Using Text Mining to Characterize Online Discussion Facilitation

ERIC Educational Resources Information Center

Ming, Norma; Baumer, Eric

2011-01-01

Facilitating class discussions effectively is a critical yet challenging component of instruction, particularly in online environments where student and faculty interaction is limited. Our goals in this research were to identify facilitation strategies that encourage productive discussion, and to explore text mining techniques that can help…

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 .

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.

Text and Structural Data Mining of Influenza Mentions in Web and Social Media

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

Corley, Courtney D.; Cook, Diane; Mikler, Armin R.

Text and structural data mining of Web and social media (WSM) provides a novel disease surveillance resource and can identify online communities for targeted public health communications (PHC) to assure wide dissemination of pertinent information. WSM that mention influenza are harvested over a 24-week period, 5-October-2008 to 21-March-2009. Link analysis reveals communities for targeted PHC. Text mining is shown to identify trends in flu posts that correlate to real-world influenza-like-illness patient report data. We also bring to bear a graph-based data mining technique to detect anomalies among flu blogs connected by publisher type, links, and user-tags.

Application of text mining in the biomedical domain.

PubMed

Fleuren, Wilco W M; Alkema, Wynand

2015-03-01

In recent years the amount of experimental data that is produced in biomedical research and the number of papers that are being published in this field have grown rapidly. In order to keep up to date with developments in their field of interest and to interpret the outcome of experiments in light of all available literature, researchers turn more and more to the use of automated literature mining. As a consequence, text mining tools have evolved considerably in number and quality and nowadays can be used to address a variety of research questions ranging from de novo drug target discovery to enhanced biological interpretation of the results from high throughput experiments. In this paper we introduce the most important techniques that are used for a text mining and give an overview of the text mining tools that are currently being used and the type of problems they are typically applied for. Copyright © 2015 Elsevier Inc. All rights reserved.

Trends of E-Learning Research from 2000 to 2008: Use of Text Mining and Bibliometrics

ERIC Educational Resources Information Center

Hung, Jui-long

2012-01-01

This study investigated the longitudinal trends of e-learning research using text mining techniques. Six hundred and eighty-nine (689) refereed journal articles and proceedings were retrieved from the Science Citation Index/Social Science Citation Index database in the period from 2000 to 2008. All e-learning publications were grouped into two…

A sentence sliding window approach to extract protein annotations from biomedical articles

PubMed Central

Krallinger, Martin; Padron, Maria; Valencia, Alfonso

2005-01-01

Background Within the emerging field of text mining and statistical natural language processing (NLP) applied to biomedical articles, a broad variety of techniques have been developed during the past years. Nevertheless, there is still a great ned of comparative assessment of the performance of the proposed methods and the development of common evaluation criteria. This issue was addressed by the Critical Assessment of Text Mining Methods in Molecular Biology (BioCreative) contest. The aim of this contest was to assess the performance of text mining systems applied to biomedical texts including tools which recognize named entities such as genes and proteins, and tools which automatically extract protein annotations. Results The "sentence sliding window" approach proposed here was found to efficiently extract text fragments from full text articles containing annotations on proteins, providing the highest number of correctly predicted annotations. Moreover, the number of correct extractions of individual entities (i.e. proteins and GO terms) involved in the relationships used for the annotations was significantly higher than the correct extractions of the complete annotations (protein-function relations). Conclusion We explored the use of averaging sentence sliding windows for information extraction, especially in a context where conventional training data is unavailable. The combination of our approach with more refined statistical estimators and machine learning techniques might be a way to improve annotation extraction for future biomedical text mining applications. PMID:15960831

Redundancy and Novelty Mining in the Business Blogosphere

ERIC Educational Resources Information Center

Tsai, Flora S.; Chan, Kap Luk

2010-01-01

Purpose: The paper aims to explore the performance of redundancy and novelty mining in the business blogosphere, which has not been studied before. Design/methodology/approach: Novelty mining techniques are implemented to single out novel information out of a massive set of text documents. This paper adopted the mixed metric approach which…

Analysis of Nature of Science Included in Recent Popular Writing Using Text Mining Techniques

ERIC Educational Resources Information Center

Jiang, Feng; McComas, William F.

2014-01-01

This study examined the inclusion of nature of science (NOS) in popular science writing to determine whether it could serve supplementary resource for teaching NOS and to evaluate the accuracy of text mining and classification as a viable research tool in science education research. Four groups of documents published from 2001 to 2010 were…

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.

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.

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.

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.

Knowledge acquisition, semantic text mining, and security risks in health and biomedical informatics

PubMed Central

Huang, Jingshan; Dou, Dejing; Dang, Jiangbo; Pardue, J Harold; Qin, Xiao; Huan, Jun; Gerthoffer, William T; Tan, Ming

2012-01-01

Computational techniques have been adopted in medical and biological systems for a long time. There is no doubt that the development and application of computational methods will render great help in better understanding biomedical and biological functions. Large amounts of datasets have been produced by biomedical and biological experiments and simulations. In order for researchers to gain knowledge from original data, nontrivial transformation is necessary, which is regarded as a critical link in the chain of knowledge acquisition, sharing, and reuse. Challenges that have been encountered include: how to efficiently and effectively represent human knowledge in formal computing models, how to take advantage of semantic text mining techniques rather than traditional syntactic text mining, and how to handle security issues during the knowledge sharing and reuse. This paper summarizes the state-of-the-art in these research directions. We aim to provide readers with an introduction of major computing themes to be applied to the medical and biological research. PMID:22371823

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

Effective use of latent semantic indexing and computational linguistics in biological and biomedical applications.

PubMed

Chen, Hongyu; Martin, Bronwen; Daimon, Caitlin M; Maudsley, Stuart

2013-01-01

Text mining is rapidly becoming an essential technique for the annotation and analysis of large biological data sets. Biomedical literature currently increases at a rate of several thousand papers per week, making automated information retrieval methods the only feasible method of managing this expanding corpus. With the increasing prevalence of open-access journals and constant growth of publicly-available repositories of biomedical literature, literature mining has become much more effective with respect to the extraction of biomedically-relevant data. In recent years, text mining of popular databases such as MEDLINE has evolved from basic term-searches to more sophisticated natural language processing techniques, indexing and retrieval methods, structural analysis and integration of literature with associated metadata. In this review, we will focus on Latent Semantic Indexing (LSI), a computational linguistics technique increasingly used for a variety of biological purposes. It is noted for its ability to consistently outperform benchmark Boolean text searches and co-occurrence models at information retrieval and its power to extract indirect relationships within a data set. LSI has been used successfully to formulate new hypotheses, generate novel connections from existing data, and validate empirical data.

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

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…

Text mining and medicine: usefulness in respiratory diseases.

PubMed

Piedra, David; Ferrer, Antoni; Gea, Joaquim

2014-03-01

It is increasingly common to have medical information in electronic format. This includes scientific articles as well as clinical management reviews, and even records from health institutions with patient data. However, traditional instruments, both individual and institutional, are of little use for selecting the most appropriate information in each case, either in the clinical or research field. So-called text or data «mining» enables this huge amount of information to be managed, extracting it from various sources using processing systems (filtration and curation), integrating it and permitting the generation of new knowledge. This review aims to provide an overview of text and data mining, and of the potential usefulness of this bioinformatic technique in the exercise of care in respiratory medicine and in research in the same field. Copyright © 2013 SEPAR. Published by Elsevier Espana. All rights reserved.

Tagline: Information Extraction for Semi-Structured Text Elements in Medical Progress Notes

ERIC Educational Resources Information Center

Finch, Dezon Kile

2012-01-01

Text analysis has become an important research activity in the Department of Veterans Affairs (VA). Statistical text mining and natural language processing have been shown to be very effective for extracting useful information from medical documents. However, neither of these techniques is effective at extracting the information stored in…

Empirical advances with text mining of electronic health records.

PubMed

Delespierre, T; Denormandie, P; Bar-Hen, A; Josseran, L

2017-08-22

Korian is a private group specializing in medical accommodations for elderly and dependent people. A professional data warehouse (DWH) established in 2010 hosts all of the residents' data. Inside this information system (IS), clinical narratives (CNs) were used only by medical staff as a residents' care linking tool. The objective of this study was to show that, through qualitative and quantitative textual analysis of a relatively small physiotherapy and well-defined CN sample, it was possible to build a physiotherapy corpus and, through this process, generate a new body of knowledge by adding relevant information to describe the residents' care and lives. Meaningful words were extracted through Standard Query Language (SQL) with the LIKE function and wildcards to perform pattern matching, followed by text mining and a word cloud using R® packages. Another step involved principal components and multiple correspondence analyses, plus clustering on the same residents' sample as well as on other health data using a health model measuring the residents' care level needs. By combining these techniques, physiotherapy treatments could be characterized by a list of constructed keywords, and the residents' health characteristics were built. Feeding defects or health outlier groups could be detected, physiotherapy residents' data and their health data were matched, and differences in health situations showed qualitative and quantitative differences in physiotherapy narratives. This textual experiment using a textual process in two stages showed that text mining and data mining techniques provide convenient tools to improve residents' health and quality of care by adding new, simple, useable data to the electronic health record (EHR). When used with a normalized physiotherapy problem list, text mining through information extraction (IE), named entity recognition (NER) and data mining (DM) can provide a real advantage to describe health care, adding new medical material and helping to integrate the EHR system into the health staff work environment.

PLAN2L: a web tool for integrated text mining and literature-based bioentity relation extraction.

PubMed

Krallinger, Martin; Rodriguez-Penagos, Carlos; Tendulkar, Ashish; Valencia, Alfonso

2009-07-01

There is an increasing interest in using literature mining techniques to complement information extracted from annotation databases or generated by bioinformatics applications. Here we present PLAN2L, a web-based online search system that integrates text mining and information extraction techniques to access systematically information useful for analyzing genetic, cellular and molecular aspects of the plant model organism Arabidopsis thaliana. Our system facilitates a more efficient retrieval of information relevant to heterogeneous biological topics, from implications in biological relationships at the level of protein interactions and gene regulation, to sub-cellular locations of gene products and associations to cellular and developmental processes, i.e. cell cycle, flowering, root, leaf and seed development. Beyond single entities, also predefined pairs of entities can be provided as queries for which literature-derived relations together with textual evidences are returned. PLAN2L does not require registration and is freely accessible at http://zope.bioinfo.cnio.es/plan2l.

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.

Integrating unified medical language system and association mining techniques into relevance feedback for biomedical literature search.

PubMed

Ji, Yanqing; Ying, Hao; Tran, John; Dews, Peter; Massanari, R Michael

2016-07-19

Finding highly relevant articles from biomedical databases is challenging not only because it is often difficult to accurately express a user's underlying intention through keywords but also because a keyword-based query normally returns a long list of hits with many citations being unwanted by the user. This paper proposes a novel biomedical literature search system, called BiomedSearch, which supports complex queries and relevance feedback. The system employed association mining techniques to build a k-profile representing a user's relevance feedback. More specifically, we developed a weighted interest measure and an association mining algorithm to find the strength of association between a query and each concept in the article(s) selected by the user as feedback. The top concepts were utilized to form a k-profile used for the next-round search. BiomedSearch relies on Unified Medical Language System (UMLS) knowledge sources to map text files to standard biomedical concepts. It was designed to support queries with any levels of complexity. A prototype of BiomedSearch software was made and it was preliminarily evaluated using the Genomics data from TREC (Text Retrieval Conference) 2006 Genomics Track. Initial experiment results indicated that BiomedSearch increased the mean average precision (MAP) for a set of queries. With UMLS and association mining techniques, BiomedSearch can effectively utilize users' relevance feedback to improve the performance of biomedical literature search.

A Review of Recent Advancement in Integrating Omics Data with Literature Mining towards Biomedical Discoveries

PubMed Central

Raja, Kalpana; Patrick, Matthew; Gao, Yilin; Madu, Desmond; Yang, Yuyang

2017-01-01

In the past decade, the volume of “omics” data generated by the different high-throughput technologies has expanded exponentially. The managing, storing, and analyzing of this big data have been a great challenge for the researchers, especially when moving towards the goal of generating testable data-driven hypotheses, which has been the promise of the high-throughput experimental techniques. Different bioinformatics approaches have been developed to streamline the downstream analyzes by providing independent information to interpret and provide biological inference. Text mining (also known as literature mining) is one of the commonly used approaches for automated generation of biological knowledge from the huge number of published articles. In this review paper, we discuss the recent advancement in approaches that integrate results from omics data and information generated from text mining approaches to uncover novel biomedical information. PMID:28331849

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.

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

[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.

Intelligent bar chart plagiarism detection in documents.

PubMed

Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Rehman, Amjad; Alkawaz, Mohammed Hazim; Saba, Tanzila; Al-Rodhaan, Mznah; Al-Dhelaan, Abdullah

2014-01-01

This paper presents a novel features mining approach from documents that could not be mined via optical character recognition (OCR). By identifying the intimate relationship between the text and graphical components, the proposed technique pulls out the Start, End, and Exact values for each bar. Furthermore, the word 2-gram and Euclidean distance methods are used to accurately detect and determine plagiarism in bar charts.

Intelligent Bar Chart Plagiarism Detection in Documents

PubMed Central

Al-Dabbagh, Mohammed Mumtaz; Salim, Naomie; Alkawaz, Mohammed Hazim; Saba, Tanzila; Al-Rodhaan, Mznah; Al-Dhelaan, Abdullah

2014-01-01

This paper presents a novel features mining approach from documents that could not be mined via optical character recognition (OCR). By identifying the intimate relationship between the text and graphical components, the proposed technique pulls out the Start, End, and Exact values for each bar. Furthermore, the word 2-gram and Euclidean distance methods are used to accurately detect and determine plagiarism in bar charts. PMID:25309952

Signal Detection Framework Using Semantic Text Mining Techniques

ERIC Educational Resources Information Center

Sudarsan, Sithu D.

2009-01-01

Signal detection is a challenging task for regulatory and intelligence agencies. Subject matter experts in those agencies analyze documents, generally containing narrative text in a time bound manner for signals by identification, evaluation and confirmation, leading to follow-up action e.g., recalling a defective product or public advisory for…

Advances in Machine Learning and Data Mining for Astronomy

NASA Astrophysics Data System (ADS)

Way, Michael J.; Scargle, Jeffrey D.; Ali, Kamal M.; Srivastava, Ashok N.

2012-03-01

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

Generating a Spanish Affective Dictionary with Supervised Learning Techniques

ERIC Educational Resources Information Center

Bermudez-Gonzalez, Daniel; Miranda-Jiménez, Sabino; García-Moreno, Raúl-Ulises; Calderón-Nepamuceno, Dora

2016-01-01

Nowadays, machine learning techniques are being used in several Natural Language Processing (NLP) tasks such as Opinion Mining (OM). OM is used to analyse and determine the affective orientation of texts. Usually, OM approaches use affective dictionaries in order to conduct sentiment analysis. These lexicons are labeled manually with affective…

Evaluation of the mining techniques in constructing a traditional Chinese-language nursing recording system.

PubMed

Liao, Pei-Hung; Chu, William; Chu, Woei-Chyn

2014-05-01

In 2009, the Department of Health, part of Taiwan's Executive Yuan, announced the advent of electronic medical records to reduce medical expenses and facilitate the international exchange of medical record information. An information technology platform for nursing records in medical institutions was then quickly established, which improved nursing information systems and electronic databases. The purpose of the present study was to explore the usability of the data mining techniques to enhance completeness and ensure consistency of nursing records in the database system.First, the study used a Chinese word-segmenting system on common and special terms often used by the nursing staff. We also used text-mining techniques to collect keywords and create a keyword lexicon. We then used an association rule and artificial neural network to measure the correlation and forecasting capability for keywords. Finally, nursing staff members were provided with an on-screen pop-up menu to use when establishing nursing records. Our study found that by using mining techniques we were able to create a powerful keyword lexicon and establish a forecasting model for nursing diagnoses, ensuring the consistency of nursing terminology and improving the nursing staff's work efficiency and productivity.

