognition, Mining , and Synthesis. Compared to software ap- proaches...web transaction processing, data mining , and multimedia. Early work in database processors [114, 96, 79, 111] reduce the costs of relational database...assignment can be solved statically or dynamically. Static assignment deter- mines offline which data structures are assigned to use HWDS resources and at

77 FR 4360 - Notice of Availability of the Draft Environmental Impact Statement for the Hycroft Mine Expansion...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-27

... comments related to the Hycroft Mine Expansion Draft EIS by any of the following methods: Web site: www.blm..., Nevada 89445, Attn. Kathleen Rehberg. Copies of the Hycroft Mine Expansion Draft EIS are available in the... hours. The FIRS is available 24 hours a day, 7 days a week, to leave a message or question with the...

Web Video Event Recognition by Semantic Analysis From Ubiquitous Documents.

PubMed

Yu, Litao; Yang, Yang; Huang, Zi; Wang, Peng; Song, Jingkuan; Shen, Heng Tao

2016-12-01

In recent years, the task of event recognition from videos has attracted increasing interest in multimedia area. While most of the existing research was mainly focused on exploring visual cues to handle relatively small-granular events, it is difficult to directly analyze video content without any prior knowledge. Therefore, synthesizing both the visual and semantic analysis is a natural way for video event understanding. In this paper, we study the problem of Web video event recognition, where Web videos often describe large-granular events and carry limited textual information. Key challenges include how to accurately represent event semantics from incomplete textual information and how to effectively explore the correlation between visual and textual cues for video event understanding. We propose a novel framework to perform complex event recognition from Web videos. In order to compensate the insufficient expressive power of visual cues, we construct an event knowledge base by deeply mining semantic information from ubiquitous Web documents. This event knowledge base is capable of describing each event with comprehensive semantics. By utilizing this base, the textual cues for a video can be significantly enriched. Furthermore, we introduce a two-view adaptive regression model, which explores the intrinsic correlation between the visual and textual cues of the videos to learn reliable classifiers. Extensive experiments on two real-world video data sets show the effectiveness of our proposed framework and prove that the event knowledge base indeed helps improve the performance of Web video event recognition.

Text messaging as an adjunct to a web-based intervention for college student alcohol use: A preliminary study.

PubMed

Tahaney, Kelli D; Palfai, Tibor P

2017-10-01

Brief, web-based motivational interventions have shown promising results for reducing alcohol use and associated harm among college students. However, findings regarding which alcohol use outcomes are impacted are mixed and effects tend to be small to moderate, with effect sizes decreasing over longer-term follow-up periods. As a result, these interventions may benefit from adjunctive strategies to bolster students' engagement with intervention material and to extend interventions beyond initial contacts into student's daily lives. This study tested the efficacy of text messaging as an adjunct to a web-based intervention for heavy episodic drinking college students. One-hundred and thirteen undergraduate student risky drinkers recruited from an introductory psychology class were randomly assigned to one of three conditions-assessment only (AO), web intervention (WI), and web intervention plus text messaging (WI+TXT). Heavy drinking episodes (HDEs), weekend quantity per occasion, and alcohol-related consequences were assessed at baseline and one month follow-up. Univariate analysis of covariance (ANCOVA) was used to assess the influence of condition assignment on 1-month outcomes, controlling for baseline variables. Planned contrasts showed that those in the WI+TXT condition showed significantly less weekend drinking than those in the AO and WI conditions. Although those in the WI+TXT condition showed significantly fewer HDEs compared to AO, it was not significantly different than the WI only condition. No differences were observed on alcohol-related problems. These findings provide partial support for the view that text messaging may be a useful adjunct to web-based interventions for reducing alcohol consumption among student drinkers. Copyright © 2017. Published by Elsevier Ltd.

The utility of web mining for epidemiological research: studying the association between parity and cancer risk.

PubMed

Tourassi, Georgia; Yoon, Hong-Jun; Xu, Songhua; Han, Xuesong

2016-05-01

The World Wide Web has emerged as a powerful data source for epidemiological studies related to infectious disease surveillance. However, its potential for cancer-related epidemiological discoveries is largely unexplored. Using advanced web crawling and tailored information extraction procedures, the authors automatically collected and analyzed the text content of 79 394 online obituary articles published between 1998 and 2014. The collected data included 51 911 cancer (27 330 breast; 9470 lung; 6496 pancreatic; 6342 ovarian; 2273 colon) and 27 483 non-cancer cases. With the derived information, the authors replicated a case-control study design to investigate the association between parity (i.e., childbearing) and cancer risk. Age-adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were calculated for each cancer type and compared to those reported in large-scale epidemiological studies. Parity was found to be associated with a significantly reduced risk of breast cancer (OR = 0.78, 95% CI, 0.75-0.82), pancreatic cancer (OR = 0.78, 95% CI, 0.72-0.83), colon cancer (OR = 0.67, 95% CI, 0.60-0.74), and ovarian cancer (OR = 0.58, 95% CI, 0.54-0.62). Marginal association was found for lung cancer risk (OR = 0.87, 95% CI, 0.81-0.92). The linear trend between increased parity and reduced cancer risk was dramatically more pronounced for breast and ovarian cancer than the other cancers included in the analysis. This large web-mining study on parity and cancer risk produced findings very similar to those reported with traditional observational studies. It may be used as a promising strategy to generate study hypotheses for guiding and prioritizing future epidemiological studies. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Agile text mining for the 2014 i2b2/UTHealth Cardiac risk factors challenge.

PubMed

Cormack, James; Nath, Chinmoy; Milward, David; Raja, Kalpana; Jonnalagadda, Siddhartha R

2015-12-01

This paper describes the use of an agile text mining platform (Linguamatics' Interactive Information Extraction Platform, I2E) to extract document-level cardiac risk factors in patient records as defined in the i2b2/UTHealth 2014 challenge. The approach uses a data-driven rule-based methodology with the addition of a simple supervised classifier. We demonstrate that agile text mining allows for rapid optimization of extraction strategies, while post-processing can leverage annotation guidelines, corpus statistics and logic inferred from the gold standard data. We also show how data imbalance in a training set affects performance. Evaluation of this approach on the test data gave an F-Score of 91.7%, one percent behind the top performing system. Copyright © 2015 Elsevier Inc. All rights reserved.

Influence on Learning of a Collaborative Learning Method Comprising the Jigsaw Method and Problem-based Learning (PBL).

PubMed

Takeda, Kayoko; Takahashi, Kiyoshi; Masukawa, Hiroyuki; Shimamori, Yoshimitsu

2017-01-01

Recently, the practice of active learning has spread, increasingly recognized as an essential component of academic studies. Classes incorporating small group discussion (SGD) are conducted at many universities. At present, assessments of the effectiveness of SGD have mostly involved evaluation by questionnaires conducted by teachers, by peer assessment, and by self-evaluation of students. However, qualitative data, such as open-ended descriptions by students, have not been widely evaluated. As a result, we have been unable to analyze the processes and methods involved in how students acquire knowledge in SGD. In recent years, due to advances in information and communication technology (ICT), text mining has enabled the analysis of qualitative data. We therefore investigated whether the introduction of a learning system comprising the jigsaw method and problem-based learning (PBL) would improve student attitudes toward learning; we did this by text mining analysis of the content of student reports. We found that by applying the jigsaw method before PBL, we were able to improve student attitudes toward learning and increase the depth of their understanding of the area of study as a result of working with others. The use of text mining to analyze qualitative data also allowed us to understand the processes and methods by which students acquired knowledge in SGD and also changes in students' understanding and performance based on improvements to the class. This finding suggests that the use of text mining to analyze qualitative data could enable teachers to evaluate the effectiveness of various methods employed to improve learning.

Teaching Science Methods Courses with Web-Enhanced Activities.

ERIC Educational Resources Information Center

Bodzin, Alec M.

Learning science in today's classroom does not have to be restricted to text-based curricular resources. Web sites present learners with a wide range of science activities in various formats ranging from text-only information to providing authentic real-time data sets and interactive simulations. This paper discusses reasons for using the Internet…

Algorithms and semantic infrastructure for mutation impact extraction and grounding.

PubMed

Laurila, Jonas B; Naderi, Nona; Witte, René; Riazanov, Alexandre; Kouznetsov, Alexandre; Baker, Christopher J O

2010-12-02

Mutation impact extraction is a hitherto unaccomplished task in state of the art mutation extraction systems. Protein mutations and their impacts on protein properties are hidden in scientific literature, making them poorly accessible for protein engineers and inaccessible for phenotype-prediction systems that currently depend on manually curated genomic variation databases. We present the first rule-based approach for the extraction of mutation impacts on protein properties, categorizing their directionality as positive, negative or neutral. Furthermore protein and mutation mentions are grounded to their respective UniProtKB IDs and selected protein properties, namely protein functions to concepts found in the Gene Ontology. The extracted entities are populated to an OWL-DL Mutation Impact ontology facilitating complex querying for mutation impacts using SPARQL. We illustrate retrieval of proteins and mutant sequences for a given direction of impact on specific protein properties. Moreover we provide programmatic access to the data through semantic web services using the SADI (Semantic Automated Discovery and Integration) framework. We address the problem of access to legacy mutation data in unstructured form through the creation of novel mutation impact extraction methods which are evaluated on a corpus of full-text articles on haloalkane dehalogenases, tagged by domain experts. Our approaches show state of the art levels of precision and recall for Mutation Grounding and respectable level of precision but lower recall for the task of Mutant-Impact relation extraction. The system is deployed using text mining and semantic web technologies with the goal of publishing to a broad spectrum of consumers.

Relax with CouchDB--into the non-relational DBMS era of bioinformatics.

PubMed

Manyam, Ganiraju; Payton, Michelle A; Roth, Jack A; Abruzzo, Lynne V; Coombes, Kevin R

2012-07-01

With the proliferation of high-throughput technologies, genome-level data analysis has become common in molecular biology. Bioinformaticians are developing extensive resources to annotate and mine biological features from high-throughput data. The underlying database management systems for most bioinformatics software are based on a relational model. Modern non-relational databases offer an alternative that has flexibility, scalability, and a non-rigid design schema. Moreover, with an accelerated development pace, non-relational databases like CouchDB can be ideal tools to construct bioinformatics utilities. We describe CouchDB by presenting three new bioinformatics resources: (a) geneSmash, which collates data from bioinformatics resources and provides automated gene-centric annotations, (b) drugBase, a database of drug-target interactions with a web interface powered by geneSmash, and (c) HapMap-CN, which provides a web interface to query copy number variations from three SNP-chip HapMap datasets. In addition to the web sites, all three systems can be accessed programmatically via web services. Copyright © 2012 Elsevier Inc. All rights reserved.

Automatic vs. manual curation of a multi-source chemical dictionary: the impact on text mining.

PubMed

Hettne, Kristina M; Williams, Antony J; van Mulligen, Erik M; Kleinjans, Jos; Tkachenko, Valery; Kors, Jan A

2010-03-23

Previously, we developed a combined dictionary dubbed Chemlist for the identification of small molecules and drugs in text based on a number of publicly available databases and tested it on an annotated corpus. To achieve an acceptable recall and precision we used a number of automatic and semi-automatic processing steps together with disambiguation rules. However, it remained to be investigated which impact an extensive manual curation of a multi-source chemical dictionary would have on chemical term identification in text. ChemSpider is a chemical database that has undergone extensive manual curation aimed at establishing valid chemical name-to-structure relationships. We acquired the component of ChemSpider containing only manually curated names and synonyms. Rule-based term filtering, semi-automatic manual curation, and disambiguation rules were applied. We tested the dictionary from ChemSpider on an annotated corpus and compared the results with those for the Chemlist dictionary. The ChemSpider dictionary of ca. 80 k names was only a 1/3 to a 1/4 the size of Chemlist at around 300 k. The ChemSpider dictionary had a precision of 0.43 and a recall of 0.19 before the application of filtering and disambiguation and a precision of 0.87 and a recall of 0.19 after filtering and disambiguation. The Chemlist dictionary had a precision of 0.20 and a recall of 0.47 before the application of filtering and disambiguation and a precision of 0.67 and a recall of 0.40 after filtering and disambiguation. We conclude the following: (1) The ChemSpider dictionary achieved the best precision but the Chemlist dictionary had a higher recall and the best F-score; (2) Rule-based filtering and disambiguation is necessary to achieve a high precision for both the automatically generated and the manually curated dictionary. ChemSpider is available as a web service at http://www.chemspider.com/ and the Chemlist dictionary is freely available as an XML file in Simple Knowledge Organization System format on the web at http://www.biosemantics.org/chemlist.

Automatic vs. manual curation of a multi-source chemical dictionary: the impact on text mining

PubMed Central

2010-01-01

Background Previously, we developed a combined dictionary dubbed Chemlist for the identification of small molecules and drugs in text based on a number of publicly available databases and tested it on an annotated corpus. To achieve an acceptable recall and precision we used a number of automatic and semi-automatic processing steps together with disambiguation rules. However, it remained to be investigated which impact an extensive manual curation of a multi-source chemical dictionary would have on chemical term identification in text. ChemSpider is a chemical database that has undergone extensive manual curation aimed at establishing valid chemical name-to-structure relationships. Results We acquired the component of ChemSpider containing only manually curated names and synonyms. Rule-based term filtering, semi-automatic manual curation, and disambiguation rules were applied. We tested the dictionary from ChemSpider on an annotated corpus and compared the results with those for the Chemlist dictionary. The ChemSpider dictionary of ca. 80 k names was only a 1/3 to a 1/4 the size of Chemlist at around 300 k. The ChemSpider dictionary had a precision of 0.43 and a recall of 0.19 before the application of filtering and disambiguation and a precision of 0.87 and a recall of 0.19 after filtering and disambiguation. The Chemlist dictionary had a precision of 0.20 and a recall of 0.47 before the application of filtering and disambiguation and a precision of 0.67 and a recall of 0.40 after filtering and disambiguation. Conclusions We conclude the following: (1) The ChemSpider dictionary achieved the best precision but the Chemlist dictionary had a higher recall and the best F-score; (2) Rule-based filtering and disambiguation is necessary to achieve a high precision for both the automatically generated and the manually curated dictionary. ChemSpider is available as a web service at http://www.chemspider.com/ and the Chemlist dictionary is freely available as an XML file in Simple Knowledge Organization System format on the web at http://www.biosemantics.org/chemlist. PMID:20331846

Finding novel relationships with integrated gene-gene association network analysis of Synechocystis sp. PCC 6803 using species-independent text-mining.