Analysis of Nature of Science Included in Recent Popular Writing Using Text Mining Techniques

NASA Astrophysics Data System (ADS)

Jiang, Feng; McComas, William F.

2014-09-01

This study examined the inclusion of nature of science (NOS) in popular science writing to determine whether it could serve supplementary resource for teaching NOS and to evaluate the accuracy of text mining and classification as a viable research tool in science education research. Four groups of documents published from 2001 to 2010 were analyzed: Scientific American, Discover magazine, winners of the Royal Society Winton Prize for Science Books, and books from NSTA's list of Outstanding Science Trade Books. Computer analysis categorized passages in the selected documents based on their inclusions of NOS. Human analysis assessed the frequency, context, coverage, and accuracy of the inclusions of NOS within computer identified NOS passages. NOS was rarely addressed in selected document sets but somewhat more frequently addressed in the letters section of the two magazines. This result suggests that readers seem interested in the discussion of NOS-related themes. In the popular science books analyzed, NOS presentations were found more likely to be aggregated in the beginning and the end of the book, rather than scattered throughout. The most commonly addressed NOS elements in the analyzed documents are science and society and empiricism in science. Only one inaccurate presentation of NOS were identified in all analyzed documents. The text mining technique demonstrated exciting performance, which invites more applications of the technique to analyze other aspects of science textbooks, popular science writing, or other materials involved in science teaching and learning.

Biomedical text mining for research rigor and integrity: tasks, challenges, directions.

PubMed

Kilicoglu, Halil

2017-06-13

An estimated quarter of a trillion US dollars is invested in the biomedical research enterprise annually. There is growing alarm that a significant portion of this investment is wasted because of problems in reproducibility of research findings and in the rigor and integrity of research conduct and reporting. Recent years have seen a flurry of activities focusing on standardization and guideline development to enhance the reproducibility and rigor of biomedical research. Research activity is primarily communicated via textual artifacts, ranging from grant applications to journal publications. These artifacts can be both the source and the manifestation of practices leading to research waste. For example, an article may describe a poorly designed experiment, or the authors may reach conclusions not supported by the evidence presented. In this article, we pose the question of whether biomedical text mining techniques can assist the stakeholders in the biomedical research enterprise in doing their part toward enhancing research integrity and rigor. In particular, we identify four key areas in which text mining techniques can make a significant contribution: plagiarism/fraud detection, ensuring adherence to reporting guidelines, managing information overload and accurate citation/enhanced bibliometrics. We review the existing methods and tools for specific tasks, if they exist, or discuss relevant research that can provide guidance for future work. With the exponential increase in biomedical research output and the ability of text mining approaches to perform automatic tasks at large scale, we propose that such approaches can support tools that promote responsible research practices, providing significant benefits for the biomedical research enterprise. Published by Oxford University Press 2017. This work is written by a US Government employee and is in the public domain in the US.

TOY SAFETY SURVEILLANCE FROM ONLINE REVIEWS

PubMed Central

Winkler, Matt; Abrahams, Alan S.; Gruss, Richard; Ehsani, Johnathan P.

2016-01-01

Toy-related injuries account for a significant number of childhood injuries and the prevention of these injuries remains a goal for regulatory agencies and manufacturers. Text-mining is an increasingly prevalent method for uncovering the significance of words using big data. This research sets out to determine the effectiveness of text-mining in uncovering potentially dangerous children’s toys. We develop a danger word list, also known as a ‘smoke word’ list, from injury and recall text narratives. We then use the smoke word lists to score over one million Amazon reviews, with the top scores denoting potential safety concerns. We compare the smoke word list to conventional sentiment analysis techniques, in terms of both word overlap and effectiveness. We find that smoke word lists are highly distinct from conventional sentiment dictionaries and provide a statistically significant method for identifying safety concerns in children’s toy reviews. Our findings indicate that text-mining is, in fact, an effective method for the surveillance of safety concerns in children’s toys and could be a gateway to effective prevention of toy-product-related injuries. PMID:27942092

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.

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.

Text-mining as a methodology to assess eating disorder-relevant factors: Comparing mentions of fitness tracking technology across online communities.

PubMed

McCaig, Duncan; Bhatia, Sudeep; Elliott, Mark T; Walasek, Lukasz; Meyer, Caroline

2018-05-07

Text-mining offers a technique to identify and extract information from a large corpus of textual data. As an example, this study presents the application of text-mining to assess and compare interest in fitness tracking technology across eating disorder and health-related online communities. A list of fitness tracking technology terms was developed, and communities (i.e., 'subreddits') on a large online discussion platform (Reddit) were compared regarding the frequency with which these terms occurred. The corpus used in this study comprised all comments posted between May 2015 and January 2018 (inclusive) on six subreddits-three eating disorder-related, and three relating to either fitness, weight-management, or nutrition. All comments relating to the same 'thread' (i.e., conversation) were concatenated, and formed the cases used in this study (N = 377,276). Within the eating disorder-related subreddits, the findings indicated that a 'pro-eating disorder' subreddit, which is less recovery focused than the other eating disorder subreddits, had the highest frequency of fitness tracker terms. Across all subreddits, the weight-management subreddit had the highest frequency of the fitness tracker terms' occurrence, and MyFitnessPal was the most frequently mentioned fitness tracker. The technique exemplified here can potentially be used to assess group differences to identify at-risk populations, generate and explore clinically relevant research questions in populations who are difficult to recruit, and scope an area for which there is little extant literature. The technique also facilitates methodological triangulation of research findings obtained through more 'traditional' techniques, such as surveys or interviews. © 2018 Wiley Periodicals, Inc.

Comparative Analysis of Document level Text Classification Algorithms using R

NASA Astrophysics Data System (ADS)

Syamala, Maganti; Nalini, N. J., Dr; Maguluri, Lakshamanaphaneendra; Ragupathy, R., Dr.

2017-08-01

From the past few decades there has been tremendous volumes of data available in Internet either in structured or unstructured form. Also, there is an exponential growth of information on Internet, so there is an emergent need of text classifiers. Text mining is an interdisciplinary field which draws attention on information retrieval, data mining, machine learning, statistics and computational linguistics. And to handle this situation, a wide range of supervised learning algorithms has been introduced. Among all these K-Nearest Neighbor(KNN) is efficient and simplest classifier in text classification family. But KNN suffers from imbalanced class distribution and noisy term features. So, to cope up with this challenge we use document based centroid dimensionality reduction(CentroidDR) using R Programming. By combining these two text classification techniques, KNN and Centroid classifiers, we propose a scalable and effective flat classifier, called MCenKNN which works well substantially better than CenKNN.

Combining Natural Language Processing and Statistical Text Mining: A Study of Specialized versus Common Languages

ERIC Educational Resources Information Center

Jarman, Jay

2011-01-01

This dissertation focuses on developing and evaluating hybrid approaches for analyzing free-form text in the medical domain. This research draws on natural language processing (NLP) techniques that are used to parse and extract concepts based on a controlled vocabulary. Once important concepts are extracted, additional machine learning algorithms,…

Analysis of ingredient lists of commercially available gluten-free and gluten-containing food products using the text mining technique.

PubMed

do Nascimento, Amanda Bagolin; Fiates, Giovanna Medeiros Rataichesck; Dos Anjos, Adilson; Teixeira, Evanilda

2013-03-01

Ingredients mentioned on the labels of commercially available packaged gluten-free and similar gluten-containing food products were analyzed and compared, using the text mining technique. A total of 324 products' labels were analyzed for content (162 from gluten-free products), and ingredient diversity in gluten-free products was 28% lower. Raw materials used as ingredients of gluten-free products were limited to five varieties: rice, cassava, corn, soy, and potato. Sugar was the most frequently mentioned ingredient on both types of products' labels. Salt and sodium also were among these ingredients. Presence of hydrocolloids, enzymes or raw materials of high nutritional content such as pseudocereals, suggested by academic studies as alternatives to improve nutritional and sensorial quality of gluten-free food products, was not identified in the present study. Nutritional quality of gluten-free diets and health of celiac patients may be compromised.

Training and Employment of Land Mine and Booby Trap Detector Dogs. Volume II

DTIC Science & Technology

1976-09-01

1Of injury, disease, and other physical abnormalities. All obligatory Li [1/ • ,i 4’: vaccinations should 1•e current ( canine distemper , infectious...as a procedures manual and reference text to be used during the training of initially naive canines v for land mine and booby trap detection service... canine L. training contexts. * • The techniques and procedures elaborated in the present docu- ment were developed for the United States Army Mobility

Text Mining of UU-ITE Implementation in Indonesia

NASA Astrophysics Data System (ADS)

Hakim, Lukmanul; Kusumasari, Tien F.; Lubis, Muharman

2018-04-01

At present, social media and networks act as one of the main platforms for sharing information, idea, thought and opinions. Many people share their knowledge and express their views on the specific topics or current hot issues that interest them. The social media texts have rich information about the complaints, comments, recommendation and suggestion as the automatic reaction or respond to government initiative or policy in order to overcome certain issues.This study examines the sentiment from netizensas part of citizen who has vocal sound about the implementation of UU ITE as the first cyberlaw in Indonesia as a means to identify the current tendency of citizen perception. To perform text mining techniques, this study used Twitter Rest API while R programming was utilized for the purpose of classification analysis based on hierarchical cluster.

Machine learning approaches to analysing textual injury surveillance data: a systematic review.

PubMed

Vallmuur, Kirsten

2015-06-01

To synthesise recent research on the use of machine learning approaches to mining textual injury surveillance data. Systematic review. The electronic databases which were searched included PubMed, Cinahl, Medline, Google Scholar, and Proquest. The bibliography of all relevant articles was examined and associated articles were identified using a snowballing technique. For inclusion, articles were required to meet the following criteria: (a) used a health-related database, (b) focused on injury-related cases, AND used machine learning approaches to analyse textual data. The papers identified through the search were screened resulting in 16 papers selected for review. Articles were reviewed to describe the databases and methodology used, the strength and limitations of different techniques, and quality assurance approaches used. Due to heterogeneity between studies meta-analysis was not performed. Occupational injuries were the focus of half of the machine learning studies and the most common methods described were Bayesian probability or Bayesian network based methods to either predict injury categories or extract common injury scenarios. Models were evaluated through either comparison with gold standard data or content expert evaluation or statistical measures of quality. Machine learning was found to provide high precision and accuracy when predicting a small number of categories, was valuable for visualisation of injury patterns and prediction of future outcomes. However, difficulties related to generalizability, source data quality, complexity of models and integration of content and technical knowledge were discussed. The use of narrative text for injury surveillance has grown in popularity, complexity and quality over recent years. With advances in data mining techniques, increased capacity for analysis of large databases, and involvement of computer scientists in the injury prevention field, along with more comprehensive use and description of quality assurance methods in text mining approaches, it is likely that we will see a continued growth and advancement in knowledge of text mining in the injury field. Copyright © 2015 Elsevier Ltd. All rights reserved.

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action.

PubMed

Papamokos, George; Silins, Ilona

2016-01-01

There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens.

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action

PubMed Central

Papamokos, George; Silins, Ilona

2016-01-01

There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens. PMID:27625608

Improve Data Mining and Knowledge Discovery Through the Use of MatLab

NASA Technical Reports Server (NTRS)

Shaykhian, Gholam Ali; Martin, Dawn (Elliott); Beil, Robert

2011-01-01

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

Improve Data Mining and Knowledge Discovery through the use of MatLab

NASA Technical Reports Server (NTRS)

Shaykahian, Gholan Ali; Martin, Dawn Elliott; Beil, Robert

2011-01-01

Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(TradeMark)(MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.

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

PKDE4J: Entity and relation extraction for public knowledge discovery.

PubMed

Song, Min; Kim, Won Chul; Lee, Dahee; Heo, Go Eun; Kang, Keun Young

2015-10-01

Due to an enormous number of scientific publications that cannot be handled manually, there is a rising interest in text-mining techniques for automated information extraction, especially in the biomedical field. Such techniques provide effective means of information search, knowledge discovery, and hypothesis generation. Most previous studies have primarily focused on the design and performance improvement of either named entity recognition or relation extraction. In this paper, we present PKDE4J, a comprehensive text-mining system that integrates dictionary-based entity extraction and rule-based relation extraction in a highly flexible and extensible framework. Starting with the Stanford CoreNLP, we developed the system to cope with multiple types of entities and relations. The system also has fairly good performance in terms of accuracy as well as the ability to configure text-processing components. We demonstrate its competitive performance by evaluating it on many corpora and found that it surpasses existing systems with average F-measures of 85% for entity extraction and 81% for relation extraction. Copyright © 2015 Elsevier Inc. All rights reserved.

Unsupervised text mining for assessing and augmenting GWAS results.

PubMed

Ailem, Melissa; Role, François; Nadif, Mohamed; Demenais, Florence

2016-04-01

Text mining can assist in the analysis and interpretation of large-scale biomedical data, helping biologists to quickly and cheaply gain confirmation of hypothesized relationships between biological entities. We set this question in the context of genome-wide association studies (GWAS), an actively emerging field that contributed to identify many genes associated with multifactorial diseases. These studies allow to identify groups of genes associated with the same phenotype, but provide no information about the relationships between these genes. Therefore, our objective is to leverage unsupervised text mining techniques using text-based cosine similarity comparisons and clustering applied to candidate and random gene vectors, in order to augment the GWAS results. We propose a generic framework which we used to characterize the relationships between 10 genes reported associated with asthma by a previous GWAS. The results of this experiment showed that the similarities between these 10 genes were significantly stronger than would be expected by chance (one-sided p-value<0.01). The clustering of observed and randomly selected gene also allowed to generate hypotheses about potential functional relationships between these genes and thus contributed to the discovery of new candidate genes for asthma. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

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

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…

Overview of the gene ontology task at BioCreative IV.

PubMed

Mao, Yuqing; Van Auken, Kimberly; Li, Donghui; Arighi, Cecilia N; McQuilton, Peter; Hayman, G Thomas; Tweedie, Susan; Schaeffer, Mary L; Laulederkind, Stanley J F; Wang, Shur-Jen; Gobeill, Julien; Ruch, Patrick; Luu, Anh Tuan; Kim, Jung-Jae; Chiang, Jung-Hsien; Chen, Yu-De; Yang, Chia-Jung; Liu, Hongfang; Zhu, Dongqing; Li, Yanpeng; Yu, Hong; Emadzadeh, Ehsan; Gonzalez, Graciela; Chen, Jian-Ming; Dai, Hong-Jie; Lu, Zhiyong

2014-01-01

Gene ontology (GO) annotation is a common task among model organism databases (MODs) for capturing gene function data from journal articles. It is a time-consuming and labor-intensive task, and is thus often considered as one of the bottlenecks in literature curation. There is a growing need for semiautomated or fully automated GO curation techniques that will help database curators to rapidly and accurately identify gene function information in full-length articles. Despite multiple attempts in the past, few studies have proven to be useful with regard to assisting real-world GO curation. The shortage of sentence-level training data and opportunities for interaction between text-mining developers and GO curators has limited the advances in algorithm development and corresponding use in practical circumstances. To this end, we organized a text-mining challenge task for literature-based GO annotation in BioCreative IV. More specifically, we developed two subtasks: (i) to automatically locate text passages that contain GO-relevant information (a text retrieval task) and (ii) to automatically identify relevant GO terms for the genes in a given article (a concept-recognition task). With the support from five MODs, we provided teams with >4000 unique text passages that served as the basis for each GO annotation in our task data. Such evidence text information has long been recognized as critical for text-mining algorithm development but was never made available because of the high cost of curation. In total, seven teams participated in the challenge task. From the team results, we conclude that the state of the art in automatically mining GO terms from literature has improved over the past decade while much progress is still needed for computer-assisted GO curation. Future work should focus on addressing remaining technical challenges for improved performance of automatic GO concept recognition and incorporating practical benefits of text-mining tools into real-world GO annotation. http://www.biocreative.org/tasks/biocreative-iv/track-4-GO/. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

Sentiment analysis of Arabic tweets using text mining techniques

NASA Astrophysics Data System (ADS)

Al-Horaibi, Lamia; Khan, Muhammad Badruddin

2016-07-01

Sentiment analysis has become a flourishing field of text mining and natural language processing. Sentiment analysis aims to determine whether the text is written to express positive, negative, or neutral emotions about a certain domain. Most sentiment analysis researchers focus on English texts, with very limited resources available for other complex languages, such as Arabic. In this study, the target was to develop an initial model that performs satisfactorily and measures Arabic Twitter sentiment by using machine learning approach, Naïve Bayes and Decision Tree for classification algorithms. The datasets used contains more than 2,000 Arabic tweets collected from Twitter. We performed several experiments to check the performance of the two algorithms classifiers using different combinations of text-processing functions. We found that available facilities for Arabic text processing need to be made from scratch or improved to develop accurate classifiers. The small functionalities developed by us in a Python language environment helped improve the results and proved that sentiment analysis in the Arabic domain needs lot of work on the lexicon side.