PubMed

Kreula, Sanna M; Kaewphan, Suwisa; Ginter, Filip; Jones, Patrik R

2018-01-01

The increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from 'reading the literature'. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced text-mining can be used to create information-rich networks even for less well studied species and apply it to generate an open-access gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. By merging the text-mining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rule-based algorithm (filter) was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already 'known', and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and filter to ( i ) discover novel candidate associations between different genes or proteins in the network, and ( ii ) rapidly evaluate the potential role of any one particular gene or protein. The full network is provided as an open-source resource.

Evaluation of a web based informatics system with data mining tools for predicting outcomes with quantitative imaging features in stroke rehabilitation clinical trials

NASA Astrophysics Data System (ADS)

Wang, Ximing; Kim, Bokkyu; Park, Ji Hoon; Wang, Erik; Forsyth, Sydney; Lim, Cody; Ravi, Ragini; Karibyan, Sarkis; Sanchez, Alexander; Liu, Brent

2017-03-01

Quantitative imaging biomarkers are used widely in clinical trials for tracking and evaluation of medical interventions. Previously, we have presented a web based informatics system utilizing quantitative imaging features for predicting outcomes in stroke rehabilitation clinical trials. The system integrates imaging features extraction tools and a web-based statistical analysis tool. The tools include a generalized linear mixed model(GLMM) that can investigate potential significance and correlation based on features extracted from clinical data and quantitative biomarkers. The imaging features extraction tools allow the user to collect imaging features and the GLMM module allows the user to select clinical data and imaging features such as stroke lesion characteristics from the database as regressors and regressands. This paper discusses the application scenario and evaluation results of the system in a stroke rehabilitation clinical trial. The system was utilized to manage clinical data and extract imaging biomarkers including stroke lesion volume, location and ventricle/brain ratio. The GLMM module was validated and the efficiency of data analysis was also evaluated.

The potential of text mining in data integration and network biology for plant research: a case study on Arabidopsis.

PubMed

Van Landeghem, Sofie; De Bodt, Stefanie; Drebert, Zuzanna J; Inzé, Dirk; Van de Peer, Yves

2013-03-01

Despite the availability of various data repositories for plant research, a wealth of information currently remains hidden within the biomolecular literature. Text mining provides the necessary means to retrieve these data through automated processing of texts. However, only recently has advanced text mining methodology been implemented with sufficient computational power to process texts at a large scale. In this study, we assess the potential of large-scale text mining for plant biology research in general and for network biology in particular using a state-of-the-art text mining system applied to all PubMed abstracts and PubMed Central full texts. We present extensive evaluation of the textual data for Arabidopsis thaliana, assessing the overall accuracy of this new resource for usage in plant network analyses. Furthermore, we combine text mining information with both protein-protein and regulatory interactions from experimental databases. Clusters of tightly connected genes are delineated from the resulting network, illustrating how such an integrative approach is essential to grasp the current knowledge available for Arabidopsis and to uncover gene information through guilt by association. All large-scale data sets, as well as the manually curated textual data, are made publicly available, hereby stimulating the application of text mining data in future plant biology studies.

Aspect level sentiment analysis using machine learning

NASA Astrophysics Data System (ADS)

Shubham, D.; Mithil, P.; Shobharani, Meesala; Sumathy, S.

2017-11-01

In modern world the development of web and smartphones increases the usage of online shopping. The overall feedback about product is generated with the help of sentiment analysis using text processing.Opinion mining or sentiment analysis is used to collect and categorized the reviews of product. The proposed system uses aspect leveldetection in which features are extracted from the datasets. The system performs pre-processing operation such as tokenization, part of speech and limitization on the data tofinds meaningful information which is used to detect the polarity level and assigns rating to product. The proposed model focuses on aspects to produces accurate result by avoiding the spam reviews.

Mining microarray data at NCBI's Gene Expression Omnibus (GEO)*.

PubMed

Barrett, Tanya; Edgar, Ron

2006-01-01

The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) has emerged as the leading fully public repository for gene expression data. This chapter describes how to use Web-based interfaces, applications, and graphics to effectively explore, visualize, and interpret the hundreds of microarray studies and millions of gene expression patterns stored in GEO. Data can be examined from both experiment-centric and gene-centric perspectives using user-friendly tools that do not require specialized expertise in microarray analysis or time-consuming download of massive data sets. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.

Experiences with Text Mining Large Collections of Unstructured Systems Development Artifacts at JPL

NASA Technical Reports Server (NTRS)

Port, Dan; Nikora, Allen; Hihn, Jairus; Huang, LiGuo

2011-01-01

Often repositories of systems engineering artifacts at NASA's Jet Propulsion Laboratory (JPL) are so large and poorly structured that they have outgrown our capability to effectively manually process their contents to extract useful information. Sophisticated text mining methods and tools seem a quick, low-effort approach to automating our limited manual efforts. Our experiences of exploring such methods mainly in three areas including historical risk analysis, defect identification based on requirements analysis, and over-time analysis of system anomalies at JPL, have shown that obtaining useful results requires substantial unanticipated efforts - from preprocessing the data to transforming the output for practical applications. We have not observed any quick 'wins' or realized benefit from short-term effort avoidance through automation in this area. Surprisingly we have realized a number of unexpected long-term benefits from the process of applying text mining to our repositories. This paper elaborates some of these benefits and our important lessons learned from the process of preparing and applying text mining to large unstructured system artifacts at JPL aiming to benefit future TM applications in similar problem domains and also in hope for being extended to broader areas of applications.

PedAM: a database for Pediatric Disease Annotation and Medicine.

PubMed

Jia, Jinmeng; An, Zhongxin; Ming, Yue; Guo, Yongli; Li, Wei; Li, Xin; Liang, Yunxiang; Guo, Dongming; Tai, Jun; Chen, Geng; Jin, Yaqiong; Liu, Zhimei; Ni, Xin; Shi, Tieliu

2018-01-04

There is a significant number of children around the world suffering from the consequence of the misdiagnosis and ineffective treatment for various diseases. To facilitate the precision medicine in pediatrics, a database namely the Pediatric Disease Annotations & Medicines (PedAM) has been built to standardize and classify pediatric diseases. The PedAM integrates both biomedical resources and clinical data from Electronic Medical Records to support the development of computational tools, by which enables robust data analysis and integration. It also uses disease-manifestation (D-M) integrated from existing biomedical ontologies as prior knowledge to automatically recognize text-mined, D-M-specific syntactic patterns from 774 514 full-text articles and 8 848 796 abstracts in MEDLINE. Additionally, disease connections based on phenotypes or genes can be visualized on the web page of PedAM. Currently, the PedAM contains standardized 8528 pediatric disease terms (4542 unique disease concepts and 3986 synonyms) with eight annotation fields for each disease, including definition synonyms, gene, symptom, cross-reference (Xref), human phenotypes and its corresponding phenotypes in the mouse. The database PedAM is freely accessible at http://www.unimd.org/pedam/. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Text mining in livestock animal science: introducing the potential of text mining to animal sciences.

PubMed

Sahadevan, S; Hofmann-Apitius, M; Schellander, K; Tesfaye, D; Fluck, J; Friedrich, C M

2012-10-01

In biological research, establishing the prior art by searching and collecting information already present in the domain has equal importance as the experiments done. To obtain a complete overview about the relevant knowledge, researchers mainly rely on 2 major information sources: i) various biological databases and ii) scientific publications in the field. The major difference between the 2 information sources is that information from databases is available, typically well structured and condensed. The information content in scientific literature is vastly unstructured; that is, dispersed among the many different sections of scientific text. The traditional method of information extraction from scientific literature occurs by generating a list of relevant publications in the field of interest and manually scanning these texts for relevant information, which is very time consuming. It is more than likely that in using this "classical" approach the researcher misses some relevant information mentioned in the literature or has to go through biological databases to extract further information. Text mining and named entity recognition methods have already been used in human genomics and related fields as a solution to this problem. These methods can process and extract information from large volumes of scientific text. Text mining is defined as the automatic extraction of previously unknown and potentially useful information from text. Named entity recognition (NER) is defined as the method of identifying named entities (names of real world objects; for example, gene/protein names, drugs, enzymes) in text. In animal sciences, text mining and related methods have been briefly used in murine genomics and associated fields, leaving behind other fields of animal sciences, such as livestock genomics. The aim of this work was to develop an information retrieval platform in the livestock domain focusing on livestock publications and the recognition of relevant data from cattle and pigs. For this purpose, the rather noncomprehensive resources of pig and cattle gene and protein terminologies were enriched with orthologue synonyms, integrated in the NER platform, ProMiner, which is successfully used in human genomics domain. Based on the performance tests done, the present system achieved a fair performance with precision 0.64, recall 0.74, and F(1) measure of 0.69 in a test scenario based on cattle literature.

SparkText: Biomedical Text Mining on Big Data Framework.

PubMed

Ye, Zhan; Tafti, Ahmad P; He, Karen Y; Wang, Kai; He, Max M

Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment. In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes. This study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research.

SparkText: Biomedical Text Mining on Big Data Framework

PubMed Central

He, Karen Y.; Wang, Kai

2016-01-01

Background Many new biomedical research articles are published every day, accumulating rich information, such as genetic variants, genes, diseases, and treatments. Rapid yet accurate text mining on large-scale scientific literature can discover novel knowledge to better understand human diseases and to improve the quality of disease diagnosis, prevention, and treatment. Results In this study, we designed and developed an efficient text mining framework called SparkText on a Big Data infrastructure, which is composed of Apache Spark data streaming and machine learning methods, combined with a Cassandra NoSQL database. To demonstrate its performance for classifying cancer types, we extracted information (e.g., breast, prostate, and lung cancers) from tens of thousands of articles downloaded from PubMed, and then employed Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression to build prediction models to mine the articles. The accuracy of predicting a cancer type by SVM using the 29,437 full-text articles was 93.81%. While competing text-mining tools took more than 11 hours, SparkText mined the dataset in approximately 6 minutes. Conclusions This study demonstrates the potential for mining large-scale scientific articles on a Big Data infrastructure, with real-time update from new articles published daily. SparkText can be extended to other areas of biomedical research. PMID:27685652

Gobe: an interactive, web-based tool for comparative genomic visualization.

PubMed

Pedersen, Brent S; Tang, Haibao; Freeling, Michael

2011-04-01

Gobe is a web-based tool for viewing comparative genomic data. It supports viewing multiple genomic regions simultaneously. Its simple text format and flash-based rendering make it an interactive, exploratory research tool. Gobe can be used without installation through our web service, or downloaded and customized with stylesheets and javascript callback functions. Gobe is a flash application that runs in all modern web-browsers. The full source-code, including that for the online web application is available under the MIT license at: http://github.com/brentp/gobe. Sample applications are hosted at http://try-gobe.appspot.com/ and http://synteny.cnr.berkeley.edu/gobe-app/.

Optimising text messaging to improve adherence to web-based smoking cessation treatment: a randomised control trial protocol.

PubMed

Graham, Amanda L; Jacobs, Megan A; Cohn, Amy M; Cha, Sarah; Abroms, Lorien C; Papandonatos, George D; Whittaker, Robyn

2016-03-30

Millions of smokers use the Internet for smoking cessation assistance each year; however, most smokers engage minimally with even the best designed websites. The ubiquity of mobile devices and their effectiveness in promoting adherence in other areas of health behaviour change make them a promising tool to address adherence in Internet smoking cessation interventions. Text messaging is used by most adults, and messages can proactively encourage use of a web-based intervention. Text messaging can also be integrated with an Internet intervention to facilitate the use of core Internet intervention components. We identified four aspects of a text message intervention that may enhance its effectiveness in promoting adherence to a web-based smoking cessation programme: personalisation, integration, dynamic tailoring and message intensity. Phase I will use a two-level full factorial design to test the impact of these four experimental features on adherence to a web-based intervention. The primary outcome is a composite metric of adherence that incorporates general utilisation metrics (eg, logins, page views) and specific feature utilisation shown to predict abstinence. Participants will be N=860 adult smokers who register on an established Internet cessation programme and enrol in its text message programme. Phase II will be a two-arm randomised trial to compare the efficacy of the web-based cessation programme alone and in conjunction with the optimised text messaging intervention on 30-day point prevalence abstinence at 9 months. Phase II participants will be N=600 adult smokers who register to use an established Internet cessation programme and enrol in text messaging. Secondary analyses will explore whether adherence mediates the effect of treatment condition on outcome. This protocol was approved by Chesapeake IRB. We will disseminate study results through peer-reviewed manuscripts and conference presentations related to the methods and design, outcomes and exploratory analyses. NCT02585206. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

TrawlerWeb: an online de novo motif discovery tool for next-generation sequencing datasets.

PubMed

Dang, Louis T; Tondl, Markus; Chiu, Man Ho H; Revote, Jerico; Paten, Benedict; Tano, Vincent; Tokolyi, Alex; Besse, Florence; Quaife-Ryan, Greg; Cumming, Helen; Drvodelic, Mark J; Eichenlaub, Michael P; Hallab, Jeannette C; Stolper, Julian S; Rossello, Fernando J; Bogoyevitch, Marie A; Jans, David A; Nim, Hieu T; Porrello, Enzo R; Hudson, James E; Ramialison, Mirana

2018-04-05

A strong focus of the post-genomic era is mining of the non-coding regulatory genome in order to unravel the function of regulatory elements that coordinate gene expression (Nat 489:57-74, 2012; Nat 507:462-70, 2014; Nat 507:455-61, 2014; Nat 518:317-30, 2015). Whole-genome approaches based on next-generation sequencing (NGS) have provided insight into the genomic location of regulatory elements throughout different cell types, organs and organisms. These technologies are now widespread and commonly used in laboratories from various fields of research. This highlights the need for fast and user-friendly software tools dedicated to extracting cis-regulatory information contained in these regulatory regions; for instance transcription factor binding site (TFBS) composition. Ideally, such tools should not require prior programming knowledge to ensure they are accessible for all users. We present TrawlerWeb, a web-based version of the Trawler_standalone tool (Nat Methods 4:563-5, 2007; Nat Protoc 5:323-34, 2010), to allow for the identification of enriched motifs in DNA sequences obtained from next-generation sequencing experiments in order to predict their TFBS composition. TrawlerWeb is designed for online queries with standard options common to web-based motif discovery tools. In addition, TrawlerWeb provides three unique new features: 1) TrawlerWeb allows the input of BED files directly generated from NGS experiments, 2) it automatically generates an input-matched biologically relevant background, and 3) it displays resulting conservation scores for each instance of the motif found in the input sequences, which assists the researcher in prioritising the motifs to validate experimentally. Finally, to date, this web-based version of Trawler_standalone remains the fastest online de novo motif discovery tool compared to other popular web-based software, while generating predictions with high accuracy. TrawlerWeb provides users with a fast, simple and easy-to-use web interface for de novo motif discovery. This will assist in rapidly analysing NGS datasets that are now being routinely generated. TrawlerWeb is freely available and accessible at: http://trawler.erc.monash.edu.au .

78 FR 2678 - Proposed Collection; Comment Request (60-Day FRN): The National Cancer Institute (NCI...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-01-14

... text message smoking cessation intervention designed for young adult smokers ages 18-29. The SmokefreeTXT program is a component of a larger series of eHealth/mHealth tobacco cessation intervention... using the bidirectional aspect of text- messaging service and a series of web-based surveys. All web...

A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts.

PubMed

Westergaard, David; Stærfeldt, Hans-Henrik; Tønsberg, Christian; Jensen, Lars Juhl; Brunak, Søren

2018-02-01

Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823-2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein-protein, disease-gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only.

A comprehensive and quantitative comparison of text-mining in 15 million full-text articles versus their corresponding abstracts

PubMed Central

Westergaard, David; Stærfeldt, Hans-Henrik

2018-01-01

Across academia and industry, text mining has become a popular strategy for keeping up with the rapid growth of the scientific literature. Text mining of the scientific literature has mostly been carried out on collections of abstracts, due to their availability. Here we present an analysis of 15 million English scientific full-text articles published during the period 1823–2016. We describe the development in article length and publication sub-topics during these nearly 250 years. We showcase the potential of text mining by extracting published protein–protein, disease–gene, and protein subcellular associations using a named entity recognition system, and quantitatively report on their accuracy using gold standard benchmark data sets. We subsequently compare the findings to corresponding results obtained on 16.5 million abstracts included in MEDLINE and show that text mining of full-text articles consistently outperforms using abstracts only. PMID:29447159

Digital Workflows for a 3d Semantic Representation of AN Ancient Mining Landscape

NASA Astrophysics Data System (ADS)

Hiebel, G.; Hanke, K.

2017-08-01

The ancient mining landscape of Schwaz/Brixlegg in the Tyrol, Austria witnessed mining from prehistoric times to modern times creating a first order cultural landscape when it comes to one of the most important inventions in human history: the production of metal. In 1991 a part of this landscape was lost due to an enormous landslide that reshaped part of the mountain. With our work we want to propose a digital workflow to create a 3D semantic representation of this ancient mining landscape with its mining structures to preserve it for posterity. First, we define a conceptual model to integrate the data. It is based on the CIDOC CRM ontology and CRMgeo for geometric data. To transform our information sources to a formal representation of the classes and properties of the ontology we applied semantic web technologies and created a knowledge graph in RDF (Resource Description Framework). Through the CRMgeo extension coordinate information of mining features can be integrated into the RDF graph and thus related to the detailed digital elevation model that may be visualized together with the mining structures using Geoinformation systems or 3D visualization tools. The RDF network of the triple store can be queried using the SPARQL query language. We created a snapshot of mining, settlement and burial sites in the Bronze Age. The results of the query were loaded into a Geoinformation system and a visualization of known bronze age sites related to mining, settlement and burial activities was created.

PubRunner: A light-weight framework for updating text mining results.

PubMed

Anekalla, Kishore R; Courneya, J P; Fiorini, Nicolas; Lever, Jake; Muchow, Michael; Busby, Ben

2017-01-01

Biomedical text mining promises to assist biologists in quickly navigating the combined knowledge in their domain. This would allow improved understanding of the complex interactions within biological systems and faster hypothesis generation. New biomedical research articles are published daily and text mining tools are only as good as the corpus from which they work. Many text mining tools are underused because their results are static and do not reflect the constantly expanding knowledge in the field. In order for biomedical text mining to become an indispensable tool used by researchers, this problem must be addressed. To this end, we present PubRunner, a framework for regularly running text mining tools on the latest publications. PubRunner is lightweight, simple to use, and can be integrated with an existing text mining tool. The workflow involves downloading the latest abstracts from PubMed, executing a user-defined tool, pushing the resulting data to a public FTP or Zenodo dataset, and publicizing the location of these results on the public PubRunner website. We illustrate the use of this tool by re-running the commonly used word2vec tool on the latest PubMed abstracts to generate up-to-date word vector representations for the biomedical domain. This shows a proof of concept that we hope will encourage text mining developers to build tools that truly will aid biologists in exploring the latest publications.

Systematic drug repositioning through mining adverse event data in ClinicalTrials.gov.

PubMed

Su, Eric Wen; Sanger, Todd M

2017-01-01

Drug repositioning (i.e., drug repurposing) is the process of discovering new uses for marketed drugs. Historically, such discoveries were serendipitous. However, the rapid growth in electronic clinical data and text mining tools makes it feasible to systematically identify drugs with the potential to be repurposed. Described here is a novel method of drug repositioning by mining ClinicalTrials.gov. The text mining tools I2E (Linguamatics) and PolyAnalyst (Megaputer) were utilized. An I2E query extracts "Serious Adverse Events" (SAE) data from randomized trials in ClinicalTrials.gov. Through a statistical algorithm, a PolyAnalyst workflow ranks the drugs where the treatment arm has fewer predefined SAEs than the control arm, indicating that potentially the drug is reducing the level of SAE. Hypotheses could then be generated for the new use of these drugs based on the predefined SAE that is indicative of disease (for example, cancer).

Using ontology network structure in text mining.

PubMed

Berndt, Donald J; McCart, James A; Luther, Stephen L

2010-11-13

Statistical text mining treats documents as bags of words, with a focus on term frequencies within documents and across document collections. Unlike natural language processing (NLP) techniques that rely on an engineered vocabulary or a full-featured ontology, statistical approaches do not make use of domain-specific knowledge. The freedom from biases can be an advantage, but at the cost of ignoring potentially valuable knowledge. The approach proposed here investigates a hybrid strategy based on computing graph measures of term importance over an entire ontology and injecting the measures into the statistical text mining process. As a starting point, we adapt existing search engine algorithms such as PageRank and HITS to determine term importance within an ontology graph. The graph-theoretic approach is evaluated using a smoking data set from the i2b2 National Center for Biomedical Computing, cast as a simple binary classification task for categorizing smoking-related documents, demonstrating consistent improvements in accuracy.

Text mining for the biocuration workflow

PubMed Central

Hirschman, Lynette; Burns, Gully A. P. C; Krallinger, Martin; Arighi, Cecilia; Cohen, K. Bretonnel; Valencia, Alfonso; Wu, Cathy H.; Chatr-Aryamontri, Andrew; Dowell, Karen G.; Huala, Eva; Lourenço, Anália; Nash, Robert; Veuthey, Anne-Lise; Wiegers, Thomas; Winter, Andrew G.

2012-01-01

Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on ‘Text Mining for the BioCuration Workflow’ at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community. PMID:22513129

Text mining for the biocuration workflow.

PubMed

Hirschman, Lynette; Burns, Gully A P C; Krallinger, Martin; Arighi, Cecilia; Cohen, K Bretonnel; Valencia, Alfonso; Wu, Cathy H; Chatr-Aryamontri, Andrew; Dowell, Karen G; Huala, Eva; Lourenço, Anália; Nash, Robert; Veuthey, Anne-Lise; Wiegers, Thomas; Winter, Andrew G

2012-01-01

Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on 'Text Mining for the BioCuration Workflow' at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community.

The Potential of Text Mining in Data Integration and Network Biology for Plant Research: A Case Study on Arabidopsis[C][W

PubMed Central

Van Landeghem, Sofie; De Bodt, Stefanie; Drebert, Zuzanna J.; Inzé, Dirk; Van de Peer, Yves

2013-01-01

Despite the availability of various data repositories for plant research, a wealth of information currently remains hidden within the biomolecular literature. Text mining provides the necessary means to retrieve these data through automated processing of texts. However, only recently has advanced text mining methodology been implemented with sufficient computational power to process texts at a large scale. In this study, we assess the potential of large-scale text mining for plant biology research in general and for network biology in particular using a state-of-the-art text mining system applied to all PubMed abstracts and PubMed Central full texts. We present extensive evaluation of the textual data for Arabidopsis thaliana, assessing the overall accuracy of this new resource for usage in plant network analyses. Furthermore, we combine text mining information with both protein–protein and regulatory interactions from experimental databases. Clusters of tightly connected genes are delineated from the resulting network, illustrating how such an integrative approach is essential to grasp the current knowledge available for Arabidopsis and to uncover gene information through guilt by association. All large-scale data sets, as well as the manually curated textual data, are made publicly available, hereby stimulating the application of text mining data in future plant biology studies. PMID:23532071

Mining Clinicians' Electronic Documentation to Identify Heart Failure Patients with Ineffective Self-Management: A Pilot Text-Mining Study.

PubMed

Topaz, Maxim; Radhakrishnan, Kavita; Lei, Victor; Zhou, Li

2016-01-01

Effective self-management can decrease up to 50% of heart failure hospitalizations. Unfortunately, self-management by patients with heart failure remains poor. This pilot study aimed to explore the use of text-mining to identify heart failure patients with ineffective self-management. We first built a comprehensive self-management vocabulary based on the literature and clinical notes review. We then randomly selected 545 heart failure patients treated within Partners Healthcare hospitals (Boston, MA, USA) and conducted a regular expression search with the compiled vocabulary within 43,107 interdisciplinary clinical notes of these patients. We found that 38.2% (n = 208) patients had documentation of ineffective heart failure self-management in the domains of poor diet adherence (28.4%), missed medical encounters (26.4%) poor medication adherence (20.2%) and non-specified self-management issues (e.g., "compliance issues", 34.6%). We showed the feasibility of using text-mining to identify patients with ineffective self-management. More natural language processing algorithms are needed to help busy clinicians identify these patients.

Frontiers of biomedical text mining: current progress

PubMed Central

Zweigenbaum, Pierre; Demner-Fushman, Dina; Yu, Hong; Cohen, Kevin B.

2008-01-01

It is now almost 15 years since the publication of the first paper on text mining in the genomics domain, and decades since the first paper on text mining in the medical domain. Enormous progress has been made in the areas of information retrieval, evaluation methodologies and resource construction. Some problems, such as abbreviation-handling, can essentially be considered solved problems, and others, such as identification of gene mentions in text, seem likely to be solved soon. However, a number of problems at the frontiers of biomedical text mining continue to present interesting challenges and opportunities for great improvements and interesting research. In this article we review the current state of the art in biomedical text mining or ‘BioNLP’ in general, focusing primarily on papers published within the past year. PMID:17977867

The efficacy of a Web-based counterargument tutor.

PubMed

Wolfe, Christopher R; Britt, M Anne; Petrovic, Melina; Albrecht, Michael; Kopp, Kristopher

2009-08-01

In two experiments, we developed and tested an interactive Web-based tutor to help students identify and evaluate counterarguments. In Experiment 1, we determined the extent to which high- and low-argumentationability participants were able to identify counterarguments. We tested the effectiveness of having participants read didactic text regarding counterarguments and highlight claims. Both preparations had some positive effects that were often limited to high-ability participants. The Web-based intervention included interactive exercises on identifying and using counterarguments. Web-based presentation was state driven, using a Java Server Pages page. As participants progressively identified argument elements, the page changed display state and presented feedback by checking what the user clicked against elements that we had coded in XML beforehand. Instructions and feedback strings were indexed by state, so that changing state selected new text to display. In Experiment 2, the tutor was effective in teaching participants to identify counterarguments, recognize responses, and determine whether counterarguments were rebutted, dismissed, or conceded.

Designing and Managing Your Digital Library.

ERIC Educational Resources Information Center

Guenther, Kim

2000-01-01

Discusses digital libraries and Web site design issues. Highlights include accessibility issues, including standards, markup languages like HTML and XML, and metadata; building virtual communities; the use of Web portals for customized delivery of information; quality assurance tools, including data mining; and determining user needs, including…

A Dynamic Recommender System for Improved Web Usage Mining and CRM Using Swarm Intelligence.

PubMed

Alphy, Anna; Prabakaran, S

2015-01-01

In modern days, to enrich e-business, the websites are personalized for each user by understanding their interests and behavior. The main challenges of online usage data are information overload and their dynamic nature. In this paper, to address these issues, a WebBluegillRecom-annealing dynamic recommender system that uses web usage mining techniques in tandem with software agents developed for providing dynamic recommendations to users that can be used for customizing a website is proposed. The proposed WebBluegillRecom-annealing dynamic recommender uses swarm intelligence from the foraging behavior of a bluegill fish. It overcomes the information overload by handling dynamic behaviors of users. Our dynamic recommender system was compared against traditional collaborative filtering systems. The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems. Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations.

A Dynamic Recommender System for Improved Web Usage Mining and CRM Using Swarm Intelligence

PubMed Central

Alphy, Anna; Prabakaran, S.

2015-01-01

In modern days, to enrich e-business, the websites are personalized for each user by understanding their interests and behavior. The main challenges of online usage data are information overload and their dynamic nature. In this paper, to address these issues, a WebBluegillRecom-annealing dynamic recommender system that uses web usage mining techniques in tandem with software agents developed for providing dynamic recommendations to users that can be used for customizing a website is proposed. The proposed WebBluegillRecom-annealing dynamic recommender uses swarm intelligence from the foraging behavior of a bluegill fish. It overcomes the information overload by handling dynamic behaviors of users. Our dynamic recommender system was compared against traditional collaborative filtering systems. The results show that the proposed system has higher precision, coverage, F1 measure, and scalability than the traditional collaborative filtering systems. Moreover, the recommendations given by our system overcome the overspecialization problem by including variety in recommendations. PMID:26229978

Studies on medicinal herbs for cognitive enhancement based on the text mining of Dongeuibogam and preliminary evaluation of its effects.