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.

Trends in Fetal Medicine: A 10-Year Bibliometric Analysis of Prenatal Diagnosis

PubMed Central

Dhombres, Ferdinand; Bodenreider, Olivier

2018-01-01

The objective is to automatically identify trends in Fetal Medicine over the past 10 years through a bibliometric analysis of articles published in Prenatal Diagnosis, using text mining techniques. We processed 2,423 full-text articles published in Prenatal Diagnosis between 2006 and 2015. We extracted salient terms, calculated their frequencies over time, and established evolution profiles for terms, from which we derived falling, stable, and rising trends. We identified 618 terms with a falling trend, 2,142 stable terms, and 839 terms with a rising trend. Terms with increasing frequencies include those related to statistics and medical study design. The most recent of these terms reflect the new opportunities of next- generation sequencing. Many terms related to cytogenetics exhibit a falling trend. A bibliometric analysis based on text mining effectively supports identification of trends over time. This scalable approach is complementary to analyses based on metadata or expert opinion. PMID:29295220

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

[Application of text mining approach to pre-education prior to clinical practice].

PubMed

Koinuma, Masayoshi; Koike, Katsuya; Nakamura, Hitoshi

2008-06-01

We developed a new survey analysis technique to understand students' actual aims for effective pretraining prior to clinical practice. We asked third-year undergraduate students to write fixed-style complete and free sentences on "preparation of drug dispensing." Then, we converted their sentence data in to text style and performed Japanese-language morphologic analysis on the data using language analysis software. We classified key words, which were created on the basis of the word class information of the Japanese language morphologic analysis, into categories based on causes and characteristics. In addition to this, we classified the characteristics into six categories consisting of those concepts including "knowledge," "skill and attitude," "image," etc. with the KJ method technique. The results showed that the awareness of students of "preparation of drug dispensing" tended to be approximately three-fold more frequent in "skill and attitude," "risk," etc. than in "knowledge." Regarding the characteristics in the category of the "image," words like "hard," "challenging," "responsibility," "life," etc. frequently occurred. The results of corresponding analysis showed that the characteristics of the words "knowledge" and "skills and attitude" were independent. As the result of developing a cause-and-effect diagram, it was demonstrated that the phase "hanging tough" described most of the various factors. We thus could understand students' actual feelings by applying text-mining as a new survey analysis technique.

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.

Answer Mining from On-Line Documents

DTIC Science & Technology

2001-01-01

successions occurred at IBM in 1999? In addition, questions may also ask about de- velopments of events or trends that are usually answered by a text ... summary . Since data produc- ing these summaries can be sourced in different documents, summary fusion techniques as pro- posed in (Radev and McKeown

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.

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

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.

Introducing Text Analytics as a Graduate Business School Course

ERIC Educational Resources Information Center

Edgington, Theresa M.

2011-01-01

Text analytics refers to the process of analyzing unstructured data from documented sources, including open-ended surveys, blogs, and other types of web dialog. Text analytics has enveloped the concept of text mining, an analysis approach influenced heavily from data mining. While text mining has been covered extensively in various computer…

Underground Mining Method Selection Using WPM and PROMETHEE

NASA Astrophysics Data System (ADS)

Balusa, Bhanu Chander; Singam, Jayanthu

2018-04-01

The aim of this paper is to represent the solution to the problem of selecting suitable underground mining method for the mining industry. It is achieved by using two multi-attribute decision making techniques. These two techniques are weighted product method (WPM) and preference ranking organization method for enrichment evaluation (PROMETHEE). In this paper, analytic hierarchy process is used for weight's calculation of the attributes (i.e. parameters which are used in this paper). Mining method selection depends on physical parameters, mechanical parameters, economical parameters and technical parameters. WPM and PROMETHEE techniques have the ability to consider the relationship between the parameters and mining methods. The proposed techniques give higher accuracy and faster computation capability when compared with other decision making techniques. The proposed techniques are presented to determine the effective mining method for bauxite mine. The results of these techniques are compared with methods used in the earlier research works. The results show, conventional cut and fill method is the most suitable mining method.

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.

Review of Mobile Learning Trends 2010-2015: A Meta-Analysis

ERIC Educational Resources Information Center

Chee, Ken Nee; Yahaya, Noraffandy; Ibrahim, Nor Hasniza; Hasan, Mohamed Noor

2017-01-01

This study examined the longitudinal trends of mobile learning (M-Learning) research using text mining techniques in a more comprehensive manner. One hundred and forty four (144) refereed journal articles were retrieved and analyzed from the Social Science Citation Index database selected from top six major educational technology-based learning…

Supporting read-across predictions of chemical toxicity using high-throughput text-mining (ACS 2017 Spring meeting )

EPA Science Inventory

Read-across is a technique used to fill data gaps within chemical safety assessments. It is based on the premise that chemicals with similar structures are likely to have similar biological activities. Known information on the property of a chemical (source) is used to make a pre...

Integration of Text- and Data-Mining Technologies for Use in Banking Applications

NASA Astrophysics Data System (ADS)

Maslankowski, Jacek

Unstructured data, most of it in the form of text files, typically accounts for 85% of an organization's knowledge stores, but it's not always easy to find, access, analyze or use (Robb 2004). That is why it is important to use solutions based on text and data mining. This solution is known as duo mining. This leads to improve management based on knowledge owned in organization. The results are interesting. Data mining provides to lead with structuralized data, usually powered from data warehouses. Text mining, sometimes called web mining, looks for patterns in unstructured data — memos, document and www. Integrating text-based information with structured data enriches predictive modeling capabilities and provides new stores of insightful and valuable information for driving business and research initiatives forward.

ChemBrowser: a flexible framework for mining chemical documents.

PubMed

Wu, Xian; Zhang, Li; Chen, Ying; Rhodes, James; Griffin, Thomas D; Boyer, Stephen K; Alba, Alfredo; Cai, Keke

2010-01-01

The ability to extract chemical and biological entities and relations from text documents automatically has great value to biochemical research and development activities. The growing maturity of text mining and artificial intelligence technologies shows promise in enabling such automatic chemical entity extraction capabilities (called "Chemical Annotation" in this paper). Many techniques have been reported in the literature, ranging from dictionary and rule-based techniques to machine learning approaches. In practice, we found that no single technique works well in all cases. A combinatorial approach that allows one to quickly compose different annotation techniques together for a given situation is most effective. In this paper, we describe the key challenges we face in real-world chemical annotation scenarios. We then present a solution called ChemBrowser which has a flexible framework for chemical annotation. ChemBrowser includes a suite of customizable processing units that might be utilized in a chemical annotator, a high-level language that describes the composition of various processing units that would form a chemical annotator, and an execution engine that translates the composition language to an actual annotator that can generate annotation results for a given set of documents. We demonstrate the impact of this approach by tailoring an annotator for extracting chemical names from patent documents and show how this annotator can be easily modified with simple configuration alone.

ANDSystem: an Associative Network Discovery System for automated literature mining in the field of biology

PubMed Central

2015-01-01

Background Sufficient knowledge of molecular and genetic interactions, which comprise the entire basis of the functioning of living systems, is one of the necessary requirements for successfully answering almost any research question in the field of biology and medicine. To date, more than 24 million scientific papers can be found in PubMed, with many of them containing descriptions of a wide range of biological processes. The analysis of such tremendous amounts of data requires the use of automated text-mining approaches. Although a handful of tools have recently been developed to meet this need, none of them provide error-free extraction of highly detailed information. Results The ANDSystem package was developed for the reconstruction and analysis of molecular genetic networks based on an automated text-mining technique. It provides a detailed description of the various types of interactions between genes, proteins, microRNA's, metabolites, cellular components, pathways and diseases, taking into account the specificity of cell lines and organisms. Although the accuracy of ANDSystem is comparable to other well known text-mining tools, such as Pathway Studio and STRING, it outperforms them in having the ability to identify an increased number of interaction types. Conclusion The use of ANDSystem, in combination with Pathway Studio and STRING, can improve the quality of the automated reconstruction of molecular and genetic networks. ANDSystem should provide a useful tool for researchers working in a number of different fields, including biology, biotechnology, pharmacology and medicine. PMID:25881313

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

Working with Data: Discovering Knowledge through Mining and Analysis; Systematic Knowledge Management and Knowledge Discovery; Text Mining; Methodological Approach in Discovering User Search Patterns through Web Log Analysis; Knowledge Discovery in Databases Using Formal Concept Analysis; Knowledge Discovery with a Little Perspective.

ERIC Educational Resources Information Center

Qin, Jian; Jurisica, Igor; Liddy, Elizabeth D.; Jansen, Bernard J; Spink, Amanda; Priss, Uta; Norton, Melanie J.

2000-01-01

These six articles discuss knowledge discovery in databases (KDD). Topics include data mining; knowledge management systems; applications of knowledge discovery; text and Web mining; text mining and information retrieval; user search patterns through Web log analysis; concept analysis; data collection; and data structure inconsistency. (LRW)

Sampling and monitoring for the mine life cycle

USGS Publications Warehouse

McLemore, Virginia T.; Smith, Kathleen S.; Russell, Carol C.

2014-01-01

Sampling and Monitoring for the Mine Life Cycle provides an overview of sampling for environmental purposes and monitoring of environmentally relevant variables at mining sites. It focuses on environmental sampling and monitoring of surface water, and also considers groundwater, process water streams, rock, soil, and other media including air and biological organisms. The handbook includes an appendix of technical summaries written by subject-matter experts that describe field measurements, collection methods, and analytical techniques and procedures relevant to environmental sampling and monitoring.The sixth of a series of handbooks on technologies for management of metal mine and metallurgical process drainage, this handbook supplements and enhances current literature and provides an awareness of the critical components and complexities involved in environmental sampling and monitoring at the mine site. It differs from most information sources by providing an approach to address all types of mining influenced water and other sampling media throughout the mine life cycle.Sampling and Monitoring for the Mine Life Cycle is organized into a main text and six appendices that are an integral part of the handbook. Sidebars and illustrations are included to provide additional detail about important concepts, to present examples and brief case studies, and to suggest resources for further information. Extensive references are included.

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

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.

Protein-protein interaction predictions using text mining methods.

PubMed

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

2015-03-01

It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, despite the plethora of various high-throughput experimental approaches for detecting protein-protein interactions, many computational methods aiming to predict new interactions have appeared and gained interest. In this review, we focus on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases. We discuss their strengths, their weaknesses and how they complement existing experimental techniques by simultaneously commenting on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools. Copyright © 2014 Elsevier Inc. All rights reserved.

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

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

Chemical named entities recognition: a review on approaches and applications

PubMed Central

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. PMID:24834132

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.

Text Mining of the Classical Medical Literature for Medicines That Show Potential in Diabetic Nephropathy

PubMed Central

Zhang, Lei; Li, Yin; Guo, Xinfeng; May, Brian H.; Xue, Charlie C. L.; Yang, Lihong; Liu, Xusheng

2014-01-01

Objectives. To apply modern text-mining methods to identify candidate herbs and formulae for the treatment of diabetic nephropathy. Methods. The method we developed includes three steps: (1) identification of candidate ancient terms; (2) systemic search and assessment of medical records written in classical Chinese; (3) preliminary evaluation of the effect and safety of candidates. Results. Ancient terms Xia Xiao, Shen Xiao, and Xiao Shen were determined as the most likely to correspond with diabetic nephropathy and used in text mining. A total of 80 Chinese formulae for treating conditions congruent with diabetic nephropathy recorded in medical books from Tang Dynasty to Qing Dynasty were collected. Sao si tang (also called Reeling Silk Decoction) was chosen to show the process of preliminary evaluation of the candidates. It had promising potential for development as new agent for the treatment of diabetic nephropathy. However, further investigations about the safety to patients with renal insufficiency are still needed. Conclusions. The methods developed in this study offer a targeted approach to identifying traditional herbs and/or formulae as candidates for further investigation in the search for new drugs for modern disease. However, more effort is still required to improve our techniques, especially with regard to compound formulae. PMID:24744808

43 CFR 3420.1-4 - General requirements for land use planning.

Code of Federal Regulations, 2011 CFR

2011-10-01

... mining by other than underground mining techniques. (ii) For the purposes of this paragraph, any surface... techniques shall be deemed to have expressed a preference in favor of mining. Where a significant number of... underground mining techniques, that area shall be considered acceptable for further consideration only for...

43 CFR 3420.1-4 - General requirements for land use planning.

Code of Federal Regulations, 2013 CFR

2013-10-01

... mining by other than underground mining techniques. (ii) For the purposes of this paragraph, any surface... techniques shall be deemed to have expressed a preference in favor of mining. Where a significant number of... underground mining techniques, that area shall be considered acceptable for further consideration only for...

43 CFR 3420.1-4 - General requirements for land use planning.

Code of Federal Regulations, 2014 CFR

2014-10-01

... mining by other than underground mining techniques. (ii) For the purposes of this paragraph, any surface... techniques shall be deemed to have expressed a preference in favor of mining. Where a significant number of... underground mining techniques, that area shall be considered acceptable for further consideration only for...

43 CFR 3420.1-4 - General requirements for land use planning.

Code of Federal Regulations, 2012 CFR

2012-10-01

... mining by other than underground mining techniques. (ii) For the purposes of this paragraph, any surface... techniques shall be deemed to have expressed a preference in favor of mining. Where a significant number of... underground mining techniques, that area shall be considered acceptable for further consideration only for...

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

tmBioC: improving interoperability of text-mining tools with BioC.

PubMed

Khare, Ritu; Wei, Chih-Hsuan; Mao, Yuqing; Leaman, Robert; Lu, Zhiyong

2014-01-01

The lack of interoperability among biomedical text-mining tools is a major bottleneck in creating more complex applications. Despite the availability of numerous methods and techniques for various text-mining tasks, combining different tools requires substantial efforts and time owing to heterogeneity and variety in data formats. In response, BioC is a recent proposal that offers a minimalistic approach to tool interoperability by stipulating minimal changes to existing tools and applications. BioC is a family of XML formats that define how to present text documents and annotations, and also provides easy-to-use functions to read/write documents in the BioC format. In this study, we introduce our text-mining toolkit, which is designed to perform several challenging and significant tasks in the biomedical domain, and repackage the toolkit into BioC to enhance its interoperability. Our toolkit consists of six state-of-the-art tools for named-entity recognition, normalization and annotation (PubTator) of genes (GenNorm), diseases (DNorm), mutations (tmVar), species (SR4GN) and chemicals (tmChem). Although developed within the same group, each tool is designed to process input articles and output annotations in a different format. We modify these tools and enable them to read/write data in the proposed BioC format. We find that, using the BioC family of formats and functions, only minimal changes were required to build the newer versions of the tools. The resulting BioC wrapped toolkit, which we have named tmBioC, consists of our tools in BioC, an annotated full-text corpus in BioC, and a format detection and conversion tool. Furthermore, through participation in the 2013 BioCreative IV Interoperability Track, we empirically demonstrate that the tools in tmBioC can be more efficiently integrated with each other as well as with external tools: Our experimental results show that using BioC reduces >60% in lines of code for text-mining tool integration. The tmBioC toolkit is publicly available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/. Database URL: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

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/

A best-fit model for concept vectors in biomedical research grants.