PubMed

Pak, Malk Eun; Kim, Yu Ri; Kim, Ha Neui; Ahn, Sung Min; Shin, Hwa Kyoung; Baek, Jin Ung; Choi, Byung Tae

2016-02-17

In literature on Korean medicine, Dongeuibogam (Treasured Mirror of Eastern Medicine), published in 1613, represents the overall results of the traditional medicines of North-East Asia based on prior medicinal literature of this region. We utilized this medicinal literature by text mining to establish a list of candidate herbs for cognitive enhancement in the elderly and then performed an evaluation of their effects. Text mining was performed for selection of candidate herbs. Cell viability was determined in HT22 hippocampal cells and immunohistochemistry and behavioral analysis was performed in a kainic acid (KA) mice model in order to observe alterations of hippocampal cells and cognition. Twenty four herbs for cognitive enhancement in the elderly were selected by text mining of Dongeuibogam. In HT22 cells, pretreatment with 3 candidate herbs resulted in significantly reduced glutamate-induced cell death. Panax ginseng was the most neuroprotective herb against glutamate-induced cell death. In the hippocampus of a KA mice model, pretreatment with 11 candidate herbs resulted in suppression of caspase-3 expression. Treatment with 7 candidate herbs resulted in significantly enhanced expression levels of phosphorylated cAMP response element binding protein. Number of proliferated cells indicated by BrdU labeling was increased by treatment with 10 candidate herbs. Schisandra chinensis was the most effective herb against cell death and proliferation of progenitor cells and Rehmannia glutinosa in neuroprotection in the hippocampus of a KA mice model. In a KA mice model, we confirmed improved spatial and short memory by treatment with the 3 most effective candidate herbs and these recovered functions were involved in a higher number of newly formed neurons from progenitor cells in the hippocampus. These established herbs and their combinations identified by text-mining technique and evaluation for effectiveness may have value in further experimental and clinical applications for cognitive enhancement in the elderly. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Textual and visual content-based anti-phishing: a Bayesian approach.

PubMed

Zhang, Haijun; Liu, Gang; Chow, Tommy W S; Liu, Wenyin

2011-10-01

A novel framework using a Bayesian approach for content-based phishing web page detection is presented. Our model takes into account textual and visual contents to measure the similarity between the protected web page and suspicious web pages. A text classifier, an image classifier, and an algorithm fusing the results from classifiers are introduced. An outstanding feature of this paper is the exploration of a Bayesian model to estimate the matching threshold. This is required in the classifier for determining the class of the web page and identifying whether the web page is phishing or not. In the text classifier, the naive Bayes rule is used to calculate the probability that a web page is phishing. In the image classifier, the earth mover's distance is employed to measure the visual similarity, and our Bayesian model is designed to determine the threshold. In the data fusion algorithm, the Bayes theory is used to synthesize the classification results from textual and visual content. The effectiveness of our proposed approach was examined in a large-scale dataset collected from real phishing cases. Experimental results demonstrated that the text classifier and the image classifier we designed deliver promising results, the fusion algorithm outperforms either of the individual classifiers, and our model can be adapted to different phishing cases. © 2011 IEEE

LIBP-Pred: web server for lipid binding proteins using structural network parameters; PDB mining of human cancer biomarkers and drug targets in parasites and bacteria.

PubMed

González-Díaz, Humberto; Munteanu, Cristian R; Postelnicu, Lucian; Prado-Prado, Francisco; Gestal, Marcos; Pazos, Alejandro

2012-03-01

Lipid-Binding Proteins (LIBPs) or Fatty Acid-Binding Proteins (FABPs) play an important role in many diseases such as different types of cancer, kidney injury, atherosclerosis, diabetes, intestinal ischemia and parasitic infections. Thus, the computational methods that can predict LIBPs based on 3D structure parameters became a goal of major importance for drug-target discovery, vaccine design and biomarker selection. In addition, the Protein Data Bank (PDB) contains 3000+ protein 3D structures with unknown function. This list, as well as new experimental outcomes in proteomics research, is a very interesting source to discover relevant proteins, including LIBPs. However, to the best of our knowledge, there are no general models to predict new LIBPs based on 3D structures. We developed new Quantitative Structure-Activity Relationship (QSAR) models based on 3D electrostatic parameters of 1801 different proteins, including 801 LIBPs. We calculated these electrostatic parameters with the MARCH-INSIDE software and they correspond to the entire protein or to specific protein regions named core, inner, middle, and surface. We used these parameters as inputs to develop a simple Linear Discriminant Analysis (LDA) classifier to discriminate 3D structure of LIBPs from other proteins. We implemented this predictor in the web server named LIBP-Pred, freely available at , along with other important web servers of the Bio-AIMS portal. The users can carry out an automatic retrieval of protein structures from PDB or upload their custom protein structural models from their disk created with LOMETS server. We demonstrated the PDB mining option performing a predictive study of 2000+ proteins with unknown function. Interesting results regarding the discovery of new Cancer Biomarkers in humans or drug targets in parasites have been discussed here in this sense.

Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure.

PubMed

P Tafti, Ahmad; Badger, Jonathan; LaRose, Eric; Shirzadi, Ehsan; Mahnke, Andrea; Mayer, John; Ye, Zhan; Page, David; Peissig, Peggy

2017-12-08

The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs. The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs. We analyzed the following two data sources: (1) biomedical articles and (2) health-related social media blog posts. We developed an intelligent and scalable text mining solution on big data infrastructures composed of Apache Spark, natural language processing, and machine learning. This was combined with an Elasticsearch No-SQL distributed database to explore and visualize ADEs. The accuracy, precision, recall, and area under receiver operating characteristic of the system were 92.7%, 93.6%, 93.0%, and 0.905, respectively, and showed better results in comparison with traditional approaches in the literature. This work not only detected and classified ADE sentences from big data biomedical literature but also scientifically visualized ADE interactions. To the best of our knowledge, this work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media. This contribution illustrates possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis. ©Ahmad P Tafti, Jonathan Badger, Eric LaRose, Ehsan Shirzadi, Andrea Mahnke, John Mayer, Zhan Ye, David Page, Peggy Peissig. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 08.12.2017.

Text mining and expert curation to develop a database on psychiatric diseases and their genes

PubMed Central

Gutiérrez-Sacristán, Alba; Bravo, Àlex; Portero-Tresserra, Marta; Valverde, Olga; Armario, Antonio; Blanco-Gandía, M.C.; Farré, Adriana; Fernández-Ibarrondo, Lierni; Fonseca, Francina; Giraldo, Jesús; Leis, Angela; Mané, Anna; Mayer, M.A.; Montagud-Romero, Sandra; Nadal, Roser; Ortiz, Jordi; Pavon, Francisco Javier; Perez, Ezequiel Jesús; Rodríguez-Arias, Marta; Serrano, Antonia; Torrens, Marta; Warnault, Vincent; Sanz, Ferran

2017-01-01

Abstract Psychiatric disorders constitute one of the main causes of disability worldwide. During the past years, considerable research has been conducted on the genetic architecture of such diseases, although little understanding of their etiology has been achieved. The difficulty to access up-to-date, relevant genotype-phenotype information has hampered the application of this wealth of knowledge to translational research and clinical practice in order to improve diagnosis and treatment of psychiatric patients. PsyGeNET (http://www.psygenet.org/) has been developed with the aim of supporting research on the genetic architecture of psychiatric diseases, by providing integrated and structured accessibility to their genotype–phenotype association data, together with analysis and visualization tools. In this article, we describe the protocol developed for the sustainable update of this knowledge resource. It includes the recruitment of a team of domain experts in order to perform the curation of the data extracted by text mining. Annotation guidelines and a web-based annotation tool were developed to support the curators’ tasks. A curation workflow was designed including a pilot phase and two rounds of curation and analysis phases. Negative evidence from the literature on gene–disease associations (GDAs) was taken into account in the curation process. We report the results of the application of this workflow to the curation of GDAs for PsyGeNET, including the analysis of the inter-annotator agreement and suggest this model as a suitable approach for the sustainable development and update of knowledge resources. Database URL: http://www.psygenet.org PsyGeNET corpus: http://www.psygenet.org/ds/PsyGeNET/results/psygenetCorpus.tar PMID:29220439

Automated detection of follow-up appointments using text mining of discharge records.

PubMed

Ruud, Kari L; Johnson, Matthew G; Liesinger, Juliette T; Grafft, Carrie A; Naessens, James M

2010-06-01

To determine whether text mining can accurately detect specific follow-up appointment criteria in free-text hospital discharge records. Cross-sectional study. Mayo Clinic Rochester hospitals. Inpatients discharged from general medicine services in 2006 (n = 6481). Textual hospital dismissal summaries were manually reviewed to determine whether the records contained specific follow-up appointment arrangement elements: date, time and either physician or location for an appointment. The data set was evaluated for the same criteria using SAS Text Miner software. The two assessments were compared to determine the accuracy of text mining for detecting records containing follow-up appointment arrangements. Agreement of text-mined appointment findings with gold standard (manual abstraction) including sensitivity, specificity, positive predictive and negative predictive values (PPV and NPV). About 55.2% (3576) of discharge records contained all criteria for follow-up appointment arrangements according to the manual review, 3.2% (113) of which were missed through text mining. Text mining incorrectly identified 3.7% (107) follow-up appointments that were not considered valid through manual review. Therefore, the text mining analysis concurred with the manual review in 96.6% of the appointment findings. Overall sensitivity and specificity were 96.8 and 96.3%, respectively; and PPV and NPV were 97.0 and 96.1%, respectively. of individual appointment criteria resulted in accuracy rates of 93.5% for date, 97.4% for time, 97.5% for physician and 82.9% for location. Text mining of unstructured hospital dismissal summaries can accurately detect documentation of follow-up appointment arrangement elements, thus saving considerable resources for performance assessment and quality-related research.

Collaborative Biomechanics Data Network (CBDN): Promoting Human Protection and Performance in Hazardous Environments Through Modeling and Data Mining of Human-Centric Data Bases

DTIC Science & Technology

2011-09-01

unique data requirements. For an excellent primer on the CBDN process see Keller and Plaga [2010]. This report shows an example of a database, in...data [Buhrman, Plaga , Cheng, Mosher, 2001]. 2.1.2.2 Web application development All technologies are based upon and run on top of the .NET...builds). Better manikin design leads to better assessment of personnel equipment under acceleration [such as helmet systems, see Plaga and Boehmer

Influence of Web Based Cooperative Learning Strategy and Achiever Motivation on Student Study Outcome

ERIC Educational Resources Information Center

Hariadi, Bambang; Wurijanto, Tutut

2016-01-01

The research aimed at examining the effect of instructional strategy (web-based STAD and text-based STAD) and achiever motivation toward student learning outcomes. The research implied quasi-experimental design with nonequivalent control group factorial version. The subjects were undergraduate students of Information Systems of academic year…

Finding novel relationships with integrated gene-gene association network analysis of Synechocystis sp. PCC 6803 using species-independent text-mining

PubMed Central

Kreula, Sanna M.; Kaewphan, Suwisa; Ginter, Filip

2018-01-01

The increasing move towards open access full-text scientific literature enhances our ability to utilize advanced text-mining methods to construct information-rich networks that no human will be able to grasp simply from ‘reading the literature’. The utility of text-mining for well-studied species is obvious though the utility for less studied species, or those with no prior track-record at all, is not clear. Here we present a concept for how advanced text-mining can be used to create information-rich networks even for less well studied species and apply it to generate an open-access gene-gene association network resource for Synechocystis sp. PCC 6803, a representative model organism for cyanobacteria and first case-study for the methodology. By merging the text-mining network with networks generated from species-specific experimental data, network integration was used to enhance the accuracy of predicting novel interactions that are biologically relevant. A rule-based algorithm (filter) was constructed in order to automate the search for novel candidate genes with a high degree of likely association to known target genes by (1) ignoring established relationships from the existing literature, as they are already ‘known’, and (2) demanding multiple independent evidences for every novel and potentially relevant relationship. Using selected case studies, we demonstrate the utility of the network resource and filter to (i) discover novel candidate associations between different genes or proteins in the network, and (ii) rapidly evaluate the potential role of any one particular gene or protein. The full network is provided as an open-source resource. PMID:29844966

Grist : grid-based data mining for astronomy

NASA Technical Reports Server (NTRS)

Jacob, Joseph C.; Katz, Daniel S.; Miller, Craig D.; Walia, Harshpreet; Williams, Roy; Djorgovski, S. George; Graham, Matthew J.; Mahabal, Ashish; Babu, Jogesh; Berk, Daniel E. Vanden;

Enriching semantic knowledge bases for opinion mining in big data applications.

PubMed

Weichselbraun, A; Gindl, S; Scharl, A

2014-10-01

This paper presents a novel method for contextualizing and enriching large semantic knowledge bases for opinion mining with a focus on Web intelligence platforms and other high-throughput big data applications. The method is not only applicable to traditional sentiment lexicons, but also to more comprehensive, multi-dimensional affective resources such as SenticNet. It comprises the following steps: (i) identify ambiguous sentiment terms, (ii) provide context information extracted from a domain-specific training corpus, and (iii) ground this contextual information to structured background knowledge sources such as ConceptNet and WordNet. A quantitative evaluation shows a significant improvement when using an enriched version of SenticNet for polarity classification. Crowdsourced gold standard data in conjunction with a qualitative evaluation sheds light on the strengths and weaknesses of the concept grounding, and on the quality of the enrichment process.

Grist: Grid-based Data Mining for Astronomy

NASA Astrophysics Data System (ADS)

Jacob, J. C.; Katz, D. S.; Miller, C. D.; Walia, H.; Williams, R. D.; Djorgovski, S. G.; Graham, M. J.; Mahabal, A. A.; Babu, G. J.; vanden Berk, D. E.; Nichol, R.

2005-12-01

The Grist project is developing a grid-technology based system as a research environment for astronomy with massive and complex datasets. This knowledge extraction system will consist of a library of distributed grid services controlled by a workflow system, compliant with standards emerging from the grid computing, web services, and virtual observatory communities. This new technology is being used to find high redshift quasars, study peculiar variable objects, search for transients in real time, and fit SDSS QSO spectra to measure black hole masses. Grist services are also a component of the ``hyperatlas'' project to serve high-resolution multi-wavelength imagery over the Internet. In support of these science and outreach objectives, the Grist framework will provide the enabling fabric to tie together distributed grid services in the areas of data access, federation, mining, subsetting, source extraction, image mosaicking, statistics, and visualization.

Graph-based biomedical text summarization: An itemset mining and sentence clustering approach.

PubMed

Nasr Azadani, Mozhgan; Ghadiri, Nasser; Davoodijam, Ensieh

2018-06-12

Automatic text summarization offers an efficient solution to access the ever-growing amounts of both scientific and clinical literature in the biomedical domain by summarizing the source documents while maintaining their most informative contents. In this paper, we propose a novel graph-based summarization method that takes advantage of the domain-specific knowledge and a well-established data mining technique called frequent itemset mining. Our summarizer exploits the Unified Medical Language System (UMLS) to construct a concept-based model of the source document and mapping the document to the concepts. Then, it discovers frequent itemsets to take the correlations among multiple concepts into account. The method uses these correlations to propose a similarity function based on which a represented graph is constructed. The summarizer then employs a minimum spanning tree based clustering algorithm to discover various subthemes of the document. Eventually, it generates the final summary by selecting the most informative and relative sentences from all subthemes within the text. We perform an automatic evaluation over a large number of summaries using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results demonstrate that the proposed summarization system outperforms various baselines and benchmark approaches. The carried out research suggests that the incorporation of domain-specific knowledge and frequent itemset mining equips the summarization system in a better way to address the informativeness measurement of the sentences. Moreover, clustering the graph nodes (sentences) can enable the summarizer to target different main subthemes of a source document efficiently. The evaluation results show that the proposed approach can significantly improve the performance of the summarization systems in the biomedical domain. Copyright © 2018. Published by Elsevier Inc.