PubMed

Johnson, Calvin; Lau, William; Bhandari, Archna; Hays, Timothy

2008-11-06

The Research, Condition, and Disease Categorization (RCDC) project was created to standardize budget reporting by research topic. Text mining techniques have been implemented to classify NIH grant applications into proper research and disease categories. A best-fit model is shown to achieve classification performance rivaling that of concept vectors produced by human experts.

Enhancing a Core Journal Collection for Digital Libraries

ERIC Educational Resources Information Center

Kovacevic, Ana; Devedzic, Vladan; Pocajt, Viktor

2010-01-01

Purpose: This paper aims to address the problem of enhancing the selection of titles offered by a digital library, by analysing the differences in these titles when they are cited by local authors in their publications and when they are listed in the digital library offer. Design/methodology/approach: Text mining techniques were used to identify…

Sentiment analysis of feature ranking methods for classification accuracy

NASA Astrophysics Data System (ADS)

Joseph, Shashank; Mugauri, Calvin; Sumathy, S.

2017-11-01

Text pre-processing and feature selection are important and critical steps in text mining. Text pre-processing of large volumes of datasets is a difficult task as unstructured raw data is converted into structured format. Traditional methods of processing and weighing took much time and were less accurate. To overcome this challenge, feature ranking techniques have been devised. A feature set from text preprocessing is fed as input for feature selection. Feature selection helps improve text classification accuracy. Of the three feature selection categories available, the filter category will be the focus. Five feature ranking methods namely: document frequency, standard deviation information gain, CHI-SQUARE, and weighted-log likelihood -ratio is analyzed.

Mining Land Subsidence Monitoring Using SENTINEL-1 SAR Data

NASA Astrophysics Data System (ADS)

Yuan, W.; Wang, Q.; Fan, J.; Li, H.

2017-09-01

In this paper, DInSAR technique was used to monitor land subsidence in mining area. The study area was selected in the coal mine area located in Yuanbaoshan District, Chifeng City, and Sentinel-1 data were used to carry out DInSAR techniqu. We analyzed the interferometric results by Sentinel-1 data from December 2015 to May 2016. Through the comparison of the results of DInSAR technique and the location of the mine on the optical images, it is shown that DInSAR technique can be used to effectively monitor the land subsidence caused by underground mining, and it is an effective tool for law enforcement of over-mining.

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].

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.

A New Data Representation Based on Training Data Characteristics to Extract Drug Name Entity in Medical Text

PubMed Central

Basaruddin, T.

2016-01-01

One essential task in information extraction from the medical corpus is drug name recognition. Compared with text sources come from other domains, the medical text mining poses more challenges, for example, more unstructured text, the fast growing of new terms addition, a wide range of name variation for the same drug, the lack of labeled dataset sources and external knowledge, and the multiple token representations for a single drug name. Although many approaches have been proposed to overwhelm the task, some problems remained with poor F-score performance (less than 0.75). This paper presents a new treatment in data representation techniques to overcome some of those challenges. We propose three data representation techniques based on the characteristics of word distribution and word similarities as a result of word embedding training. The first technique is evaluated with the standard NN model, that is, MLP. The second technique involves two deep network classifiers, that is, DBN and SAE. The third technique represents the sentence as a sequence that is evaluated with a recurrent NN model, that is, LSTM. In extracting the drug name entities, the third technique gives the best F-score performance compared to the state of the art, with its average F-score being 0.8645. PMID:27843447

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.

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.

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

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.

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.

[Exploring pharmacological principle of Artemisia carvifolia with textmining technology].

PubMed

Zhao, Yu-Ping; Wang, Hui; Yang, Guang; Qiu, Zhi-Dong; Qu, Xiao-Bo; Zhang, Xiao-Bo

2016-08-01

To explore the pharmacological principle of Artemisia carvifolia,the text mining technique was used. All the references of A. carvifolia were collected from PubMed database, and then the rules of the main ingredient,relative diseases, organs, tissues, proteins and metabolites were analyzed. Finally, a network was set up. Then it was found that the main ingredients included sesquiterpenoids,flavonoids,and volatileoils.The diseases such as malaria, cerebral malaria, falciparum malaria, visceral leishmaniasis and systemic lupus erythematosus were often treated with A. carvifolia. In association in organ were the liver, skin, trachea,lungs,and spleen.Correlations with tissues were mainly including macrophages, T lymphocytes, blood vessels, epithelial cells.The protein was correlation with it involved CYP450, PI3K, TNF-α, AASDPPT, DNA polymerase and so on. Comprehensive and systematic treatment principle of A. carvifolia was obtained by text mining, which was helpful in clinical application. Copyright© by the Chinese Pharmaceutical Association.

Spectral signature verification using statistical analysis and text mining

NASA Astrophysics Data System (ADS)

DeCoster, Mallory E.; Firpi, Alexe H.; Jacobs, Samantha K.; Cone, Shelli R.; Tzeng, Nigel H.; Rodriguez, Benjamin M.

2016-05-01

In the spectral science community, numerous spectral signatures are stored in databases representative of many sample materials collected from a variety of spectrometers and spectroscopists. Due to the variety and variability of the spectra that comprise many spectral databases, it is necessary to establish a metric for validating the quality of spectral signatures. This has been an area of great discussion and debate in the spectral science community. This paper discusses a method that independently validates two different aspects of a spectral signature to arrive at a final qualitative assessment; the textual meta-data and numerical spectral data. Results associated with the spectral data stored in the Signature Database1 (SigDB) are proposed. The numerical data comprising a sample material's spectrum is validated based on statistical properties derived from an ideal population set. The quality of the test spectrum is ranked based on a spectral angle mapper (SAM) comparison to the mean spectrum derived from the population set. Additionally, the contextual data of a test spectrum is qualitatively analyzed using lexical analysis text mining. This technique analyzes to understand the syntax of the meta-data to provide local learning patterns and trends within the spectral data, indicative of the test spectrum's quality. Text mining applications have successfully been implemented for security2 (text encryption/decryption), biomedical3 , and marketing4 applications. The text mining lexical analysis algorithm is trained on the meta-data patterns of a subset of high and low quality spectra, in order to have a model to apply to the entire SigDB data set. The statistical and textual methods combine to assess the quality of a test spectrum existing in a database without the need of an expert user. This method has been compared to other validation methods accepted by the spectral science community, and has provided promising results when a baseline spectral signature is present for comparison. The spectral validation method proposed is described from a practical application and analytical perspective.

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

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.

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 ...

Analog Tools in Digital History Classrooms: An Activity-Theory Case Study of Learning Opportunities in Digital Humanities

ERIC Educational Resources Information Center

Craig, Kalani

2017-01-01

Digital humanities is often presented as classroom savior, a narrative that competes against the idea that technology virtually guarantees student distraction. However, these arguments are often based on advocacy and anecdote, so we lack systematic research that explores the effect of digital-humanities tools and techniques such as text mining,…

Text Mining in Cancer Gene and Pathway Prioritization

PubMed Central

Luo, Yuan; Riedlinger, Gregory; Szolovits, Peter

2014-01-01

Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on the analysis of their text mining components. We explain the relatively slow progress of text mining in gene prioritization, identify several challenges to current text mining methods, and highlight a few directions where more effective text mining algorithms may improve the overall prioritization task and where prioritizing the pathways may be more desirable than prioritizing only genes. PMID:25392685

Text mining in cancer gene and pathway prioritization.

PubMed

Luo, Yuan; Riedlinger, Gregory; Szolovits, Peter

2014-01-01

Prioritization of cancer implicated genes has received growing attention as an effective way to reduce wet lab cost by computational analysis that ranks candidate genes according to the likelihood that experimental verifications will succeed. A multitude of gene prioritization tools have been developed, each integrating different data sources covering gene sequences, differential expressions, function annotations, gene regulations, protein domains, protein interactions, and pathways. This review places existing gene prioritization tools against the backdrop of an integrative Omic hierarchy view toward cancer and focuses on the analysis of their text mining components. We explain the relatively slow progress of text mining in gene prioritization, identify several challenges to current text mining methods, and highlight a few directions where more effective text mining algorithms may improve the overall prioritization task and where prioritizing the pathways may be more desirable than prioritizing only genes.

Citation Mining: Integrating Text Mining and Bibliometrics for Research User Profiling.

ERIC Educational Resources Information Center

Kostoff, Ronald N.; del Rio, J. Antonio; Humenik, James A.; Garcia, Esther Ofilia; Ramirez, Ana Maria

2001-01-01

Discusses the importance of identifying the users and impact of research, and describes an approach for identifying the pathways through which research can impact other research, technology development, and applications. Describes a study that used citation mining, an integration of citation bibliometrics and text mining, on articles from the…

Development of Workshops on Biodiversity and Evaluation of the Educational Effect by Text Mining Analysis

NASA Astrophysics Data System (ADS)

Baba, R.; Iijima, A.

2014-12-01

Conservation of biodiversity is one of the key issues in the environmental studies. As means to solve this issue, education is becoming increasingly important. In the previous work, we have developed a course of workshops on the conservation of biodiversity. To disseminate the course as a tool for environmental education, determination of the educational effect is essential. A text mining enables analyses of frequency and co-occurrence of words in the freely described texts. This study is intended to evaluate the effect of workshop by using text mining technique. We hosted the originally developed workshop on the conservation of biodiversity for 22 college students. The aim of the workshop was to inform the definition of biodiversity. Generally, biodiversity refers to the diversity of ecosystem, diversity between species, and diversity within species. To facilitate discussion, supplementary materials were used. For instance, field guides of wildlife species were used to discuss about the diversity of ecosystem. Moreover, a hierarchical framework in an ecological pyramid was shown for understanding the role of diversity between species. Besides, we offered a document material on the historical affair of Potato Famine in Ireland to discuss about the diversity within species from the genetic viewpoint. Before and after the workshop, we asked students for free description on the definition of biodiversity, and analyzed by using Tiny Text Miner. This technique enables Japanese language morphological analysis. Frequently-used words were sorted into some categories. Moreover, a principle component analysis was carried out. After the workshop, frequency of the words tagged to diversity between species and diversity within species has significantly increased. From a principle component analysis, the 1st component consists of the words such as producer, consumer, decomposer, and food chain. This indicates that the students have comprehended the close relationship between biodiversity and ecological pyramid. The 2nd component consists of the words such as gene, species, and ecosystem, suggesting that the students have correctly understood the 3 aspects of biodiversity. Consequently, this workshop shows an effect on acquirement of basic knowledge about the biodiversity.

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

Life priorities in the HIV-positive Asians: a text-mining analysis in young vs. old generation.

PubMed

Chen, Wei-Ti; Barbour, Russell

2017-04-01

HIV/AIDS is one of the most urgent and challenging public health issues, especially since it is now considered a chronic disease. In this project, we used text mining techniques to extract meaningful words and word patterns from 45 transcribed in-depth interviews of people living with HIV/AIDS (PLWHA) conducted in Taipei, Beijing, Shanghai, and San Francisco from 2006 to 2013. Text mining analysis can predict whether an emerging field will become a long-lasting source of academic interest or whether it is simply a passing source of interest that will soon disappear. The data were analyzed by age group (45 and older vs. 44 and younger). The highest ranking fragments in the order of frequency were: "care", "daughter", "disease", "family", "HIV", "hospital", "husband", "medicines", "money", "people", "son", "tell/disclosure", "thought", "want", and "years". Participants in the 44-year-old and younger group were focused mainly on disease disclosure, their families, and their financial condition. In older PLWHA, social supports were one of the main concerns. In this study, we learned that different age groups perceive the disease differently. Therefore, when designing intervention, researchers should consider to tailor an intervention to a specific population and to help PLWHA achieve a better quality of life. Promoting self-management can be an effective strategy for every encounter with HIV-positive individuals.

Block-suffix shifting: fast, simultaneous medical concept set identification in large medical record corpora.

PubMed

Liu, Ying; Lita, Lucian Vlad; Niculescu, Radu Stefan; Mitra, Prasenjit; Giles, C Lee

2008-11-06

Owing to new advances in computer hardware, large text databases have become more prevalent than ever.Automatically mining information from these databases proves to be a challenge due to slow pattern/string matching techniques. In this paper we present a new, fast multi-string pattern matching method based on the well known Aho-Chorasick algorithm. Advantages of our algorithm include:the ability to exploit the natural structure of text, the ability to perform significant character shifting, avoiding backtracking jumps that are not useful, efficiency in terms of matching time and avoiding the typical "sub-string" false positive errors.Our algorithm is applicable to many fields with free text, such as the health care domain and the scientific document field. In this paper, we apply the BSS algorithm to health care data and mine hundreds of thousands of medical concepts from a large Electronic Medical Record (EMR) corpora simultaneously and efficiently. Experimental results show the superiority of our algorithm when compared with the top of the line multi-string matching algorithms.

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].

Science and Technology Text Mining: Near-Earth Space

DTIC Science & Technology

2003-07-21

TRANSFER; 177SATELLITE IMAGES; 175 SPATIAL RESOLUTION ; 174 SEA ICE; 166 SYSTEM GPS; 166 TOPEX POSEIDON; 165 SATELLITE MEASUREMENTS; 163 RADIATION BUDGET...1073 ICE; 1065 SATELLITES; 1062 PAPER; 1009 EARTH; 1008 RESOLUTION ; 1000 MODELS; 962 RADIATION; 943 DERIVED; 938 OCEAN; 928 CURRENT; 925 SPATIAL ; 899...PARAMETERS; 729 TECHNIQUE; 714 OPTICAL; 714 SPACECRAFT; 711 DEGREE; 702 TRANSMISSION; 696 LARGE; 693 TEST; 686 NUMBER; 671 EFFECTS ; 662 SPECTRAL ; 661

Seminal nanotechnology literature: a review.

PubMed

Kostoff, Ronald N; Koytcheff, Raymond G; Lau, Clifford G Y

2009-11-01

This paper uses complementary text mining techniques to identify and retrieve the high impact (seminal) nanotechnology literature over a span of time. Following a brief scientometric analysis of the seminal articles retrieved, these seminal articles are then used as a basis for a comprehensive literature survey of nanoscience and nanotechnology. The paper ends with a global analysis of the relation of seminal nanotechnology document production to total nanotechnology document production.

Generation of Acid Mine Lakes Associated with Abandoned Coal Mines in Northwest Turkey.

PubMed

Sanliyuksel Yucel, Deniz; Balci, Nurgul; Baba, Alper

2016-05-01

A total of five acid mine lakes (AMLs) located in northwest Turkey were investigated using combined isotope, molecular, and geochemical techniques to identify geochemical processes controlling and promoting acid formation. All of the investigated lakes showed typical characteristics of an AML with low pH (2.59-3.79) and high electrical conductivity values (1040-6430 μS/cm), in addition to high sulfate (594-5370 mg/l) and metal (aluminum [Al], iron [Fe], manganese [Mn], nickel [Ni], and zinc [Zn]) concentrations. Geochemical and isotope results showed that the acid-generation mechanism and source of sulfate in the lakes can change and depends on the age of the lakes. In the relatively older lakes (AMLs 1 through 3), biogeochemical Fe cycles seem to be the dominant process controlling metal concentration and pH of the water unlike in the younger lakes (AMLs 4 and 5). Bacterial species determined in an older lake (AML 2) indicate that biological oxidation and reduction of Fe and S are the dominant processes in the lakes. Furthermore, O and S isotopes of sulfate indicate that sulfate in the older mine lakes may be a product of much more complex oxidation/dissolution reactions. However, the major source of sulfate in the younger mine lakes is in situ pyrite oxidation catalyzed by Fe(III) produced by way of oxidation of Fe(II). Consistent with this, insignificant fractionation between δ(34) [Formula: see text] and δ(34) [Formula: see text] values indicated that the oxidation of pyrite, along with dissolution and precipitation reactions of Fe(III) minerals, is the main reason for acid formation in the region. Overall, the results showed that acid generation during early stage formation of an AML associated with pyrite-rich mine waste is primarily controlled by the oxidation of pyrite with Fe cycles becoming the dominant processes regulating pH and metal cycles in the later stages of mine lake development.

What Online Communities Can Tell Us About Electronic Cigarettes and Hookah Use: A Study Using Text Mining and Visualization Techniques.

PubMed

Chen, Annie T; Zhu, Shu-Hong; Conway, Mike

2015-09-29

The rise in popularity of electronic cigarettes (e-cigarettes) and hookah over recent years has been accompanied by some confusion and uncertainty regarding the development of an appropriate regulatory response towards these emerging products. Mining online discussion content can lead to insights into people's experiences, which can in turn further our knowledge of how to address potential health implications. In this work, we take a novel approach to understanding the use and appeal of these emerging products by applying text mining techniques to compare consumer experiences across discussion forums. This study examined content from the websites Vapor Talk, Hookah Forum, and Reddit to understand people's experiences with different tobacco products. Our investigation involves three parts. First, we identified contextual factors that inform our understanding of tobacco use behaviors, such as setting, time, social relationships, and sensory experience, and compared the forums to identify the ones where content on these factors is most common. Second, we compared how the tobacco use experience differs with combustible cigarettes and e-cigarettes. Third, we investigated differences between e-cigarette and hookah use. In the first part of our study, we employed a lexicon-based extraction approach to estimate prevalence of contextual factors, and then we generated a heat map based on these estimates to compare the forums. In the second and third parts of the study, we employed a text mining technique called topic modeling to identify important topics and then developed a visualization, Topic Bars, to compare topic coverage across forums. In the first part of the study, we identified two forums, Vapor Talk Health & Safety and the Stopsmoking subreddit, where discussion concerning contextual factors was particularly common. The second part showed that the discussion in Vapor Talk Health & Safety focused on symptoms and comparisons of combustible cigarettes and e-cigarettes, and the Stopsmoking subreddit focused on psychological aspects of quitting. Last, we examined the discussion content on Vapor Talk and Hookah Forum. Prominent topics included equipment, technique, experiential elements of use, and the buying and selling of equipment. This study has three main contributions. Discussion forums differ in the extent to which their content may help us understand behaviors with potential health implications. Identifying dimensions of interest and using a heat map visualization to compare across forums can be helpful for identifying forums with the greatest density of health information. Additionally, our work has shown that the quitting experience can potentially be very different depending on whether or not e-cigarettes are used. Finally, e-cigarette and hookah forums are similar in that members represent a "hobbyist culture" that actively engages in information exchange. These differences have important implications for both tobacco regulation and smoking cessation intervention design.

Information Extraction for Clinical Data Mining: A Mammography Case Study

PubMed Central

Nassif, Houssam; Woods, Ryan; Burnside, Elizabeth; Ayvaci, Mehmet; Shavlik, Jude; Page, David

2013-01-01

Breast cancer is the leading cause of cancer mortality in women between the ages of 15 and 54. During mammography screening, radiologists use a strict lexicon (BI-RADS) to describe and report their findings. Mammography records are then stored in a well-defined database format (NMD). Lately, researchers have applied data mining and machine learning techniques to these databases. They successfully built breast cancer classifiers that can help in early detection of malignancy. However, the validity of these models depends on the quality of the underlying databases. Unfortunately, most databases suffer from inconsistencies, missing data, inter-observer variability and inappropriate term usage. In addition, many databases are not compliant with the NMD format and/or solely consist of text reports. BI-RADS feature extraction from free text and consistency checks between recorded predictive variables and text reports are crucial to addressing this problem. We describe a general scheme for concept information retrieval from free text given a lexicon, and present a BI-RADS features extraction algorithm for clinical data mining. It consists of a syntax analyzer, a concept finder and a negation detector. The syntax analyzer preprocesses the input into individual sentences. The concept finder uses a semantic grammar based on the BI-RADS lexicon and the experts’ input. It parses sentences detecting BI-RADS concepts. Once a concept is located, a lexical scanner checks for negation. Our method can handle multiple latent concepts within the text, filtering out ultrasound concepts. On our dataset, our algorithm achieves 97.7% precision, 95.5% recall and an F1-score of 0.97. It outperforms manual feature extraction at the 5% statistical significance level. PMID:23765123

Information Extraction for Clinical Data Mining: A Mammography Case Study.

PubMed

Nassif, Houssam; Woods, Ryan; Burnside, Elizabeth; Ayvaci, Mehmet; Shavlik, Jude; Page, David

2009-01-01

Breast cancer is the leading cause of cancer mortality in women between the ages of 15 and 54. During mammography screening, radiologists use a strict lexicon (BI-RADS) to describe and report their findings. Mammography records are then stored in a well-defined database format (NMD). Lately, researchers have applied data mining and machine learning techniques to these databases. They successfully built breast cancer classifiers that can help in early detection of malignancy. However, the validity of these models depends on the quality of the underlying databases. Unfortunately, most databases suffer from inconsistencies, missing data, inter-observer variability and inappropriate term usage. In addition, many databases are not compliant with the NMD format and/or solely consist of text reports. BI-RADS feature extraction from free text and consistency checks between recorded predictive variables and text reports are crucial to addressing this problem. We describe a general scheme for concept information retrieval from free text given a lexicon, and present a BI-RADS features extraction algorithm for clinical data mining. It consists of a syntax analyzer, a concept finder and a negation detector. The syntax analyzer preprocesses the input into individual sentences. The concept finder uses a semantic grammar based on the BI-RADS lexicon and the experts' input. It parses sentences detecting BI-RADS concepts. Once a concept is located, a lexical scanner checks for negation. Our method can handle multiple latent concepts within the text, filtering out ultrasound concepts. On our dataset, our algorithm achieves 97.7% precision, 95.5% recall and an F 1 -score of 0.97. It outperforms manual feature extraction at the 5% statistical significance level.

A primer to frequent itemset mining for bioinformatics

PubMed Central

Naulaerts, Stefan; Meysman, Pieter; Bittremieux, Wout; Vu, Trung Nghia; Vanden Berghe, Wim; Goethals, Bart

2015-01-01

Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences. PMID:24162173

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.

Contextual Text Mining

ERIC Educational Resources Information Center

Mei, Qiaozhu

2009-01-01

With the dramatic growth of text information, there is an increasing need for powerful text mining systems that can automatically discover useful knowledge from text. Text is generally associated with all kinds of contextual information. Those contexts can be explicit, such as the time and the location where a blog article is written, and the…

Text mining applied to electronic cardiovascular procedure reports to identify patients with trileaflet aortic stenosis and coronary artery disease.

PubMed

Small, Aeron M; Kiss, Daniel H; Zlatsin, Yevgeny; Birtwell, David L; Williams, Heather; Guerraty, Marie A; Han, Yuchi; Anwaruddin, Saif; Holmes, John H; Chirinos, Julio A; Wilensky, Robert L; Giri, Jay; Rader, Daniel J

2017-08-01

Interrogation of the electronic health record (EHR) using billing codes as a surrogate for diagnoses of interest has been widely used for clinical research. However, the accuracy of this methodology is variable, as it reflects billing codes rather than severity of disease, and depends on the disease and the accuracy of the coding practitioner. Systematic application of text mining to the EHR has had variable success for the detection of cardiovascular phenotypes. We hypothesize that the application of text mining algorithms to cardiovascular procedure reports may be a superior method to identify patients with cardiovascular conditions of interest. We adapted the Oracle product Endeca, which utilizes text mining to identify terms of interest from a NoSQL-like database, for purposes of searching cardiovascular procedure reports and termed the tool "PennSeek". We imported 282,569 echocardiography reports representing 81,164 individuals and 27,205 cardiac catheterization reports representing 14,567 individuals from non-searchable databases into PennSeek. We then applied clinical criteria to these reports in PennSeek to identify patients with trileaflet aortic stenosis (TAS) and coronary artery disease (CAD). Accuracy of patient identification by text mining through PennSeek was compared with ICD-9 billing codes. Text mining identified 7115 patients with TAS and 9247 patients with CAD. ICD-9 codes identified 8272 patients with TAS and 6913 patients with CAD. 4346 patients with AS and 6024 patients with CAD were identified by both approaches. A randomly selected sample of 200-250 patients uniquely identified by text mining was compared with 200-250 patients uniquely identified by billing codes for both diseases. We demonstrate that text mining was superior, with a positive predictive value (PPV) of 0.95 compared to 0.53 by ICD-9 for TAS, and a PPV of 0.97 compared to 0.86 for CAD. These results highlight the superiority of text mining algorithms applied to electronic cardiovascular procedure reports in the identification of phenotypes of interest for cardiovascular research. Copyright © 2017. Published by Elsevier Inc.

APPLYING DATA MINING APPROACHES TO FURTHER ...

EPA Pesticide Factsheets

This dataset will be used to illustrate various data mining techniques to biologically profile the chemical space. This dataset will be used to illustrate various data mining techniques to biologically profile the chemical space.

A systematic mapping study of process mining

NASA Astrophysics Data System (ADS)

Maita, Ana Rocío Cárdenas; Martins, Lucas Corrêa; López Paz, Carlos Ramón; Rafferty, Laura; Hung, Patrick C. K.; Peres, Sarajane Marques; Fantinato, Marcelo

2018-05-01

This study systematically assesses the process mining scenario from 2005 to 2014. The analysis of 705 papers evidenced 'discovery' (71%) as the main type of process mining addressed and 'categorical prediction' (25%) as the main mining task solved. The most applied traditional technique is the 'graph structure-based' ones (38%). Specifically concerning computational intelligence and machine learning techniques, we concluded that little relevance has been given to them. The most applied are 'evolutionary computation' (9%) and 'decision tree' (6%), respectively. Process mining challenges, such as balancing among robustness, simplicity, accuracy and generalization, could benefit from a larger use of such techniques.

Privacy Preserving Sequential Pattern Mining in Data Stream

NASA Astrophysics Data System (ADS)

Huang, Qin-Hua

The privacy preserving data mining technique researches have gained much attention in recent years. For data stream systems, wireless networks and mobile devices, the related stream data mining techniques research is still in its' early stage. In this paper, an data mining algorithm dealing with privacy preserving problem in data stream is presented.

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/.

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.

Comparative analysis of data mining techniques for business data

NASA Astrophysics Data System (ADS)

Jamil, Jastini Mohd; Shaharanee, Izwan Nizal Mohd

2014-12-01

Data mining is the process of employing one or more computer learning techniques to automatically analyze and extract knowledge from data contained within a database. Companies are using this tool to further understand their customers, to design targeted sales and marketing campaigns, to predict what product customers will buy and the frequency of purchase, and to spot trends in customer preferences that can lead to new product development. In this paper, we conduct a systematic approach to explore several of data mining techniques in business application. The experimental result reveals that all data mining techniques accomplish their goals perfectly, but each of the technique has its own characteristics and specification that demonstrate their accuracy, proficiency and preference.

Construction accident narrative classification: An evaluation of text mining techniques.

PubMed

Goh, Yang Miang; Ubeynarayana, C U

2017-11-01

Learning from past accidents is fundamental to accident prevention. Thus, accident and near miss reporting are encouraged by organizations and regulators. However, for organizations managing large safety databases, the time taken to accurately classify accident and near miss narratives will be very significant. This study aims to evaluate the utility of various text mining classification techniques in classifying 1000 publicly available construction accident narratives obtained from the US OSHA website. The study evaluated six machine learning algorithms, including support vector machine (SVM), linear regression (LR), random forest (RF), k-nearest neighbor (KNN), decision tree (DT) and Naive Bayes (NB), and found that SVM produced the best performance in classifying the test set of 251 cases. Further experimentation with tokenization of the processed text and non-linear SVM were also conducted. In addition, a grid search was conducted on the hyperparameters of the SVM models. It was found that the best performing classifiers were linear SVM with unigram tokenization and radial basis function (RBF) SVM with uni-gram tokenization. In view of its relative simplicity, the linear SVM is recommended. Across the 11 labels of accident causes or types, the precision of the linear SVM ranged from 0.5 to 1, recall ranged from 0.36 to 0.9 and F1 score was between 0.45 and 0.92. The reasons for misclassification were discussed and suggestions on ways to improve the performance were provided. Copyright © 2017 Elsevier Ltd. All rights reserved.

Detection of interaction articles and experimental methods in biomedical literature.

PubMed

Schneider, Gerold; Clematide, Simon; Rinaldi, Fabio

2011-10-03

This article describes the approaches taken by the OntoGene group at the University of Zurich in dealing with two tasks of the BioCreative III competition: classification of articles which contain curatable protein-protein interactions (PPI-ACT) and extraction of experimental methods (PPI-IMT). Two main achievements are described in this paper: (a) a system for document classification which crucially relies on the results of an advanced pipeline of natural language processing tools; (b) a system which is capable of detecting all experimental methods mentioned in scientific literature, and listing them with a competitive ranking (AUC iP/R > 0.5). The results of the BioCreative III shared evaluation clearly demonstrate that significant progress has been achieved in the domain of biomedical text mining in the past few years. Our own contribution, together with the results of other participants, provides evidence that natural language processing techniques have become by now an integral part of advanced text mining approaches.

Runtime support for parallelizing data mining algorithms

NASA Astrophysics Data System (ADS)

Jin, Ruoming; Agrawal, Gagan

2002-03-01

With recent technological advances, shared memory parallel machines have become more scalable, and offer large main memories and high bus bandwidths. They are emerging as good platforms for data warehousing and data mining. In this paper, we focus on shared memory parallelization of data mining algorithms. We have developed a series of techniques for parallelization of data mining algorithms, including full replication, full locking, fixed locking, optimized full locking, and cache-sensitive locking. Unlike previous work on shared memory parallelization of specific data mining algorithms, all of our techniques apply to a large number of common data mining algorithms. In addition, we propose a reduction-object based interface for specifying a data mining algorithm. We show how our runtime system can apply any of the technique we have developed starting from a common specification of the algorithm.

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

OntoGene web services for biomedical text mining.

PubMed

Rinaldi, Fabio; Clematide, Simon; Marques, Hernani; Ellendorff, Tilia; Romacker, Martin; Rodriguez-Esteban, Raul

2014-01-01

Text mining services are rapidly becoming a crucial component of various knowledge management pipelines, for example in the process of database curation, or for exploration and enrichment of biomedical data within the pharmaceutical industry. Traditional architectures, based on monolithic applications, do not offer sufficient flexibility for a wide range of use case scenarios, and therefore open architectures, as provided by web services, are attracting increased interest. We present an approach towards providing advanced text mining capabilities through web services, using a recently proposed standard for textual data interchange (BioC). The web services leverage a state-of-the-art platform for text mining (OntoGene) which has been tested in several community-organized evaluation challenges,with top ranked results in several of them.

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.