PubMedPortable: A Framework for Supporting the Development of Text Mining Applications.

PubMed

Döring, Kersten; Grüning, Björn A; Telukunta, Kiran K; Thomas, Philippe; Günther, Stefan

2016-01-01

Information extraction from biomedical literature is continuously growing in scope and importance. Many tools exist that perform named entity recognition, e.g. of proteins, chemical compounds, and diseases. Furthermore, several approaches deal with the extraction of relations between identified entities. The BioCreative community supports these developments with yearly open challenges, which led to a standardised XML text annotation format called BioC. PubMed provides access to the largest open biomedical literature repository, but there is no unified way of connecting its data to natural language processing tools. Therefore, an appropriate data environment is needed as a basis to combine different software solutions and to develop customised text mining applications. PubMedPortable builds a relational database and a full text index on PubMed citations. It can be applied either to the complete PubMed data set or an arbitrary subset of downloaded PubMed XML files. The software provides the infrastructure to combine stand-alone applications by exporting different data formats, e.g. BioC. The presented workflows show how to use PubMedPortable to retrieve, store, and analyse a disease-specific data set. The provided use cases are well documented in the PubMedPortable wiki. The open-source software library is small, easy to use, and scalable to the user's system requirements. It is freely available for Linux on the web at https://github.com/KerstenDoering/PubMedPortable and for other operating systems as a virtual container. The approach was tested extensively and applied successfully in several projects.

PubMedPortable: A Framework for Supporting the Development of Text Mining Applications

PubMed Central

Döring, Kersten; Grüning, Björn A.; Telukunta, Kiran K.; Thomas, Philippe; Günther, Stefan

2016-01-01

Information extraction from biomedical literature is continuously growing in scope and importance. Many tools exist that perform named entity recognition, e.g. of proteins, chemical compounds, and diseases. Furthermore, several approaches deal with the extraction of relations between identified entities. The BioCreative community supports these developments with yearly open challenges, which led to a standardised XML text annotation format called BioC. PubMed provides access to the largest open biomedical literature repository, but there is no unified way of connecting its data to natural language processing tools. Therefore, an appropriate data environment is needed as a basis to combine different software solutions and to develop customised text mining applications. PubMedPortable builds a relational database and a full text index on PubMed citations. It can be applied either to the complete PubMed data set or an arbitrary subset of downloaded PubMed XML files. The software provides the infrastructure to combine stand-alone applications by exporting different data formats, e.g. BioC. The presented workflows show how to use PubMedPortable to retrieve, store, and analyse a disease-specific data set. The provided use cases are well documented in the PubMedPortable wiki. The open-source software library is small, easy to use, and scalable to the user’s system requirements. It is freely available for Linux on the web at https://github.com/KerstenDoering/PubMedPortable and for other operating systems as a virtual container. The approach was tested extensively and applied successfully in several projects. PMID:27706202

Data Mining: The Art of Automated Knowledge Extraction

NASA Astrophysics Data System (ADS)

Karimabadi, H.; Sipes, T.

2012-12-01

Data mining algorithms are used routinely in a wide variety of fields and they are gaining adoption in sciences. The realities of real world data analysis are that (a) data has flaws, and (b) the models and assumptions that we bring to the data are inevitably flawed, and/or biased and misspecified in some way. Data mining can improve data analysis by detecting anomalies in the data, check for consistency of the user model assumptions, and decipher complex patterns and relationships that would not be possible otherwise. The common form of data collected from in situ spacecraft measurements is multi-variate time series which represents one of the most challenging problems in data mining. We have successfully developed algorithms to deal with such data and have extended the algorithms to handle streaming data. In this talk, we illustrate the utility of our algorithms through several examples including automated detection of reconnection exhausts in the solar wind and flux ropes in the magnetotail. We also show examples from successful applications of our technique to analysis of 3D kinetic simulations. With an eye to the future, we provide an overview of our upcoming plans that include collaborative data mining, expert outsourcing data mining, computer vision for image analysis, among others. Finally, we discuss the integration of data mining algorithms with web-based services such as VxOs and other Heliophysics data centers and the resulting capabilities that it would enable.

OSCAR4: a flexible architecture for chemical text-mining.

PubMed

Jessop, David M; Adams, Sam E; Willighagen, Egon L; Hawizy, Lezan; Murray-Rust, Peter

2011-10-14

The Open-Source Chemistry Analysis Routines (OSCAR) software, a toolkit for the recognition of named entities and data in chemistry publications, has been developed since 2002. Recent work has resulted in the separation of the core OSCAR functionality and its release as the OSCAR4 library. This library features a modular API (based on reduction of surface coupling) that permits client programmers to easily incorporate it into external applications. OSCAR4 offers a domain-independent architecture upon which chemistry specific text-mining tools can be built, and its development and usage are discussed.

PathText: a text mining integrator for biological pathway visualizations

PubMed Central

Kemper, Brian; Matsuzaki, Takuya; Matsuoka, Yukiko; Tsuruoka, Yoshimasa; Kitano, Hiroaki; Ananiadou, Sophia; Tsujii, Jun'ichi

2010-01-01

Motivation: Metabolic and signaling pathways are an increasingly important part of organizing knowledge in systems biology. They serve to integrate collective interpretations of facts scattered throughout literature. Biologists construct a pathway by reading a large number of articles and interpreting them as a consistent network, but most of the models constructed currently lack direct links to those articles. Biologists who want to check the original articles have to spend substantial amounts of time to collect relevant articles and identify the sections relevant to the pathway. Furthermore, with the scientific literature expanding by several thousand papers per week, keeping a model relevant requires a continuous curation effort. In this article, we present a system designed to integrate a pathway visualizer, text mining systems and annotation tools into a seamless environment. This will enable biologists to freely move between parts of a pathway and relevant sections of articles, as well as identify relevant papers from large text bases. The system, PathText, is developed by Systems Biology Institute, Okinawa Institute of Science and Technology, National Centre for Text Mining (University of Manchester) and the University of Tokyo, and is being used by groups of biologists from these locations. Contact: brian@monrovian.com. PMID:20529930

In silico mining of putative microsatellite markers from whole genome sequence of water buffalo (Bubalus bubalis) and development of first BuffSatDB

PubMed Central

2013-01-01

Background Though India has sequenced water buffalo genome but its draft assembly is based on cattle genome BTau 4.0, thus de novo chromosome wise assembly is a major pending issue for global community. The existing radiation hybrid of buffalo and these reported STR can be used further in final gap plugging and “finishing” expected in de novo genome assembly. QTL and gene mapping needs mining of putative STR from buffalo genome at equal interval on each and every chromosome. Such markers have potential role in improvement of desirable characteristics, such as high milk yields, resistance to diseases, high growth rate. The STR mining from whole genome and development of user friendly database is yet to be done to reap the benefit of whole genome sequence. Description By in silico microsatellite mining of whole genome, we have developed first STR database of water buffalo, BuffSatDb (Buffalo MicroSatellite Database (http://cabindb.iasri.res.in/buffsatdb/) which is a web based relational database of 910529 microsatellite markers, developed using PHP and MySQL database. Microsatellite markers have been generated using MIcroSAtellite tool. It is simple and systematic web based search for customised retrieval of chromosome wise and genome-wide microsatellites. Search has been enabled based on chromosomes, motif type (mono-hexa), repeat motif and repeat kind (simple and composite). The search may be customised by limiting location of STR on chromosome as well as number of markers in that range. This is a novel approach and not been implemented in any of the existing marker database. This database has been further appended with Primer3 for primer designing of the selected markers enabling researcher to select markers of choice at desired interval over the chromosome. The unique add-on of degenerate bases further helps in resolving presence of degenerate bases in current buffalo assembly. Conclusion Being first buffalo STR database in the world , this would not only pave the way in resolving current assembly problem but shall be of immense use for global community in QTL/gene mapping critically required to increase knowledge in the endeavour to increase buffalo productivity, especially for third world country where rural economy is significantly dependent on buffalo productivity. PMID:23336431

In silico mining of putative microsatellite markers from whole genome sequence of water buffalo (Bubalus bubalis) and development of first BuffSatDB.

PubMed

Sarika; Arora, Vasu; Iquebal, Mir Asif; Rai, Anil; Kumar, Dinesh

2013-01-19

Though India has sequenced water buffalo genome but its draft assembly is based on cattle genome BTau 4.0, thus de novo chromosome wise assembly is a major pending issue for global community. The existing radiation hybrid of buffalo and these reported STR can be used further in final gap plugging and "finishing" expected in de novo genome assembly. QTL and gene mapping needs mining of putative STR from buffalo genome at equal interval on each and every chromosome. Such markers have potential role in improvement of desirable characteristics, such as high milk yields, resistance to diseases, high growth rate. The STR mining from whole genome and development of user friendly database is yet to be done to reap the benefit of whole genome sequence. By in silico microsatellite mining of whole genome, we have developed first STR database of water buffalo, BuffSatDb (Buffalo MicroSatellite Database (http://cabindb.iasri.res.in/buffsatdb/) which is a web based relational database of 910529 microsatellite markers, developed using PHP and MySQL database. Microsatellite markers have been generated using MIcroSAtellite tool. It is simple and systematic web based search for customised retrieval of chromosome wise and genome-wide microsatellites. Search has been enabled based on chromosomes, motif type (mono-hexa), repeat motif and repeat kind (simple and composite). The search may be customised by limiting location of STR on chromosome as well as number of markers in that range. This is a novel approach and not been implemented in any of the existing marker database. This database has been further appended with Primer3 for primer designing of the selected markers enabling researcher to select markers of choice at desired interval over the chromosome. The unique add-on of degenerate bases further helps in resolving presence of degenerate bases in current buffalo assembly. Being first buffalo STR database in the world , this would not only pave the way in resolving current assembly problem but shall be of immense use for global community in QTL/gene mapping critically required to increase knowledge in the endeavour to increase buffalo productivity, especially for third world country where rural economy is significantly dependent on buffalo productivity.

Development and application of a Chinese webpage suicide information mining system (sims).

PubMed

Chen, Penglai; Chai, Jing; Zhang, Lu; Wang, Debin

2014-11-01

This study aims at designing and piloting a convenient Chinese webpage suicide information mining system (SIMS) to help search and filter required data from the internet and discover potential features and trends of suicide. SIMS utilizes Microsoft Visual Studio2008, SQL2008 and C# as development tools. It collects webpage data via popular search engines; cleans the data using trained models plus minimum manual help; translates the cleaned texts into quantitative data through models and supervised fuzzy recognition; analyzes and visualizes related variables by self-programmed algorithms. The SIMS developed comprises such functions as suicide news and blogs collection, data filtering, cleaning, extraction and translation, data analysis and presentation. SIMS-mediated mining of one-year webpage revealed that: peak months and hours of web-reported suicide events were June-July and 10-11 am respectively, and the lowest months and hours, September-October and 1-7 am; suicide reports came mostly from Soho, Tecent, Sina etc.; male suicide victims over counted female victims in most sub-regions but southwest China; homes, public places and rented houses were the top three places to commit suicide; poisoning, cutting vein and jumping from building were the most commonly used methods to commit suicide; love disputes, family disputes and mental diseases were the leading causes. SIMS provides a preliminary and supplementary means for monitoring and understanding suicide. It proposes useful aspects as well as tools for analyzing the features and trends of suicide using data derived from Chinese webpages. Yet given the intrinsic "dual nature" of internet-based suicide information and the tremendous difficulties experienced by ourselves and other researchers, there is still a long way to go for us to expand, refine and evaluate the system.

Documenting clinical pharmacist intervention before and after the introduction of a web-based tool.

PubMed

Nurgat, Zubeir A; Al-Jazairi, Abdulrazaq S; Abu-Shraie, Nada; Al-Jedai, Ahmed

2011-04-01

To develop a database for documenting pharmacist intervention through a web-based application. The secondary endpoint was to determine if the new, web-based application provides any benefits with regards to documentation compliance by clinical pharmacists and ease of calculating cost savings compared with our previous method of documenting pharmacist interventions. A tertiary care hospital in Saudi Arabia. The documentation of interventions using a web-based documentation application was retrospectively compared with previous methods of documentation of clinical pharmacists' interventions (multi-user PC software). The number and types of interventions recorded by pharmacists, data mining of archived data, efficiency, cost savings, and the accuracy of the data generated. The number of documented clinical interventions increased from 4,926, using the multi-user PC software, to 6,840 for the web-based application. On average, we observed 653 interventions per clinical pharmacist using the web-based application, which showed an increase compared to an average of 493 interventions using the old multi-user PC software. However, using a paired Student's t-test there was no statistical significance difference between the two means (P = 0.201). Using a χ² test, which captured management level and the type of system used, we found a strong effect of management level (P < 2.2 × 10⁻¹⁶) on the number of documented interventions. We also found a moderately significant relationship between educational level and the number of interventions documented (P = 0.045). The mean ± SD time required to document an intervention using the web-based application was 66.55 ± 8.98 s. Using the web-based application, 29.06% of documented interventions resulted in cost-savings, while using the multi-user PC software only 4.75% of interventions did so. The majority of cost savings across both platforms resulted from the discontinuation of unnecessary drugs and a change in dosage regimen. Data collection using the web-based application was consistently more complete when compared to the multi-user PC software. The web-based application is an efficient system for documenting pharmacist interventions. Its flexibility and accessibility, as well as its detailed report functionality is a useful tool that will hopefully encourage other primary and secondary care facilities to adopt similar applications.

Development of a data mining and imaging informatics display tool for a multiple sclerosis e-folder system

NASA Astrophysics Data System (ADS)

Liu, Margaret; Loo, Jerry; Ma, Kevin; Liu, Brent

2011-03-01

Multiple sclerosis (MS) is a debilitating autoimmune disease of the central nervous system that damages axonal pathways through inflammation and demyelination. In order to address the need for a centralized application to manage and study MS patients, the MS e-Folder - a web-based, disease-specific electronic medical record system - was developed. The e-Folder has a PHP and MySQL based graphical user interface (GUI) that can serve as both a tool for clinician decision support and a data mining tool for researchers. This web-based GUI gives the e-Folder a user friendly interface that can be securely accessed through the internet and requires minimal software installation on the client side. The e-Folder GUI displays and queries patient medical records--including demographic data, social history, past medical history, and past MS history. In addition, DICOM format imaging data, and computer aided detection (CAD) results from a lesion load algorithm are also displayed. The GUI interface is dynamic and allows manipulation of the DICOM images, such as zoom, pan, and scrolling, and the ability to rotate 3D images. Given the complexity of clinical management and the need to bolster research in MS, the MS e-Folder system will improve patient care and provide MS researchers with a function-rich patient data hub.

Strategic Integration of Multiple Bioinformatics Resources for System Level Analysis of Biological Networks.

PubMed

D'Souza, Mark; Sulakhe, Dinanath; Wang, Sheng; Xie, Bing; Hashemifar, Somaye; Taylor, Andrew; Dubchak, Inna; Conrad Gilliam, T; Maltsev, Natalia

2017-01-01

Recent technological advances in genomics allow the production of biological data at unprecedented tera- and petabyte scales. Efficient mining of these vast and complex datasets for the needs of biomedical research critically depends on a seamless integration of the clinical, genomic, and experimental information with prior knowledge about genotype-phenotype relationships. Such experimental data accumulated in publicly available databases should be accessible to a variety of algorithms and analytical pipelines that drive computational analysis and data mining.We present an integrated computational platform Lynx (Sulakhe et al., Nucleic Acids Res 44:D882-D887, 2016) ( http://lynx.cri.uchicago.edu ), a web-based database and knowledge extraction engine. It provides advanced search capabilities and a variety of algorithms for enrichment analysis and network-based gene prioritization. It gives public access to the Lynx integrated knowledge base (LynxKB) and its analytical tools via user-friendly web services and interfaces. The Lynx service-oriented architecture supports annotation and analysis of high-throughput experimental data. Lynx tools assist the user in extracting meaningful knowledge from LynxKB and experimental data, and in the generation of weighted hypotheses regarding the genes and molecular mechanisms contributing to human phenotypes or conditions of interest. The goal of this integrated platform is to support the end-to-end analytical needs of various translational projects.

Mining adverse drug reactions from online healthcare forums using hidden Markov model.

PubMed

Sampathkumar, Hariprasad; Chen, Xue-wen; Luo, Bo

2014-10-23

Adverse Drug Reactions are one of the leading causes of injury or death among patients undergoing medical treatments. Not all Adverse Drug Reactions are identified before a drug is made available in the market. Current post-marketing drug surveillance methods, which are based purely on voluntary spontaneous reports, are unable to provide the early indications necessary to prevent the occurrence of such injuries or fatalities. The objective of this research is to extract reports of adverse drug side-effects from messages in online healthcare forums and use them as early indicators to assist in post-marketing drug surveillance. We treat the task of extracting adverse side-effects of drugs from healthcare forum messages as a sequence labeling problem and present a Hidden Markov Model(HMM) based Text Mining system that can be used to classify a message as containing drug side-effect information and then extract the adverse side-effect mentions from it. A manually annotated dataset from http://www.medications.com is used in the training and validation of the HMM based Text Mining system. A 10-fold cross-validation on the manually annotated dataset yielded on average an F-Score of 0.76 from the HMM Classifier, in comparison to 0.575 from the Baseline classifier. Without the Plain Text Filter component as a part of the Text Processing module, the F-Score of the HMM Classifier was reduced to 0.378 on average, while absence of the HTML Filter component was found to have no impact. Reducing the Drug names dictionary size by half, on average reduced the F-Score of the HMM Classifier to 0.359, while a similar reduction to the side-effects dictionary yielded an F-Score of 0.651 on average. Adverse side-effects mined from http://www.medications.com and http://www.steadyhealth.com were found to match the Adverse Drug Reactions on the Drug Package Labels of several drugs. In addition, some novel adverse side-effects, which can be potential Adverse Drug Reactions, were also identified. The results from the HMM based Text Miner are encouraging to pursue further enhancements to this approach. The mined novel side-effects can act as early indicators for health authorities to help focus their efforts in post-marketing drug surveillance.

BioHackathon series in 2011 and 2012: penetration of ontology and linked data in life science domains

PubMed Central

2014-01-01

The application of semantic technologies to the integration of biological data and the interoperability of bioinformatics analysis and visualization tools has been the common theme of a series of annual BioHackathons hosted in Japan for the past five years. Here we provide a review of the activities and outcomes from the BioHackathons held in 2011 in Kyoto and 2012 in Toyama. In order to efficiently implement semantic technologies in the life sciences, participants formed various sub-groups and worked on the following topics: Resource Description Framework (RDF) models for specific domains, text mining of the literature, ontology development, essential metadata for biological databases, platforms to enable efficient Semantic Web technology development and interoperability, and the development of applications for Semantic Web data. In this review, we briefly introduce the themes covered by these sub-groups. The observations made, conclusions drawn, and software development projects that emerged from these activities are discussed. PMID:24495517

GarlicESTdb: an online database and mining tool for garlic EST sequences.

PubMed

Kim, Dae-Won; Jung, Tae-Sung; Nam, Seong-Hyeuk; Kwon, Hyuk-Ryul; Kim, Aeri; Chae, Sung-Hwa; Choi, Sang-Haeng; Kim, Dong-Wook; Kim, Ryong Nam; Park, Hong-Seog

2009-05-18

Allium sativum., commonly known as garlic, is a species in the onion genus (Allium), which is a large and diverse one containing over 1,250 species. Its close relatives include chives, onion, leek and shallot. Garlic has been used throughout recorded history for culinary, medicinal use and health benefits. Currently, the interest in garlic is highly increasing due to nutritional and pharmaceutical value including high blood pressure and cholesterol, atherosclerosis and cancer. For all that, there are no comprehensive databases available for Expressed Sequence Tags(EST) of garlic for gene discovery and future efforts of genome annotation. That is why we developed a new garlic database and applications to enable comprehensive analysis of garlic gene expression. GarlicESTdb is an integrated database and mining tool for large-scale garlic (Allium sativum) EST sequencing. A total of 21,595 ESTs collected from an in-house cDNA library were used to construct the database. The analysis pipeline is an automated system written in JAVA and consists of the following components: automatic preprocessing of EST reads, assembly of raw sequences, annotation of the assembled sequences, storage of the analyzed information into MySQL databases, and graphic display of all processed data. A web application was implemented with the latest J2EE (Java 2 Platform Enterprise Edition) software technology (JSP/EJB/JavaServlet) for browsing and querying the database, for creation of dynamic web pages on the client side, and for mapping annotated enzymes to KEGG pathways, the AJAX framework was also used partially. The online resources, such as putative annotation, single nucleotide polymorphisms (SNP) and tandem repeat data sets, can be searched by text, explored on the website, searched using BLAST, and downloaded. To archive more significant BLAST results, a curation system was introduced with which biologists can easily edit best-hit annotation information for others to view. The GarlicESTdb web application is freely available at http://garlicdb.kribb.re.kr. GarlicESTdb is the first incorporated online information database of EST sequences isolated from garlic that can be freely accessed and downloaded. It has many useful features for interactive mining of EST contigs and datasets from each library, including curation of annotated information, expression profiling, information retrieval, and summary of statistics of functional annotation. Consequently, the development of GarlicESTdb will provide a crucial contribution to biologists for data-mining and more efficient experimental studies.

RCrawler: An R package for parallel web crawling and scraping

NASA Astrophysics Data System (ADS)

Khalil, Salim; Fakir, Mohamed

RCrawler is a contributed R package for domain-based web crawling and content scraping. As the first implementation of a parallel web crawler in the R environment, RCrawler can crawl, parse, store pages, extract contents, and produce data that can be directly employed for web content mining applications. However, it is also flexible, and could be adapted to other applications. The main features of RCrawler are multi-threaded crawling, content extraction, and duplicate content detection. In addition, it includes functionalities such as URL and content-type filtering, depth level controlling, and a robot.txt parser. Our crawler has a highly optimized system, and can download a large number of pages per second while being robust against certain crashes and spider traps. In this paper, we describe the design and functionality of RCrawler, and report on our experience of implementing it in an R environment, including different optimizations that handle the limitations of R. Finally, we discuss our experimental results.

Soil food web changes during spontaneous succession at post mining sites: a possible ecosystem engineering effect on food web organization?

PubMed

Frouz, Jan; Thébault, Elisa; Pižl, Václav; Adl, Sina; Cajthaml, Tomáš; Baldrián, Petr; Háněl, Ladislav; Starý, Josef; Tajovský, Karel; Materna, Jan; Nováková, Alena; de Ruiter, Peter C

2013-01-01

Parameters characterizing the structure of the decomposer food web, biomass of the soil microflora (bacteria and fungi) and soil micro-, meso- and macrofauna were studied at 14 non-reclaimed 1- 41-year-old post-mining sites near the town of Sokolov (Czech Republic). These observations on the decomposer food webs were compared with knowledge of vegetation and soil microstructure development from previous studies. The amount of carbon entering the food web increased with succession age in a similar way as the total amount of C in food web biomass and the number of functional groups in the food web. Connectance did not show any significant changes with succession age, however. In early stages of the succession, the bacterial channel dominated the food web. Later on, in shrub-dominated stands, the fungal channel took over. Even later, in the forest stage, the bacterial channel prevailed again. The best predictor of fungal bacterial ratio is thickness of fermentation layer. We argue that these changes correspond with changes in topsoil microstructure driven by a combination of plant organic matter input and engineering effects of earthworms. In early stages, soil is alkaline, and a discontinuous litter layer on the soil surface promotes bacterial biomass growth, so the bacterial food web channel can dominate. Litter accumulation on the soil surface supports the development of the fungal channel. In older stages, earthworms arrive, mix litter into the mineral soil and form an organo-mineral topsoil, which is beneficial for bacteria and enhances the bacterial food web channel.

Soil Food Web Changes during Spontaneous Succession at Post Mining Sites: A Possible Ecosystem Engineering Effect on Food Web Organization?

PubMed Central

Frouz, Jan; Thébault, Elisa; Pižl, Václav; Adl, Sina; Cajthaml, Tomáš; Baldrián, Petr; Háněl, Ladislav; Starý, Josef; Tajovský, Karel; Materna, Jan; Nováková, Alena; de Ruiter, Peter C.

2013-01-01

Parameters characterizing the structure of the decomposer food web, biomass of the soil microflora (bacteria and fungi) and soil micro-, meso- and macrofauna were studied at 14 non-reclaimed 1– 41-year-old post-mining sites near the town of Sokolov (Czech Republic). These observations on the decomposer food webs were compared with knowledge of vegetation and soil microstructure development from previous studies. The amount of carbon entering the food web increased with succession age in a similar way as the total amount of C in food web biomass and the number of functional groups in the food web. Connectance did not show any significant changes with succession age, however. In early stages of the succession, the bacterial channel dominated the food web. Later on, in shrub-dominated stands, the fungal channel took over. Even later, in the forest stage, the bacterial channel prevailed again. The best predictor of fungal bacterial ratio is thickness of fermentation layer. We argue that these changes correspond with changes in topsoil microstructure driven by a combination of plant organic matter input and engineering effects of earthworms. In early stages, soil is alkaline, and a discontinuous litter layer on the soil surface promotes bacterial biomass growth, so the bacterial food web channel can dominate. Litter accumulation on the soil surface supports the development of the fungal channel. In older stages, earthworms arrive, mix litter into the mineral soil and form an organo-mineral topsoil, which is beneficial for bacteria and enhances the bacterial food web channel. PMID:24260281

Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature.

PubMed

Ravikumar, Komandur Elayavilli; Wagholikar, Kavishwar B; Li, Dingcheng; Kocher, Jean-Pierre; Liu, Hongfang

2015-06-06

Advances in the next generation sequencing technology has accelerated the pace of individualized medicine (IM), which aims to incorporate genetic/genomic information into medicine. One immediate need in interpreting sequencing data is the assembly of information about genetic variants and their corresponding associations with other entities (e.g., diseases or medications). Even with dedicated effort to capture such information in biological databases, much of this information remains 'locked' in the unstructured text of biomedical publications. There is a substantial lag between the publication and the subsequent abstraction of such information into databases. Multiple text mining systems have been developed, but most of them focus on the sentence level association extraction with performance evaluation based on gold standard text annotations specifically prepared for text mining systems. We developed and evaluated a text mining system, MutD, which extracts protein mutation-disease associations from MEDLINE abstracts by incorporating discourse level analysis, using a benchmark data set extracted from curated database records. MutD achieves an F-measure of 64.3% for reconstructing protein mutation disease associations in curated database records. Discourse level analysis component of MutD contributed to a gain of more than 10% in F-measure when compared against the sentence level association extraction. Our error analysis indicates that 23 of the 64 precision errors are true associations that were not captured by database curators and 68 of the 113 recall errors are caused by the absence of associated disease entities in the abstract. After adjusting for the defects in the curated database, the revised F-measure of MutD in association detection reaches 81.5%. Our quantitative analysis reveals that MutD can effectively extract protein mutation disease associations when benchmarking based on curated database records. The analysis also demonstrates that incorporating discourse level analysis significantly improved the performance of extracting the protein-mutation-disease association. Future work includes the extension of MutD for full text articles.

EAGLE: 'EAGLE'Is an' Algorithmic Graph Library for Exploration

DOE Office of Scientific and Technical Information (OSTI.GOV)

2015-01-16

The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. Today there is no tools to conduct "graph mining" on RDF standard data sets. We address that need through implementation of popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, degree distribution,more » diversity degree, PageRank, etc.). We implement these algorithms as SPARQL queries, wrapped within Python scripts and call our software tool as EAGLE. In RDF style, EAGLE stands for "EAGLE 'Is an' algorithmic graph library for exploration. EAGLE is like 'MATLAB' for 'Linked Data.'« less

School and Library Media. Introduction; The Uniform Computer Information Transactions Act (UCITA): More Critical for Educators than Copyright Law?; Redefining Professional Growth: New Attitudes, New Tools--A Case Study; Diversity in School Library Media Center Resources; Image-Text Relationships in Web Pages; Aiming for Effective Student Learning in Web-Based Courses: Insights from Student Experiences.

ERIC Educational Resources Information Center

Fitzgerald, Mary Ann; Gregory, Vicki L.; Brock, Kathy; Bennett, Elizabeth; Chen, Shu-Hsien Lai; Marsh, Emily; Moore, Joi L.; Kim, Kyung-Sun; Esser, Linda R.