OntoGene web services for biomedical text mining

PubMed Central

2014-01-01

Text mining services are rapidly becoming a crucial component of various knowledge management pipelines, for example in the process of database curation, or for exploration and enrichment of biomedical data within the pharmaceutical industry. Traditional architectures, based on monolithic applications, do not offer sufficient flexibility for a wide range of use case scenarios, and therefore open architectures, as provided by web services, are attracting increased interest. We present an approach towards providing advanced text mining capabilities through web services, using a recently proposed standard for textual data interchange (BioC). The web services leverage a state-of-the-art platform for text mining (OntoGene) which has been tested in several community-organized evaluation challenges, with top ranked results in several of them. PMID:25472638

Using Workflows to Explore and Optimise Named Entity Recognition for Chemistry

PubMed Central

Kolluru, BalaKrishna; Hawizy, Lezan; Murray-Rust, Peter; Tsujii, Junichi; Ananiadou, Sophia

2011-01-01

Chemistry text mining tools should be interoperable and adaptable regardless of system-level implementation, installation or even programming issues. We aim to abstract the functionality of these tools from the underlying implementation via reconfigurable workflows for automatically identifying chemical names. To achieve this, we refactored an established named entity recogniser (in the chemistry domain), OSCAR and studied the impact of each component on the net performance. We developed two reconfigurable workflows from OSCAR using an interoperable text mining framework, U-Compare. These workflows can be altered using the drag-&-drop mechanism of the graphical user interface of U-Compare. These workflows also provide a platform to study the relationship between text mining components such as tokenisation and named entity recognition (using maximum entropy Markov model (MEMM) and pattern recognition based classifiers). Results indicate that, for chemistry in particular, eliminating noise generated by tokenisation techniques lead to a slightly better performance than others, in terms of named entity recognition (NER) accuracy. Poor tokenisation translates into poorer input to the classifier components which in turn leads to an increase in Type I or Type II errors, thus, lowering the overall performance. On the Sciborg corpus, the workflow based system, which uses a new tokeniser whilst retaining the same MEMM component, increases the F-score from 82.35% to 84.44%. On the PubMed corpus, it recorded an F-score of 84.84% as against 84.23% by OSCAR. PMID:21633495

Using workflows to explore and optimise named entity recognition for chemistry.

PubMed

Kolluru, Balakrishna; Hawizy, Lezan; Murray-Rust, Peter; Tsujii, Junichi; Ananiadou, Sophia

2011-01-01

Chemistry text mining tools should be interoperable and adaptable regardless of system-level implementation, installation or even programming issues. We aim to abstract the functionality of these tools from the underlying implementation via reconfigurable workflows for automatically identifying chemical names. To achieve this, we refactored an established named entity recogniser (in the chemistry domain), OSCAR and studied the impact of each component on the net performance. We developed two reconfigurable workflows from OSCAR using an interoperable text mining framework, U-Compare. These workflows can be altered using the drag-&-drop mechanism of the graphical user interface of U-Compare. These workflows also provide a platform to study the relationship between text mining components such as tokenisation and named entity recognition (using maximum entropy Markov model (MEMM) and pattern recognition based classifiers). Results indicate that, for chemistry in particular, eliminating noise generated by tokenisation techniques lead to a slightly better performance than others, in terms of named entity recognition (NER) accuracy. Poor tokenisation translates into poorer input to the classifier components which in turn leads to an increase in Type I or Type II errors, thus, lowering the overall performance. On the Sciborg corpus, the workflow based system, which uses a new tokeniser whilst retaining the same MEMM component, increases the F-score from 82.35% to 84.44%. On the PubMed corpus, it recorded an F-score of 84.84% as against 84.23% by OSCAR.

Using text mining techniques to extract phenotypic information from the PhenoCHF corpus

PubMed Central

2015-01-01

Background Phenotypic information locked away in unstructured narrative text presents significant barriers to information accessibility, both for clinical practitioners and for computerised applications used for clinical research purposes. Text mining (TM) techniques have previously been applied successfully to extract different types of information from text in the biomedical domain. They have the potential to be extended to allow the extraction of information relating to phenotypes from free text. Methods To stimulate the development of TM systems that are able to extract phenotypic information from text, we have created a new corpus (PhenoCHF) that is annotated by domain experts with several types of phenotypic information relating to congestive heart failure. To ensure that systems developed using the corpus are robust to multiple text types, it integrates text from heterogeneous sources, i.e., electronic health records (EHRs) and scientific articles from the literature. We have developed several different phenotype extraction methods to demonstrate the utility of the corpus, and tested these methods on a further corpus, i.e., ShARe/CLEF 2013. Results Evaluation of our automated methods showed that PhenoCHF can facilitate the training of reliable phenotype extraction systems, which are robust to variations in text type. These results have been reinforced by evaluating our trained systems on the ShARe/CLEF corpus, which contains clinical records of various types. Like other studies within the biomedical domain, we found that solutions based on conditional random fields produced the best results, when coupled with a rich feature set. Conclusions PhenoCHF is the first annotated corpus aimed at encoding detailed phenotypic information. The unique heterogeneous composition of the corpus has been shown to be advantageous in the training of systems that can accurately extract phenotypic information from a range of different text types. Although the scope of our annotation is currently limited to a single disease, the promising results achieved can stimulate further work into the extraction of phenotypic information for other diseases. The PhenoCHF annotation guidelines and annotations are publicly available at https://code.google.com/p/phenochf-corpus. PMID:26099853

Using text mining techniques to extract phenotypic information from the PhenoCHF corpus.

PubMed

Alnazzawi, Noha; Thompson, Paul; Batista-Navarro, Riza; Ananiadou, Sophia

2015-01-01

Phenotypic information locked away in unstructured narrative text presents significant barriers to information accessibility, both for clinical practitioners and for computerised applications used for clinical research purposes. Text mining (TM) techniques have previously been applied successfully to extract different types of information from text in the biomedical domain. They have the potential to be extended to allow the extraction of information relating to phenotypes from free text. To stimulate the development of TM systems that are able to extract phenotypic information from text, we have created a new corpus (PhenoCHF) that is annotated by domain experts with several types of phenotypic information relating to congestive heart failure. To ensure that systems developed using the corpus are robust to multiple text types, it integrates text from heterogeneous sources, i.e., electronic health records (EHRs) and scientific articles from the literature. We have developed several different phenotype extraction methods to demonstrate the utility of the corpus, and tested these methods on a further corpus, i.e., ShARe/CLEF 2013. Evaluation of our automated methods showed that PhenoCHF can facilitate the training of reliable phenotype extraction systems, which are robust to variations in text type. These results have been reinforced by evaluating our trained systems on the ShARe/CLEF corpus, which contains clinical records of various types. Like other studies within the biomedical domain, we found that solutions based on conditional random fields produced the best results, when coupled with a rich feature set. PhenoCHF is the first annotated corpus aimed at encoding detailed phenotypic information. The unique heterogeneous composition of the corpus has been shown to be advantageous in the training of systems that can accurately extract phenotypic information from a range of different text types. Although the scope of our annotation is currently limited to a single disease, the promising results achieved can stimulate further work into the extraction of phenotypic information for other diseases. The PhenoCHF annotation guidelines and annotations are publicly available at https://code.google.com/p/phenochf-corpus.

Data Mining Techniques Applied to Hydrogen Lactose Breath Test.

PubMed

Rubio-Escudero, Cristina; Valverde-Fernández, Justo; Nepomuceno-Chamorro, Isabel; Pontes-Balanza, Beatriz; Hernández-Mendoza, Yoedusvany; Rodríguez-Herrera, Alfonso

2017-01-01

Analyze a set of data of hydrogen breath tests by use of data mining tools. Identify new patterns of H2 production. Hydrogen breath tests data sets as well as k-means clustering as the data mining technique to a dataset of 2571 patients. Six different patterns have been extracted upon analysis of the hydrogen breath test data. We have also shown the relevance of each of the samples taken throughout the test. Analysis of the hydrogen breath test data sets using data mining techniques has identified new patterns of hydrogen generation upon lactose absorption. We can see the potential of application of data mining techniques to clinical data sets. These results offer promising data for future research on the relations between gut microbiota produced hydrogen and its link to clinical symptoms.

A Review of Financial Accounting Fraud Detection based on Data Mining Techniques

NASA Astrophysics Data System (ADS)

Sharma, Anuj; Kumar Panigrahi, Prabin

2012-02-01

With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. The failure of internal auditing system of the organization in identifying the accounting frauds has lead to use of specialized procedures to detect financial accounting fraud, collective known as forensic accounting. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. This paper presents a comprehensive review of the literature on the application of data mining techniques for the detection of financial accounting fraud and proposes a framework for data mining techniques based accounting fraud detection. The systematic and comprehensive literature review of the data mining techniques applicable to financial accounting fraud detection may provide a foundation to future research in this field. The findings of this review show that data mining techniques like logistic models, neural networks, Bayesian belief network, and decision trees have been applied most extensively to provide primary solutions to the problems inherent in the detection and classification of fraudulent data.

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.

Evaluating bump control techniques through convergence monitoring

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

Campoli, A.A.

1987-07-01

A coal mine bump is the violent failure of a pillar or pillars due to overstress. Retreat coal mining concentrates stresses on the pillars directly outby gob areas, and the situation becomes critical when mining a coalbed encased in rigid associated strata. Bump control techniques employed by the Olga Mine, McDowell County, WV, were evaluated through convergence monitoring in a Bureau of Mines study. Olga uses a novel pillar splitting mining method to extract 55-ft by 70-ft chain pillars, under 1,100 to 1,550 ft of overburden. Three rows of pillars are mined simultaneously to soften the pillar line and reducemore » strain energy storage capacity. Localized stress reduction (destressing) techniques, auger drilling and shot firing, induced approximately 0.1 in. of roof-to-floor convergence in ''high'' -stress pillars near the gob line. Auger drilling of a ''low''-stress pillar located between two barrier pillars produced no convergence effects.« less

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.

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.

MSL: Facilitating automatic and physical analysis of published scientific literature in PDF format.

PubMed

Ahmed, Zeeshan; Dandekar, Thomas

2015-01-01

Published scientific literature contains millions of figures, including information about the results obtained from different scientific experiments e.g. PCR-ELISA data, microarray analysis, gel electrophoresis, mass spectrometry data, DNA/RNA sequencing, diagnostic imaging (CT/MRI and ultrasound scans), and medicinal imaging like electroencephalography (EEG), magnetoencephalography (MEG), echocardiography  (ECG), positron-emission tomography (PET) images. The importance of biomedical figures has been widely recognized in scientific and medicine communities, as they play a vital role in providing major original data, experimental and computational results in concise form. One major challenge for implementing a system for scientific literature analysis is extracting and analyzing text and figures from published PDF files by physical and logical document analysis. Here we present a product line architecture based bioinformatics tool 'Mining Scientific Literature (MSL)', which supports the extraction of text and images by interpreting all kinds of published PDF files using advanced data mining and image processing techniques. It provides modules for the marginalization of extracted text based on different coordinates and keywords, visualization of extracted figures and extraction of embedded text from all kinds of biological and biomedical figures using applied Optimal Character Recognition (OCR). Moreover, for further analysis and usage, it generates the system's output in different formats including text, PDF, XML and images files. Hence, MSL is an easy to install and use analysis tool to interpret published scientific literature in PDF format.

Text Mining for Precision Medicine: Bringing structure to EHRs and biomedical literature to understand genes and health

PubMed Central

Simmons, Michael; Singhal, Ayush; Lu, Zhiyong

2018-01-01

The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text — found in biomedical publications and clinical notes — is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine. PMID:27807747

Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health.

PubMed

Simmons, Michael; Singhal, Ayush; Lu, Zhiyong

2016-01-01

The key question of precision medicine is whether it is possible to find clinically actionable granularity in diagnosing disease and classifying patient risk. The advent of next-generation sequencing and the widespread adoption of electronic health records (EHRs) have provided clinicians and researchers a wealth of data and made possible the precise characterization of individual patient genotypes and phenotypes. Unstructured text-found in biomedical publications and clinical notes-is an important component of genotype and phenotype knowledge. Publications in the biomedical literature provide essential information for interpreting genetic data. Likewise, clinical notes contain the richest source of phenotype information in EHRs. Text mining can render these texts computationally accessible and support information extraction and hypothesis generation. This chapter reviews the mechanics of text mining in precision medicine and discusses several specific use cases, including database curation for personalized cancer medicine, patient outcome prediction from EHR-derived cohorts, and pharmacogenomic research. Taken as a whole, these use cases demonstrate how text mining enables effective utilization of existing knowledge sources and thus promotes increased value for patients and healthcare systems. Text mining is an indispensable tool for translating genotype-phenotype data into effective clinical care that will undoubtedly play an important role in the eventual realization of precision medicine.

Using Text Mining to Uncover Students' Technology-Related Problems in Live Video Streaming

ERIC Educational Resources Information Center

Abdous, M'hammed; He, Wu

2011-01-01

Because of their capacity to sift through large amounts of data, text mining and data mining are enabling higher education institutions to reveal valuable patterns in students' learning behaviours without having to resort to traditional survey methods. In an effort to uncover live video streaming (LVS) students' technology related-problems and to…

The Feasibility of Using Large-Scale Text Mining to Detect Adverse Childhood Experiences in a VA-Treated Population.

PubMed

Hammond, Kenric W; Ben-Ari, Alon Y; Laundry, Ryan J; Boyko, Edward J; Samore, Matthew H

2015-12-01

Free text in electronic health records resists large-scale analysis. Text records facts of interest not found in encoded data, and text mining enables their retrieval and quantification. The U.S. Department of Veterans Affairs (VA) clinical data repository affords an opportunity to apply text-mining methodology to study clinical questions in large populations. To assess the feasibility of text mining, investigation of the relationship between exposure to adverse childhood experiences (ACEs) and recorded diagnoses was conducted among all VA-treated Gulf war veterans, utilizing all progress notes recorded from 2000-2011. Text processing extracted ACE exposures recorded among 44.7 million clinical notes belonging to 243,973 veterans. The relationship of ACE exposure to adult illnesses was analyzed using logistic regression. Bias considerations were assessed. ACE score was strongly associated with suicide attempts and serious mental disorders (ORs = 1.84 to 1.97), and less so with behaviorally mediated and somatic conditions (ORs = 1.02 to 1.36) per unit. Bias adjustments did not remove persistent associations between ACE score and most illnesses. Text mining to detect ACE exposure in a large population was feasible. Analysis of the relationship between ACE score and adult health conditions yielded patterns of association consistent with prior research. Copyright © 2015 International Society for Traumatic Stress Studies.

A strategy for selecting data mining techniques in metabolomics.

PubMed

Banimustafa, Ahmed Hmaidan; Hardy, Nigel W

2012-01-01

There is a general agreement that the development of metabolomics depends not only on advances in chemical analysis techniques but also on advances in computing and data analysis methods. Metabolomics data usually requires intensive pre-processing, analysis, and mining procedures. Selecting and applying such procedures requires attention to issues including justification, traceability, and reproducibility. We describe a strategy for selecting data mining techniques which takes into consideration the goals of data mining techniques on the one hand, and the goals of metabolomics investigations and the nature of the data on the other. The strategy aims to ensure the validity and soundness of results and promote the achievement of the investigation goals.

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.

Data mining techniques for assisting the diagnosis of pressure ulcer development in surgical patients.

PubMed

Su, Chao-Ton; Wang, Pa-Chun; Chen, Yan-Cheng; Chen, Li-Fei

2012-08-01

Pressure ulcer is a serious problem during patient care processes. The high risk factors in the development of pressure ulcer remain unclear during long surgery. Moreover, past preventive policies are hard to implement in a busy operation room. The objective of this study is to use data mining techniques to construct the prediction model for pressure ulcers. Four data mining techniques, namely, Mahalanobis Taguchi System (MTS), Support Vector Machines (SVMs), decision tree (DT), and logistic regression (LR), are used to select the important attributes from the data to predict the incidence of pressure ulcers. Measurements of sensitivity, specificity, F(1), and g-means were used to compare the performance of four classifiers on the pressure ulcer data set. The results show that data mining techniques obtain good results in predicting the incidence of pressure ulcer. We can conclude that data mining techniques can help identify the important factors and provide a feasible model to predict pressure ulcer development.

What Online Communities Can Tell Us About Electronic Cigarettes and Hookah Use: A Study Using Text Mining and Visualization Techniques

PubMed Central

Zhu, Shu-Hong; Conway, Mike

2015-01-01

Background The rise in popularity of electronic cigarettes (e-cigarettes) and hookah over recent years has been accompanied by some confusion and uncertainty regarding the development of an appropriate regulatory response towards these emerging products. Mining online discussion content can lead to insights into people’s experiences, which can in turn further our knowledge of how to address potential health implications. In this work, we take a novel approach to understanding the use and appeal of these emerging products by applying text mining techniques to compare consumer experiences across discussion forums. Objective This study examined content from the websites Vapor Talk, Hookah Forum, and Reddit to understand people’s experiences with different tobacco products. Our investigation involves three parts. First, we identified contextual factors that inform our understanding of tobacco use behaviors, such as setting, time, social relationships, and sensory experience, and compared the forums to identify the ones where content on these factors is most common. Second, we compared how the tobacco use experience differs with combustible cigarettes and e-cigarettes. Third, we investigated differences between e-cigarette and hookah use. Methods In the first part of our study, we employed a lexicon-based extraction approach to estimate prevalence of contextual factors, and then we generated a heat map based on these estimates to compare the forums. In the second and third parts of the study, we employed a text mining technique called topic modeling to identify important topics and then developed a visualization, Topic Bars, to compare topic coverage across forums. Results In the first part of the study, we identified two forums, Vapor Talk Health & Safety and the Stopsmoking subreddit, where discussion concerning contextual factors was particularly common. The second part showed that the discussion in Vapor Talk Health & Safety focused on symptoms and comparisons of combustible cigarettes and e-cigarettes, and the Stopsmoking subreddit focused on psychological aspects of quitting. Last, we examined the discussion content on Vapor Talk and Hookah Forum. Prominent topics included equipment, technique, experiential elements of use, and the buying and selling of equipment. Conclusions This study has three main contributions. Discussion forums differ in the extent to which their content may help us understand behaviors with potential health implications. Identifying dimensions of interest and using a heat map visualization to compare across forums can be helpful for identifying forums with the greatest density of health information. Additionally, our work has shown that the quitting experience can potentially be very different depending on whether or not e-cigarettes are used. Finally, e-cigarette and hookah forums are similar in that members represent a “hobbyist culture” that actively engages in information exchange. These differences have important implications for both tobacco regulation and smoking cessation intervention design. PMID:26420469

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.

BioCreative Workshops for DOE Genome Sciences: Text Mining for Metagenomics

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

Wu, Cathy H.; Hirschman, Lynette

The objective of this project was to host BioCreative workshops to define and develop text mining tasks to meet the needs of the Genome Sciences community, focusing on metadata information extraction in metagenomics. Following the successful introduction of metagenomics at the BioCreative IV workshop, members of the metagenomics community and BioCreative communities continued discussion to identify candidate topics for a BioCreative metagenomics track for BioCreative V. Of particular interest was the capture of environmental and isolation source information from text. The outcome was to form a “community of interest” around work on the interactive EXTRACT system, which supported interactive taggingmore » of environmental and species data. This experiment is included in the BioCreative V virtual issue of Database. In addition, there was broad participation by members of the metagenomics community in the panels held at BioCreative V, leading to valuable exchanges between the text mining developers and members of the metagenomics research community. These exchanges are reflected in a number of the overview and perspective pieces also being captured in the BioCreative V virtual issue. Overall, this conversation has exposed the metagenomics researchers to the possibilities of text mining, and educated the text mining developers to the specific needs of the metagenomics community.« less

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.

Using natural language processing techniques to inform research on nanotechnology.

PubMed

Lewinski, Nastassja A; McInnes, Bridget T

2015-01-01

Literature in the field of nanotechnology is exponentially increasing with more and more engineered nanomaterials being created, characterized, and tested for performance and safety. With the deluge of published data, there is a need for natural language processing approaches to semi-automate the cataloguing of engineered nanomaterials and their associated physico-chemical properties, performance, exposure scenarios, and biological effects. In this paper, we review the different informatics methods that have been applied to patent mining, nanomaterial/device characterization, nanomedicine, and environmental risk assessment. Nine natural language processing (NLP)-based tools were identified: NanoPort, NanoMapper, TechPerceptor, a Text Mining Framework, a Nanodevice Analyzer, a Clinical Trial Document Classifier, Nanotoxicity Searcher, NanoSifter, and NEIMiner. We conclude with recommendations for sharing NLP-related tools through online repositories to broaden participation in nanoinformatics.

Evaluating a Bilingual Text-Mining System with a Taxonomy of Key Words and Hierarchical Visualization for Understanding Learner-Generated Text

ERIC Educational Resources Information Center

Kong, Siu Cheung; Li, Ping; Song, Yanjie

2018-01-01

This study evaluated a bilingual text-mining system, which incorporated a bilingual taxonomy of key words and provided hierarchical visualization, for understanding learner-generated text in the learning management systems through automatic identification and counting of matching key words. A class of 27 in-service teachers studied a course…

Beyond accuracy: creating interoperable and scalable text-mining web services.

PubMed

Wei, Chih-Hsuan; Leaman, Robert; Lu, Zhiyong

2016-06-15

The biomedical literature is a knowledge-rich resource and an important foundation for future research. With over 24 million articles in PubMed and an increasing growth rate, research in automated text processing is becoming increasingly important. We report here our recently developed web-based text mining services for biomedical concept recognition and normalization. Unlike most text-mining software tools, our web services integrate several state-of-the-art entity tagging systems (DNorm, GNormPlus, SR4GN, tmChem and tmVar) and offer a batch-processing mode able to process arbitrary text input (e.g. scholarly publications, patents and medical records) in multiple formats (e.g. BioC). We support multiple standards to make our service interoperable and allow simpler integration with other text-processing pipelines. To maximize scalability, we have preprocessed all PubMed articles, and use a computer cluster for processing large requests of arbitrary text. Our text-mining web service is freely available at http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/tmTools/#curl : Zhiyong.Lu@nih.gov. Published by Oxford University Press 2016. This work is written by US Government employees and is in the public domain in the US.

75 FR 51291 - National Science Board: Sunshine Act Meetings; Notice

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-19

...-Gathering Activities. [cir] COV Report Text-Mining. [cir] Design of Research Questions for External Input. [cir] SBE/CISE Text-Mining Projects. [cir] Using a Blog for Informal Input. Committee on Education and...

Data mining learning bootstrap through semantic thumbnail analysis

NASA Astrophysics Data System (ADS)

Battiato, Sebastiano; Farinella, Giovanni Maria; Giuffrida, Giovanni; Tribulato, Giuseppe

2007-01-01

The rapid increase of technological innovations in the mobile phone industry induces the research community to develop new and advanced systems to optimize services offered by mobile phones operators (telcos) to maximize their effectiveness and improve their business. Data mining algorithms can run over data produced by mobile phones usage (e.g. image, video, text and logs files) to discover user's preferences and predict the most likely (to be purchased) offer for each individual customer. One of the main challenges is the reduction of the learning time and cost of these automatic tasks. In this paper we discuss an experiment where a commercial offer is composed by a small picture augmented with a short text describing the offer itself. Each customer's purchase is properly logged with all relevant information. Upon arrival of new items we need to learn who the best customers (prospects) for each item are, that is, the ones most likely to be interested in purchasing that specific item. Such learning activity is time consuming and, in our specific case, is not applicable given the large number of new items arriving every day. Basically, given the current customer base we are not able to learn on all new items. Thus, we need somehow to select among those new items to identify the best candidates. We do so by using a joint analysis between visual features and text to estimate how good each new item could be, that is, whether or not is worth to learn on it. Preliminary results show the effectiveness of the proposed approach to improve classical data mining techniques.

Evaluation of Aster Images for Characterization and Mapping of Amethyst Mining Residues

NASA Astrophysics Data System (ADS)

Markoski, P. R.; Rolim, S. B. A.

2012-07-01

The objective of this work was to evaluate the potential of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), subsystems VNIR (Visible and Near Infrared) and SWIR (Short Wave Infrared) images, for discrimination and mapping of amethyst mining residues (basalt) in the Ametista do Sul Region, Rio Grande do Sul State, Brazil. This region provides the most part of amethyst mining of the World. The basalt is extracted during the mining process and deposited outside the mine. As a result, mounts of residues (basalt) rise up. These mounts are many times smaller than ASTER pixel size (VNIR - 15 meters and SWIR - 30 meters). Thus, the pixel composition becomes a mixing of various materials, hampering its identification and mapping. Trying to solve this problem, multispectral algorithm Maximum Likelihood (MaxVer) and the hyperspectral technique SAM (Spectral Angle Mapper) were used in this work. Images from ASTER subsystems VNIR and SWIR were used to perform the classifications. SAM technique produced better results than MaxVer algorithm. The main error found by the techniques was the mixing between "shadow" and "mining residues/basalt" classes. With the SAM technique the confusion decreased because it employed the basalt spectral curve as a reference, while the multispectral techniques employed pixels groups that could have spectral mixture with other targets. The results showed that in tropical terrains as the study area, ASTER data can be efficacious for the characterization of mining residues.

76 FR 51274 - Supplemental Nutrition Assistance Program: Major System Failures

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-18

... data mining as necessary to determine if losses are occurring in the process of issuing benefits. It is... further by using data mining techniques on States' data or analyzing QC data for error patterns that may... conjunction with an additional sample of cases. Data mining techniques may be employed when QC data cannot...

Assessment of practicality of remote sensing techniques for a study of the effects of strip mining in Alabama

NASA Technical Reports Server (NTRS)

Hughes, T. H.; Dillion, A. C., III; White, J. R., Jr.; Drummond, S. E., Jr.; Hooks, W. G.

1975-01-01

Because of the volume of coal produced by strip mining, the proximity of mining operations, and the diversity of mining methods (e.g. contour stripping, area stripping, multiple seam stripping, and augering, as well as underground mining), the Warrior Coal Basin seemed best suited for initial studies on the physical impact of strip mining in Alabama. Two test sites, (Cordova and Searles) representative of the various strip mining techniques and environmental problems, were chosen for intensive studies of the correlation between remote sensing and ground truth data. Efforts were eventually concentrated in the Searles Area, since it is more accessible and offers a better opportunity for study of erosional and depositional processes than the Cordova Area.

Environmental characterisation of coal mine waste rock in the field: an example from New Zealand

NASA Astrophysics Data System (ADS)

Hughes, J.; Craw, D.; Peake, B.; Lindsay, P.; Weber, P.

2007-08-01

Characterisation of mine waste rock with respect to acid generation potential is a necessary part of routine mine operations, so that environmentally benign waste rock stacks can be constructed for permanent storage. Standard static characterisation techniques, such as acid neutralisation capacity (ANC), maximum potential acidity, and associated acid-base accounting, require laboratory tests that can be difficult to obtain rapidly at remote mine sites. We show that a combination of paste pH and a simple portable carbonate dissolution test, both techniques that can be done in the field in a 15 min time-frame, is useful for distinguishing rocks that are potentially acid-forming from those that are acid-neutralising. Use of these techniques could allow characterisation of mine wastes at the metre scale during mine excavation operations. Our application of these techniques to pyrite-bearing (total S = 1-4 wt%) but variably calcareous coal mine overburden shows that there is a strong correlation between the portable carbonate dissolution technique and laboratory-determined ANC measurements (range of 0-10 wt% calcite equivalent). Paste pH measurements on the same rocks are bimodal, with high-sulphur, low-calcite rocks yielding pH near 3 after 10 min, whereas high-ANC rocks yield paste pH of 7-8. In our coal mine example, the field tests were most effective when used in conjunction with stratigraphy. However, the same field tests have potential for routine use in any mine in which distinction of acid-generating rocks from acid-neutralising rocks is required. Calibration of field-based acid-base accounting characteristics of the rocks with laboratory-based static and/or kinetic tests is still necessary.

Imitating manual curation of text-mined facts in biomedicine.

PubMed

Rodriguez-Esteban, Raul; Iossifov, Ivan; Rzhetsky, Andrey

2006-09-08

Text-mining algorithms make mistakes in extracting facts from natural-language texts. In biomedical applications, which rely on use of text-mined data, it is critical to assess the quality (the probability that the message is correctly extracted) of individual facts--to resolve data conflicts and inconsistencies. Using a large set of almost 100,000 manually produced evaluations (most facts were independently reviewed more than once, producing independent evaluations), we implemented and tested a collection of algorithms that mimic human evaluation of facts provided by an automated information-extraction system. The performance of our best automated classifiers closely approached that of our human evaluators (ROC score close to 0.95). Our hypothesis is that, were we to use a larger number of human experts to evaluate any given sentence, we could implement an artificial-intelligence curator that would perform the classification job at least as accurately as an average individual human evaluator. We illustrated our analysis by visualizing the predicted accuracy of the text-mined relations involving the term cocaine.

Assimilating Text-Mining & Bio-Informatics Tools to Analyze Cellulase structures

NASA Astrophysics Data System (ADS)

Satyasree, K. P. N. V., Dr; Lalitha Kumari, B., Dr; Jyotsna Devi, K. S. N. V.; Choudri, S. M. Roy; Pratap Joshi, K.

2017-08-01

Text-mining is one of the best potential way of automatically extracting information from the huge biological literature. To exploit its prospective, the knowledge encrypted in the text should be converted to some semantic representation such as entities and relations, which could be analyzed by machines. But large-scale practical systems for this purpose are rare. But text mining could be helpful for generating or validating predictions. Cellulases have abundant applications in various industries. Cellulose degrading enzymes are cellulases and the same producing bacteria - Bacillus subtilis & fungus Pseudomonas putida were isolated from top soil of Guntur Dt. A.P. India. Absolute cultures were conserved on potato dextrose agar medium for molecular studies. In this paper, we presented how well the text mining concepts can be used to analyze cellulase producing bacteria and fungi, their comparative structures are also studied with the aid of well-establised, high quality standard bioinformatic tools such as Bioedit, Swissport, Protparam, EMBOSSwin with which a complete data on Cellulases like structure, constituents of the enzyme has been obtained.

Text mining electronic hospital records to automatically classify admissions against disease: Measuring the impact of linking data sources.

PubMed

Kocbek, Simon; Cavedon, Lawrence; Martinez, David; Bain, Christopher; Manus, Chris Mac; Haffari, Gholamreza; Zukerman, Ingrid; Verspoor, Karin

2016-12-01

Text and data mining play an important role in obtaining insights from Health and Hospital Information Systems. This paper presents a text mining system for detecting admissions marked as positive for several diseases: Lung Cancer, Breast Cancer, Colon Cancer, Secondary Malignant Neoplasm of Respiratory and Digestive Organs, Multiple Myeloma and Malignant Plasma Cell Neoplasms, Pneumonia, and Pulmonary Embolism. We specifically examine the effect of linking multiple data sources on text classification performance. Support Vector Machine classifiers are built for eight data source combinations, and evaluated using the metrics of Precision, Recall and F-Score. Sub-sampling techniques are used to address unbalanced datasets of medical records. We use radiology reports as an initial data source and add other sources, such as pathology reports and patient and hospital admission data, in order to assess the research question regarding the impact of the value of multiple data sources. Statistical significance is measured using the Wilcoxon signed-rank test. A second set of experiments explores aspects of the system in greater depth, focusing on Lung Cancer. We explore the impact of feature selection; analyse the learning curve; examine the effect of restricting admissions to only those containing reports from all data sources; and examine the impact of reducing the sub-sampling. These experiments provide better understanding of how to best apply text classification in the context of imbalanced data of variable completeness. Radiology questions plus patient and hospital admission data contribute valuable information for detecting most of the diseases, significantly improving performance when added to radiology reports alone or to the combination of radiology and pathology reports. Overall, linking data sources significantly improved classification performance for all the diseases examined. However, there is no single approach that suits all scenarios; the choice of the most effective combination of data sources depends on the specific disease to be classified. Copyright © 2016 Elsevier Inc. All rights reserved.