2002-01-01

Chapters in this section of "Educational Media and Technology Yearbook" examine important trends prominent in the landscape of the school library media profession in 2001. Themes include mandated educational reform; diversity in school library resources; communication through image-text juxtaposition in Web pages; and professional development and…

Streaming Media for Web Based Training.

ERIC Educational Resources Information Center

Childers, Chad; Rizzo, Frank; Bangert, Linda

This paper discusses streaming media for World Wide Web-based training (WBT). The first section addresses WBT in the 21st century, including the Synchronized Multimedia Integration Language (SMIL) standard that allows multimedia content such as text, pictures, sound, and video to be synchronized for a coherent learning experience. The second…

Web-Based Learning and Instruction Support System for Pneumatics

ERIC Educational Resources Information Center

Yen, Chiaming; Li, Wu-Jeng

2003-01-01

This research presents a Web-based learning and instructional system for Pneumatics. The system includes course material, remote data acquisition modules, and a pneumatic laboratory set. The course material is in the HTML format accompanied with text, still and animated images, simulation programs, and computer aided design tools. The data…

Mining Microarray Data at NCBI’s Gene Expression Omnibus (GEO)*

PubMed Central

Barrett, Tanya; Edgar, Ron

2006-01-01

Summary The Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) has emerged as the leading fully public repository for gene expression data. This chapter describes how to use Web-based interfaces, applications, and graphics to effectively explore, visualize, and interpret the hundreds of microarray studies and millions of gene expression patterns stored in GEO. Data can be examined from both experiment-centric and gene-centric perspectives using user-friendly tools that do not require specialized expertise in microarray analysis or time-consuming download of massive data sets. The GEO database is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo. PMID:16888359

Web Usage Mining: Application to an Online Educational Digital Library Service

ERIC Educational Resources Information Center

Palmer, Bart C.

2012-01-01

This dissertation was situated in the crossroads of educational data mining (EDM), educational digital libraries (such as the National Science Digital Library; http://nsdl.org), and examination of teacher behaviors while creating online learning resources in an end-user authoring system, the Instructional Architect (IA; http://ia.usu.edu). The…

30 CFR 74.16 - Material required for record.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Material required for record. 74.16 Section 74.16 Mineral Resources MINE SAFETY AND HEALTH ADMINISTRATION, DEPARTMENT OF LABOR COAL MINE SAFETY AND... deliver a complete sampling device free of charge to NIOSH at the address specified on the NIOSH Web page...

Literature evidence in open targets - a target validation platform.

PubMed

Kafkas, Şenay; Dunham, Ian; McEntyre, Johanna

2017-06-06

We present the Europe PMC literature component of Open Targets - a target validation platform that integrates various evidence to aid drug target identification and validation. The component identifies target-disease associations in documents and ranks the documents based on their confidence from the Europe PMC literature database, by using rules utilising expert-provided heuristic information. The confidence score of a given document represents how valuable the document is in the scope of target validation for a given target-disease association by taking into account the credibility of the association based on the properties of the text. The component serves the platform regularly with the up-to-date data since December, 2015. Currently, there are a total number of 1168365 distinct target-disease associations text mined from >26 million PubMed abstracts and >1.2 million Open Access full text articles. Our comparative analyses on the current available evidence data in the platform revealed that 850179 of these associations are exclusively identified by literature mining. This component helps the platform's users by providing the most relevant literature hits for a given target and disease. The text mining evidence along with the other types of evidence can be explored visually through https://www.targetvalidation.org and all the evidence data is available for download in json format from https://www.targetvalidation.org/downloads/data .

Text mining resources for the life sciences.

PubMed

Przybyła, Piotr; Shardlow, Matthew; Aubin, Sophie; Bossy, Robert; Eckart de Castilho, Richard; Piperidis, Stelios; McNaught, John; Ananiadou, Sophia

2016-01-01

Text mining is a powerful technology for quickly distilling key information from vast quantities of biomedical literature. However, to harness this power the researcher must be well versed in the availability, suitability, adaptability, interoperability and comparative accuracy of current text mining resources. In this survey, we give an overview of the text mining resources that exist in the life sciences to help researchers, especially those employed in biocuration, to engage with text mining in their own work. We categorize the various resources under three sections: Content Discovery looks at where and how to find biomedical publications for text mining; Knowledge Encoding describes the formats used to represent the different levels of information associated with content that enable text mining, including those formats used to carry such information between processes; Tools and Services gives an overview of workflow management systems that can be used to rapidly configure and compare domain- and task-specific processes, via access to a wide range of pre-built tools. We also provide links to relevant repositories in each section to enable the reader to find resources relevant to their own area of interest. Throughout this work we give a special focus to resources that are interoperable-those that have the crucial ability to share information, enabling smooth integration and reusability. © The Author(s) 2016. Published by Oxford University Press.

Text mining resources for the life sciences

PubMed Central

Shardlow, Matthew; Aubin, Sophie; Bossy, Robert; Eckart de Castilho, Richard; Piperidis, Stelios; McNaught, John; Ananiadou, Sophia

2016-01-01

Text mining is a powerful technology for quickly distilling key information from vast quantities of biomedical literature. However, to harness this power the researcher must be well versed in the availability, suitability, adaptability, interoperability and comparative accuracy of current text mining resources. In this survey, we give an overview of the text mining resources that exist in the life sciences to help researchers, especially those employed in biocuration, to engage with text mining in their own work. We categorize the various resources under three sections: Content Discovery looks at where and how to find biomedical publications for text mining; Knowledge Encoding describes the formats used to represent the different levels of information associated with content that enable text mining, including those formats used to carry such information between processes; Tools and Services gives an overview of workflow management systems that can be used to rapidly configure and compare domain- and task-specific processes, via access to a wide range of pre-built tools. We also provide links to relevant repositories in each section to enable the reader to find resources relevant to their own area of interest. Throughout this work we give a special focus to resources that are interoperable—those that have the crucial ability to share information, enabling smooth integration and reusability. PMID:27888231

Are Pretty Interfaces Worth the Time? The Effects of User Interface Types on Web-Based Instruction

ERIC Educational Resources Information Center

Cheon, Jongpil; Grant, Michael M.

2009-01-01

The purpose of this study was to examine the effectiveness of three different interface types on Web-based instruction: a text-based interface, a graphical interface and a metaphorical interface. In order to determine differences among three interface groups, we compared learning performance, cognitive load, usability, and appeal with various data…

Types of Online Hierarchical Repository Structures

ERIC Educational Resources Information Center

Hershkovitz, Arnon; Azran, Ronit; Hardof-Jaffe, Sharon; Nachmias, Rafi

2011-01-01

This study presents an empirical investigation of online hierarchical repositories of items presented to university students in Web-supported course websites, using Web mining methods. To this end, data from 1747 courses were collected, and the use of online repositories of content items in these courses was examined. At a later stage, courses…

Contributions of the NOAA Hollings Undergraduate Scholarship Program to the Geosciences Pipeline

NASA Astrophysics Data System (ADS)

Kaplan, M.

2016-12-01

Since 2005, the NOAA Ernest F. Hollings Undergraduate Scholarship Program has provided tuition support and paid summer internship opportunities at NOAA to exceptional students majoring in the geosciences. The purpose of the scholarship program is to train students in NOAA mission fields. Multiple methods were used to track the career trajectories of Hollings alumni, including mining LinkedIn data, conducting an impact analysis based on a professionally developed web-based evaluation survey, and a web-based alumni update system. At least one postgraduate record was recorded for 80% of Hollings Scholarship alumni. Of the alumni reached, more than 75% continued on to graduate school in a NOAA mission field, and 86% of those graduate degrees were in a NOAA mission field or other STEM field. More than 60% of alumni had at least one professional record, with the most alumni working in private industry, followed by nongovernmental organizations and federal, state and local government.

Mining and beneficiation: A review of possible lunar applications

NASA Technical Reports Server (NTRS)

Chamberlain, Peter G.

1991-01-01

Successful exploration of Mars and outer space may require base stations strategically located on the Moon. Such bases must develop a certain self-sufficiency, particularly in the critical life support materials, fuel components, and construction materials. Technology is reviewed for the first steps in lunar resource recovery-mining and beneficiation. The topic is covered in three main categories: site selection; mining; and beneficiation. It will also include (in less detail) in-situ processes. The text described mining technology ranging from simple diggings and hauling vehicles (the strawman) to more specialized technology including underground excavation methods. The section of beneficiation emphasizes dry separation techniques and methods of sorting the ore by particle size. In-situ processes, chemical and thermal, are identified to stimulate further thinking by future researchers.

Scrutinizing the Cybersell: Teen-Targeted Web Sites as Texts

ERIC Educational Resources Information Center

Crovitz, Darren

2007-01-01

Darren Crovitz explains that the explosive growth of Web-based content and communication in recent years compels us to teach students how to examine the "rhetorical nature and ethical dimensions of the online world." He demonstrates successful approaches to accomplish this goal through his analysis of the selling techniques of two Web sites…

How Readability and Topic Incidence Relate to Performance on Mathematics Story Problems in Computer-Based Curricula

ERIC Educational Resources Information Center

Walkington, Candace; Clinton, Virginia; Ritter, Steven N.; Nathan, Mitchell J.

2015-01-01

Solving mathematics story problems requires text comprehension skills. However, previous studies have found few connections between traditional measures of text readability and performance on story problems. We hypothesized that recently developed measures of readability and topic incidence measured by text-mining tools may illuminate associations…

Improving the interactivity and functionality of Web-based radiology teaching files with the Java programming language.

PubMed

Eng, J

1997-01-01

Java is a programming language that runs on a "virtual machine" built into World Wide Web (WWW)-browsing programs on multiple hardware platforms. Web pages were developed with Java to enable Web-browsing programs to overlay transparent graphics and text on displayed images so that the user could control the display of labels and annotations on the images, a key feature not available with standard Web pages. This feature was extended to include the presentation of normal radiologic anatomy. Java programming was also used to make Web browsers compatible with the Digital Imaging and Communications in Medicine (DICOM) file format. By enhancing the functionality of Web pages, Java technology should provide greater incentive for using a Web-based approach in the development of radiology teaching material.

ParaBTM: A Parallel Processing Framework for Biomedical Text Mining on Supercomputers.

PubMed

Xing, Yuting; Wu, Chengkun; Yang, Xi; Wang, Wei; Zhu, En; Yin, Jianping

2018-04-27

A prevailing way of extracting valuable information from biomedical literature is to apply text mining methods on unstructured texts. However, the massive amount of literature that needs to be analyzed poses a big data challenge to the processing efficiency of text mining. In this paper, we address this challenge by introducing parallel processing on a supercomputer. We developed paraBTM, a runnable framework that enables parallel text mining on the Tianhe-2 supercomputer. It employs a low-cost yet effective load balancing strategy to maximize the efficiency of parallel processing. We evaluated the performance of paraBTM on several datasets, utilizing three types of named entity recognition tasks as demonstration. Results show that, in most cases, the processing efficiency can be greatly improved with parallel processing, and the proposed load balancing strategy is simple and effective. In addition, our framework can be readily applied to other tasks of biomedical text mining besides NER.

Mining of relations between proteins over biomedical scientific literature using a deep-linguistic approach.

PubMed

Rinaldi, Fabio; Schneider, Gerold; Kaljurand, Kaarel; Hess, Michael; Andronis, Christos; Konstandi, Ourania; Persidis, Andreas

2007-02-01

The amount of new discoveries (as published in the scientific literature) in the biomedical area is growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and thus the extraction of the core information becomes very expensive. Therefore, there is a growing interest in text processing approaches that can deliver selected information from scientific publications, which can limit the amount of human intervention normally needed to gather those results. This paper presents and evaluates an approach aimed at automating the process of extracting functional relations (e.g. interactions between genes and proteins) from scientific literature in the biomedical domain. The approach, using a novel dependency-based parser, is based on a complete syntactic analysis of the corpus. We have implemented a state-of-the-art text mining system for biomedical literature, based on a deep-linguistic, full-parsing approach. The results are validated on two different corpora: the manually annotated genomics information access (GENIA) corpus and the automatically annotated arabidopsis thaliana circadian rhythms (ATCR) corpus. We show how a deep-linguistic approach (contrary to common belief) can be used in a real world text mining application, offering high-precision relation extraction, while at the same time retaining a sufficient recall.

An overview of the BioCreative 2012 Workshop Track III: interactive text mining task

PubMed Central

Arighi, Cecilia N.; Carterette, Ben; Cohen, K. Bretonnel; Krallinger, Martin; Wilbur, W. John; Fey, Petra; Dodson, Robert; Cooper, Laurel; Van Slyke, Ceri E.; Dahdul, Wasila; Mabee, Paula; Li, Donghui; Harris, Bethany; Gillespie, Marc; Jimenez, Silvia; Roberts, Phoebe; Matthews, Lisa; Becker, Kevin; Drabkin, Harold; Bello, Susan; Licata, Luana; Chatr-aryamontri, Andrew; Schaeffer, Mary L.; Park, Julie; Haendel, Melissa; Van Auken, Kimberly; Li, Yuling; Chan, Juancarlos; Muller, Hans-Michael; Cui, Hong; Balhoff, James P.; Chi-Yang Wu, Johnny; Lu, Zhiyong; Wei, Chih-Hsuan; Tudor, Catalina O.; Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar; Cejuela, Juan Miguel; Dubey, Pratibha; Wu, Cathy

2013-01-01

In many databases, biocuration primarily involves literature curation, which usually involves retrieving relevant articles, extracting information that will translate into annotations and identifying new incoming literature. As the volume of biological literature increases, the use of text mining to assist in biocuration becomes increasingly relevant. A number of groups have developed tools for text mining from a computer science/linguistics perspective, and there are many initiatives to curate some aspect of biology from the literature. Some biocuration efforts already make use of a text mining tool, but there have not been many broad-based systematic efforts to study which aspects of a text mining tool contribute to its usefulness for a curation task. Here, we report on an effort to bring together text mining tool developers and database biocurators to test the utility and usability of tools. Six text mining systems presenting diverse biocuration tasks participated in a formal evaluation, and appropriate biocurators were recruited for testing. The performance results from this evaluation indicate that some of the systems were able to improve efficiency of curation by speeding up the curation task significantly (∼1.7- to 2.5-fold) over manual curation. In addition, some of the systems were able to improve annotation accuracy when compared with the performance on the manually curated set. In terms of inter-annotator agreement, the factors that contributed to significant differences for some of the systems included the expertise of the biocurator on the given curation task, the inherent difficulty of the curation and attention to annotation guidelines. After the task, annotators were asked to complete a survey to help identify strengths and weaknesses of the various systems. The analysis of this survey highlights how important task completion is to the biocurators’ overall experience of a system, regardless of the system’s high score on design, learnability and usability. In addition, strategies to refine the annotation guidelines and systems documentation, to adapt the tools to the needs and query types the end user might have and to evaluate performance in terms of efficiency, user interface, result export and traditional evaluation metrics have been analyzed during this task. This analysis will help to plan for a more intense study in BioCreative IV. PMID:23327936

An overview of the BioCreative 2012 Workshop Track III: interactive text mining task.

PubMed

Arighi, Cecilia N; Carterette, Ben; Cohen, K Bretonnel; Krallinger, Martin; Wilbur, W John; Fey, Petra; Dodson, Robert; Cooper, Laurel; Van Slyke, Ceri E; Dahdul, Wasila; Mabee, Paula; Li, Donghui; Harris, Bethany; Gillespie, Marc; Jimenez, Silvia; Roberts, Phoebe; Matthews, Lisa; Becker, Kevin; Drabkin, Harold; Bello, Susan; Licata, Luana; Chatr-aryamontri, Andrew; Schaeffer, Mary L; Park, Julie; Haendel, Melissa; Van Auken, Kimberly; Li, Yuling; Chan, Juancarlos; Muller, Hans-Michael; Cui, Hong; Balhoff, James P; Chi-Yang Wu, Johnny; Lu, Zhiyong; Wei, Chih-Hsuan; Tudor, Catalina O; Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar; Cejuela, Juan Miguel; Dubey, Pratibha; Wu, Cathy

2013-01-01

In many databases, biocuration primarily involves literature curation, which usually involves retrieving relevant articles, extracting information that will translate into annotations and identifying new incoming literature. As the volume of biological literature increases, the use of text mining to assist in biocuration becomes increasingly relevant. A number of groups have developed tools for text mining from a computer science/linguistics perspective, and there are many initiatives to curate some aspect of biology from the literature. Some biocuration efforts already make use of a text mining tool, but there have not been many broad-based systematic efforts to study which aspects of a text mining tool contribute to its usefulness for a curation task. Here, we report on an effort to bring together text mining tool developers and database biocurators to test the utility and usability of tools. Six text mining systems presenting diverse biocuration tasks participated in a formal evaluation, and appropriate biocurators were recruited for testing. The performance results from this evaluation indicate that some of the systems were able to improve efficiency of curation by speeding up the curation task significantly (∼1.7- to 2.5-fold) over manual curation. In addition, some of the systems were able to improve annotation accuracy when compared with the performance on the manually curated set. In terms of inter-annotator agreement, the factors that contributed to significant differences for some of the systems included the expertise of the biocurator on the given curation task, the inherent difficulty of the curation and attention to annotation guidelines. After the task, annotators were asked to complete a survey to help identify strengths and weaknesses of the various systems. The analysis of this survey highlights how important task completion is to the biocurators' overall experience of a system, regardless of the system's high score on design, learnability and usability. In addition, strategies to refine the annotation guidelines and systems documentation, to adapt the tools to the needs and query types the end user might have and to evaluate performance in terms of efficiency, user interface, result export and traditional evaluation metrics have been analyzed during this task. This analysis will help to plan for a more intense study in BioCreative IV.

IRIS Earthquake Browser with Integration to the GEON IDV for 3-D Visualization of Hypocenters.

NASA Astrophysics Data System (ADS)

Weertman, B. R.

2007-12-01

We present a new generation of web based earthquake query tool - the IRIS Earthquake Browser (IEB). The IEB combines the DMC's large set of earthquake catalogs (provided by USGS/NEIC, ISC and the ANF) with the popular Google Maps web interface. With the IEB you can quickly and easily find earthquakes in any region of the globe. Using Google's detailed satellite images, earthquakes can be easily co-located with natural geographic features such as volcanoes as well as man made features such as commercial mines. A set of controls allow earthquakes to be filtered by time, magnitude, and depth range as well as catalog name, contributor name and magnitude type. Displayed events can be easily exported in NetCDF format into the GEON Integrated Data Viewer (IDV) where hypocenters may be visualized in three dimensions. Looking "under the hood", the IEB is based on AJAX technology and utilizes REST style web services hosted at the IRIS DMC. The IEB is part of a broader effort at the DMC aimed at making our data holdings available via web services. The IEB is useful both educationally and as a research tool.

An intelligent tool for activity data collection.

PubMed

Sarkar, A M Jehad

2011-01-01

Activity recognition systems using simple and ubiquitous sensors require a large variety of real-world sensor data for not only evaluating their performance but also training the systems for better functioning. However, a tremendous amount of effort is required to setup an environment for collecting such data. For example, expertise and resources are needed to design and install the sensors, controllers, network components, and middleware just to perform basic data collections. It is therefore desirable to have a data collection method that is inexpensive, flexible, user-friendly, and capable of providing large and diverse activity datasets. In this paper, we propose an intelligent activity data collection tool which has the ability to provide such datasets inexpensively without physically deploying the testbeds. It can be used as an inexpensive and alternative technique to collect human activity data. The tool provides a set of web interfaces to create a web-based activity data collection environment. It also provides a web-based experience sampling tool to take the user's activity input. The tool generates an activity log using its activity knowledge and the user-given inputs. The activity knowledge is mined from the web. We have performed two experiments to validate the tool's performance in producing reliable datasets.

Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy.

PubMed

Kirk, Jon; Shah, Nirav; Noll, Braxton; Stevens, Craig B; Lawler, Marshall; Mougeot, Farah B; Mougeot, Jean-Luc C

2018-08-01

Oral mucositis (OM) is a major dose-limiting side effect of chemotherapy and radiation used in cancer treatment. Due to the complex nature of OM, currently available drug-based treatments are of limited efficacy. Our objectives were (i) to determine genes and molecular pathways associated with OM and wound healing using computational tools and publicly available data and (ii) to identify drugs formulated for topical use targeting the relevant OM molecular pathways. OM and wound healing-associated genes were determined by text mining, and the intersection of the two gene sets was selected for gene ontology analysis using the GeneCodis program. Protein interaction network analysis was performed using STRING-db. Enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in OM. Our analysis identified 447 genes common to both the "OM" and "wound healing" text mining concepts. Gene enrichment analysis yielded 20 genes representing six pathways and targetable by a total of 32 drugs which could possibly be formulated for topical application. A manual search on ClinicalTrials.gov confirmed no relevant pathway/drug candidate had been overlooked. Twenty-five of the 32 drugs can directly affect the PTGS2 (COX-2) pathway, the pathway that has been targeted in previous clinical trials with limited success. Drug discovery using in silico text mining and pathway analysis tools can facilitate the identification of existing drugs that have the potential of topical administration to improve OM treatment.

TaylorActive--Examining the effectiveness of web-based personally-tailored videos to increase physical activity: a randomised controlled trial protocol.

PubMed

Vandelanotte, C; Short, C; Plotnikoff, R C; Hooker, C; Canoy, D; Rebar, A; Alley, S; Schoeppe, S; Mummery, W K; Duncan, M J

2015-10-05

Physical inactivity levels are unacceptably high and effective interventions that can increase physical activity in large populations at low cost are urgently needed. Web-based interventions that use computer-tailoring have shown to be effective, though people tend to 'skim' and 'scan' text on the Internet rather than thoroughly read it. The use of online videos is, however, popular and engaging. Therefore, the aim of this 3-group randomised controlled trial is to examine whether a web-based physical activity intervention that provides personally-tailored videos is more effective when compared with traditional personally-tailored text-based intervention and a control group. In total 510 Australians will be recruited through social media advertisements, e-mail and third party databases. Participants will be randomised to one of three groups: text-tailored, video-tailored, or control. All groups will gain access to the same web-based platform and a library containing brief physical activity articles. The text-tailored group will additionally have access to 8 sessions of personalised physical activity advice that is instantaneously generated based on responses to brief online surveys. The theory-based advice will be provided over a period of 3 months and address constructs such as self-efficacy, motivation, goal setting, intentions, social support, attitudes, barriers, outcome expectancies, relapse prevention and feedback on performance. Text-tailored participants will also be able to complete 7 action plans to help them plan what, when, where, who with, and how they will become more active. Participants in the video-tailored group will gain access to the same intervention content as those in the text-tailored group, however all sessions will be provided as personalised videos rather than text on a webpage. The control group will only gain access to the library with generic physical activity articles. The primary outcome is objectively measured physical activity. Secondary outcomes include website engagement and retention, quality of life, depression, anxiety, stress, sitting time, sleep and psychosocial correlates of physical activity. Outcomes will be measured at baseline, 3, and 9 months. This study presents an ideal opportunity to study the effectiveness of an isolated feature within a web-based physical activity intervention and the knowledge generated from this study will help to increase intervention effectiveness. Australian New-Zealand Clinical Trial Registry: ACTRN12615000057583 . Registered 22 January 2015. CQUniversity Ethics Project Number: H14/07-163.

Text-mining and information-retrieval services for molecular biology

PubMed Central

Krallinger, Martin; Valencia, Alfonso

2005-01-01

Text-mining in molecular biology - defined as the automatic extraction of information about genes, proteins and their functional relationships from text documents - has emerged as a hybrid discipline on the edges of the fields of information science, bioinformatics and computational linguistics. A range of text-mining applications have been developed recently that will improve access to knowledge for biologists and database annotators. PMID:15998455

A web-based quantitative signal detection system on adverse drug reaction in China.

PubMed

Li, Chanjuan; Xia, Jielai; Deng, Jianxiong; Chen, Wenge; Wang, Suzhen; Jiang, Jing; Chen, Guanquan

2009-07-01

To establish a web-based quantitative signal detection system for adverse drug reactions (ADRs) based on spontaneous reporting to the Guangdong province drug-monitoring database in China. Using Microsoft Visual Basic and Active Server Pages programming languages and SQL Server 2000, a web-based system with three software modules was programmed to perform data preparation and association detection, and to generate reports. Information component (IC), the internationally recognized measure of disproportionality for quantitative signal detection, was integrated into the system, and its capacity for signal detection was tested with ADR reports collected from 1 January 2002 to 30 June 2007 in Guangdong. A total of 2,496 associations including known signals were mined from the test database. Signals (e.g., cefradine-induced hematuria) were found early by using the IC analysis. In addition, 291 drug-ADR associations were alerted for the first time in the second quarter of 2007. The system can be used for the detection of significant associations from the Guangdong drug-monitoring database and could be an extremely useful adjunct to the expert assessment of very large numbers of spontaneously reported ADRs for the first time in China.

Text mining for traditional Chinese medical knowledge discovery: a survey.

PubMed

Zhou, Xuezhong; Peng, Yonghong; Liu, Baoyan

2010-08-01

Extracting meaningful information and knowledge from free text is the subject of considerable research interest in the machine learning and data mining fields. Text data mining (or text mining) has become one of the most active research sub-fields in data mining. Significant developments in the area of biomedical text mining during the past years have demonstrated its great promise for supporting scientists in developing novel hypotheses and new knowledge from the biomedical literature. Traditional Chinese medicine (TCM) provides a distinct methodology with which to view human life. It is one of the most complete and distinguished traditional medicines with a history of several thousand years of studying and practicing the diagnosis and treatment of human disease. It has been shown that the TCM knowledge obtained from clinical practice has become a significant complementary source of information for modern biomedical sciences. TCM literature obtained from the historical period and from modern clinical studies has recently been transformed into digital data in the form of relational databases or text documents, which provide an effective platform for information sharing and retrieval. This motivates and facilitates research and development into knowledge discovery approaches and to modernize TCM. In order to contribute to this still growing field, this paper presents (1) a comparative introduction to TCM and modern biomedicine, (2) a survey of the related information sources of TCM, (3) a review and discussion of the state of the art and the development of text mining techniques with applications to TCM, (4) a discussion of the research issues around TCM text mining and its future directions. Copyright 2010 Elsevier Inc. All rights reserved.

Managing biological networks by using text mining and computer-aided curation

NASA Astrophysics Data System (ADS)

Yu, Seok Jong; Cho, Yongseong; Lee, Min-Ho; Lim, Jongtae; Yoo, Jaesoo

2015-11-01

In order to understand a biological mechanism in a cell, a researcher should collect a huge number of protein interactions with experimental data from experiments and the literature. Text mining systems that extract biological interactions from papers have been used to construct biological networks for a few decades. Even though the text mining of literature is necessary to construct a biological network, few systems with a text mining tool are available for biologists who want to construct their own biological networks. We have developed a biological network construction system called BioKnowledge Viewer that can generate a biological interaction network by using a text mining tool and biological taggers. It also Boolean simulation software to provide a biological modeling system to simulate the model that is made with the text mining tool. A user can download PubMed articles and construct a biological network by using the Multi-level Knowledge Emergence Model (KMEM), MetaMap, and A Biomedical Named Entity Recognizer (ABNER) as a text mining tool. To evaluate the system, we constructed an aging-related biological network that consist 9,415 nodes (genes) by using manual curation. With network analysis, we found that several genes, including JNK, AP-1, and BCL-2, were highly related in aging biological network. We provide a semi-automatic curation environment so that users can obtain a graph database for managing text mining results that are generated in the server system and can navigate the network with BioKnowledge Viewer, which is freely available at http://bioknowledgeviewer.kisti.re.kr.

ESTminer: a Web interface for mining EST contig and cluster databases.

PubMed

Huang, Yecheng; Pumphrey, Janie; Gingle, Alan R

2005-03-01

ESTminer is a Web application and database schema for interactive mining of expressed sequence tag (EST) contig and cluster datasets. The Web interface contains a query frame that allows the selection of contigs/clusters with specific cDNA library makeup or a threshold number of members. The results are displayed as color-coded tree nodes, where the color indicates the fractional size of each cDNA library component. The nodes are expandable, revealing library statistics as well as EST or contig members, with links to sequence data, GenBank records or user configurable links. Also, the interface allows 'queries within queries' where the result set of a query is further filtered by the subsequent query. ESTminer is implemented in Java/JSP and the package, including MySQL and Oracle schema creation scripts, is available from http://cggc.agtec.uga.edu/Data/download.asp agingle@uga.edu.

An overview of the biocreative 2012 workshop track III: Interactive text mining task

USDA-ARS?s Scientific Manuscript database

An important question is how to make use of text mining to enhance the biocuration workflow. A number of groups have developed tools for text mining from a computer science/linguistics perspective and there are many initiatives to curate some aspect of biology from the literature. In some cases the ...