Mining Adverse Drug Reactions in Social Media with Named Entity Recognition and Semantic Methods.

PubMed

Chen, Xiaoyi; Deldossi, Myrtille; Aboukhamis, Rim; Faviez, Carole; Dahamna, Badisse; Karapetiantz, Pierre; Guenegou-Arnoux, Armelle; Girardeau, Yannick; Guillemin-Lanne, Sylvie; Lillo-Le-Louët, Agnès; Texier, Nathalie; Burgun, Anita; Katsahian, Sandrine

2017-01-01

Suspected adverse drug reactions (ADR) reported by patients through social media can be a complementary source to current pharmacovigilance systems. However, the performance of text mining tools applied to social media text data to discover ADRs needs to be evaluated. In this paper, we introduce the approach developed to mine ADR from French social media. A protocol of evaluation is highlighted, which includes a detailed sample size determination and evaluation corpus constitution. Our text mining approach provided very encouraging preliminary results with F-measures of 0.94 and 0.81 for recognition of drugs and symptoms respectively, and with F-measure of 0.70 for ADR detection. Therefore, this approach is promising for downstream pharmacovigilance analysis.

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…

MET network in PubMed: a text-mined network visualization and curation system.

PubMed

Dai, Hong-Jie; Su, Chu-Hsien; Lai, Po-Ting; Huang, Ming-Siang; Jonnagaddala, Jitendra; Rose Jue, Toni; Rao, Shruti; Chou, Hui-Jou; Milacic, Marija; Singh, Onkar; Syed-Abdul, Shabbir; Hsu, Wen-Lian

2016-01-01

Metastasis is the dissemination of a cancer/tumor from one organ to another, and it is the most dangerous stage during cancer progression, causing more than 90% of cancer deaths. Improving the understanding of the complicated cellular mechanisms underlying metastasis requires investigations of the signaling pathways. To this end, we developed a METastasis (MET) network visualization and curation tool to assist metastasis researchers retrieve network information of interest while browsing through the large volume of studies in PubMed. MET can recognize relations among genes, cancers, tissues and organs of metastasis mentioned in the literature through text-mining techniques, and then produce a visualization of all mined relations in a metastasis network. To facilitate the curation process, MET is developed as a browser extension that allows curators to review and edit concepts and relations related to metastasis directly in PubMed. PubMed users can also view the metastatic networks integrated from the large collection of research papers directly through MET. For the BioCreative 2015 interactive track (IAT), a curation task was proposed to curate metastatic networks among PubMed abstracts. Six curators participated in the proposed task and a post-IAT task, curating 963 unique metastatic relations from 174 PubMed abstracts using MET.Database URL: http://btm.tmu.edu.tw/metastasisway. © The Author(s) 2016. Published by Oxford University Press.

What the papers say: Text mining for genomics and systems biology

PubMed Central

2010-01-01

Keeping up with the rapidly growing literature has become virtually impossible for most scientists. This can have dire consequences. First, we may waste research time and resources on reinventing the wheel simply because we can no longer maintain a reliable grasp on the published literature. Second, and perhaps more detrimental, judicious (or serendipitous) combination of knowledge from different scientific disciplines, which would require following disparate and distinct research literatures, is rapidly becoming impossible for even the most ardent readers of research publications. Text mining -- the automated extraction of information from (electronically) published sources -- could potentially fulfil an important role -- but only if we know how to harness its strengths and overcome its weaknesses. As we do not expect that the rate at which scientific results are published will decrease, text mining tools are now becoming essential in order to cope with, and derive maximum benefit from, this information explosion. In genomics, this is particularly pressing as more and more rare disease-causing variants are found and need to be understood. Not being conversant with this technology may put scientists and biomedical regulators at a severe disadvantage. In this review, we introduce the basic concepts underlying modern text mining and its applications in genomics and systems biology. We hope that this review will serve three purposes: (i) to provide a timely and useful overview of the current status of this field, including a survey of present challenges; (ii) to enable researchers to decide how and when to apply text mining tools in their own research; and (iii) to highlight how the research communities in genomics and systems biology can help to make text mining from biomedical abstracts and texts more straightforward. PMID:21106487

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.

Research on preventive technologies for bed-separation water hazard in China coal mines

NASA Astrophysics Data System (ADS)

Gui, Herong; Tong, Shijie; Qiu, Weizhong; Lin, Manli

2018-03-01

Bed-separation water is one of the major water hazards in coal mines. Targeted researches on the preventive technologies are of paramount importance to safe mining. This article studied the restrictive effect of geological and mining factors, such as lithological properties of roof strata, coal seam inclination, water source to bed separations, roof management method, dimensions of mining working face, and mining progress, on the formation of bed-separation water hazard. The key techniques to prevent bed-separation water-related accidents include interception, diversion, destructing the buffer layer, grouting and backfilling, etc. The operation and efficiency of each technique are corroborated in field engineering cases. The results of this study will offer reference to countries with similar mining conditions in the researches on bed-separation water burst and hazard control in coal mines.

Data Mining.

ERIC Educational Resources Information Center

Benoit, Gerald

2002-01-01

Discusses data mining (DM) and knowledge discovery in databases (KDD), taking the view that KDD is the larger view of the entire process, with DM emphasizing the cleaning, warehousing, mining, and visualization of knowledge discovery in databases. Highlights include algorithms; users; the Internet; text mining; and information extraction.…

Ask and Ye Shall Receive? Automated Text Mining of Michigan Capital Facility Finance Bond Election Proposals to Identify Which Topics Are Associated with Bond Passage and Voter Turnout

ERIC Educational Resources Information Center

Bowers, Alex J.; Chen, Jingjing

2015-01-01

The purpose of this study is to bring together recent innovations in the research literature around school district capital facility finance, municipal bond elections, statistical models of conditional time-varying outcomes, and data mining algorithms for automated text mining of election ballot proposals to examine the factors that influence the…

New directions in biomedical text annotation: definitions, guidelines and corpus construction

PubMed Central

Wilbur, W John; Rzhetsky, Andrey; Shatkay, Hagit

2006-01-01

Background While biomedical text mining is emerging as an important research area, practical results have proven difficult to achieve. We believe that an important first step towards more accurate text-mining lies in the ability to identify and characterize text that satisfies various types of information needs. We report here the results of our inquiry into properties of scientific text that have sufficient generality to transcend the confines of a narrow subject area, while supporting practical mining of text for factual information. Our ultimate goal is to annotate a significant corpus of biomedical text and train machine learning methods to automatically categorize such text along certain dimensions that we have defined. Results We have identified five qualitative dimensions that we believe characterize a broad range of scientific sentences, and are therefore useful for supporting a general approach to text-mining: focus, polarity, certainty, evidence, and directionality. We define these dimensions and describe the guidelines we have developed for annotating text with regard to them. To examine the effectiveness of the guidelines, twelve annotators independently annotated the same set of 101 sentences that were randomly selected from current biomedical periodicals. Analysis of these annotations shows 70–80% inter-annotator agreement, suggesting that our guidelines indeed present a well-defined, executable and reproducible task. Conclusion We present our guidelines defining a text annotation task, along with annotation results from multiple independently produced annotations, demonstrating the feasibility of the task. The annotation of a very large corpus of documents along these guidelines is currently ongoing. These annotations form the basis for the categorization of text along multiple dimensions, to support viable text mining for experimental results, methodology statements, and other forms of information. We are currently developing machine learning methods, to be trained and tested on the annotated corpus, that would allow for the automatic categorization of biomedical text along the general dimensions that we have presented. The guidelines in full detail, along with annotated examples, are publicly available. PMID:16867190

MSL: Facilitating automatic and physical analysis of published scientific literature in PDF format

PubMed Central

Ahmed, Zeeshan; Dandekar, Thomas

2018-01-01

Published scientific literature contains millions of figures, including information about the results obtained from different scientific experiments e.g. PCR-ELISA data, microarray analysis, gel electrophoresis, mass spectrometry data, DNA/RNA sequencing, diagnostic imaging (CT/MRI and ultrasound scans), and medicinal imaging like electroencephalography (EEG), magnetoencephalography (MEG), echocardiography  (ECG), positron-emission tomography (PET) images. The importance of biomedical figures has been widely recognized in scientific and medicine communities, as they play a vital role in providing major original data, experimental and computational results in concise form. One major challenge for implementing a system for scientific literature analysis is extracting and analyzing text and figures from published PDF files by physical and logical document analysis. Here we present a product line architecture based bioinformatics tool ‘Mining Scientific Literature (MSL)’, which supports the extraction of text and images by interpreting all kinds of published PDF files using advanced data mining and image processing techniques. It provides modules for the marginalization of extracted text based on different coordinates and keywords, visualization of extracted figures and extraction of embedded text from all kinds of biological and biomedical figures using applied Optimal Character Recognition (OCR). Moreover, for further analysis and usage, it generates the system’s output in different formats including text, PDF, XML and images files. Hence, MSL is an easy to install and use analysis tool to interpret published scientific literature in PDF format. PMID:29721305

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.

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.

Comparing digital data processing techniques for surface mine and reclamation monitoring

NASA Technical Reports Server (NTRS)

Witt, R. G.; Bly, B. G.; Campbell, W. J.; Bloemer, H. H. L.; Brumfield, J. O.

1982-01-01

The results of three techniques used for processing Landsat digital data are compared for their utility in delineating areas of surface mining and subsequent reclamation. An unsupervised clustering algorithm (ISOCLS), a maximum-likelihood classifier (CLASFY), and a hybrid approach utilizing canonical analysis (ISOCLS/KLTRANS/ISOCLS) were compared by means of a detailed accuracy assessment with aerial photography at NASA's Goddard Space Flight Center. Results show that the hybrid approach was superior to the traditional techniques in distinguishing strip mined and reclaimed areas.

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

Chironis, N.P.

This book contains a wealth of valuable information carefully selected and compiled from recent issues of Coal Age magazine. Much of the source material has been gathered by Coal Age Editors during their visits to coal mines, research establishments, universities and technical symposiums. Equally important are the articles and data contributed by over 50 top experts, many of whom are well known to the mining industry. Specifically, this easy-to-use handbook is divided into eleven key areas of underground mining. Here you will find the latest information on continuous mining techniques, longwall and shortwall methods and equipment, specialized mining and boringmore » systems, continuous haulage techniques, improved roof control and ventilation methods, mine communications and instrumentation, power systems, fire control methods, and new mining regulations. There is also a section on engineering and management considerations, including the modern use of computer terminals, practical techniques for picking leaders and for encouraging more safety consciousness in employees, factors affecting absenteeism, and some highly important financial considerations. All of this valuable information has been thoroughly indexed to provide immediate access to the specific data needed by the reader.« less

Data mining in pharma sector: benefits.

PubMed

Ranjan, Jayanthi

2009-01-01

The amount of data getting generated in any sector at present is enormous. The information flow in the pharma industry is huge. Pharma firms are progressing into increased technology-enabled products and services. Data mining, which is knowledge discovery from large sets of data, helps pharma firms to discover patterns in improving the quality of drug discovery and delivery methods. The paper aims to present how data mining is useful in the pharma industry, how its techniques can yield good results in pharma sector, and to show how data mining can really enhance in making decisions using pharmaceutical data. This conceptual paper is written based on secondary study, research and observations from magazines, reports and notes. The author has listed the types of patterns that can be discovered using data mining in pharma data. The paper shows how data mining is useful in the pharma industry and how its techniques can yield good results in pharma sector. Although much work can be produced for discovering knowledge in pharma data using data mining, the paper is limited to conceptualizing the ideas and view points at this stage; future work may include applying data mining techniques to pharma data based on primary research using the available, famous significant data mining tools. Research papers and conceptual papers related to data mining in Pharma industry are rare; this is the motivation for the paper.

Data Mining in Child Welfare.

ERIC Educational Resources Information Center

Schoech, Dick; Quinn, Andrew; Rycraft, Joan R.

2000-01-01

Examines the historical and larger context of data mining and describes data mining processes, techniques, and tools. Illustrates these using a child welfare dataset concerning the employee turnover that is mined, using logistic regression and a Bayesian neural network. Discusses the data mining process, the resulting models, their predictive…

Text mining of rheumatoid arthritis and diabetes mellitus to understand the mechanisms of Chinese medicine in different diseases with same treatment.

PubMed

Zhao, Ning; Zheng, Guang; Li, Jian; Zhao, Hong-Yan; Lu, Cheng; Jiang, Miao; Zhang, Chi; Guo, Hong-Tao; Lu, Ai-Ping

2018-01-09

To identify the commonalities between rheumatoid arthritis (RA) and diabetes mellitus (DM) to understand the mechanisms of Chinese medicine (CM) in different diseases with the same treatment. A text mining approach was adopted to analyze the commonalities between RA and DM according to CM and biological elements. The major commonalities were subsequently verifified in RA and DM rat models, in which herbal formula for the treatment of both RA and DM identifified via text mining was used as the intervention. Similarities were identifified between RA and DM regarding the CM approach used for diagnosis and treatment, as well as the networks of biological activities affected by each disease, including the involvement of adhesion molecules, oxidative stress, cytokines, T-lymphocytes, apoptosis, and inflfl ammation. The Ramulus Cinnamomi-Radix Paeoniae Alba-Rhizoma Anemarrhenae is an herbal combination used to treat RA and DM. This formula demonstrated similar effects on oxidative stress and inflfl ammation in rats with collagen-induced arthritis, which supports the text mining results regarding the commonalities between RA and DM. Commonalities between the biological activities involved in RA and DM were identifified through text mining, and both RA and DM might be responsive to the same intervention at a specifific stage.

Matisse: A Visual Analytics System for Exploring Emotion Trends in Social Media Text Streams

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

Steed, Chad A; Drouhard, Margaret MEG G; Beaver, Justin M

Dynamically mining textual information streams to gain real-time situational awareness is especially challenging with social media systems where throughput and velocity properties push the limits of a static analytical approach. In this paper, we describe an interactive visual analytics system, called Matisse, that aids with the discovery and investigation of trends in streaming text. Matisse addresses the challenges inherent to text stream mining through the following technical contributions: (1) robust stream data management, (2) automated sentiment/emotion analytics, (3) interactive coordinated visualizations, and (4) a flexible drill-down interaction scheme that accesses multiple levels of detail. In addition to positive/negative sentiment prediction,more » Matisse provides fine-grained emotion classification based on Valence, Arousal, and Dominance dimensions and a novel machine learning process. Information from the sentiment/emotion analytics are fused with raw data and summary information to feed temporal, geospatial, term frequency, and scatterplot visualizations using a multi-scale, coordinated interaction model. After describing these techniques, we conclude with a practical case study focused on analyzing the Twitter sample stream during the week of the 2013 Boston Marathon bombings. The case study demonstrates the effectiveness of Matisse at providing guided situational awareness of significant trends in social media streams by orchestrating computational power and human cognition.« less

The Weather Forecast Using Data Mining Research Based on Cloud Computing.

NASA Astrophysics Data System (ADS)

Wang, ZhanJie; Mazharul Mujib, A. B. M.

2017-10-01

Weather forecasting has been an important application in meteorology and one of the most scientifically and technologically challenging problem around the world. In my study, we have analyzed the use of data mining techniques in forecasting weather. This paper proposes a modern method to develop a service oriented architecture for the weather information systems which forecast weather using these data mining techniques. This can be carried out by using Artificial Neural Network and Decision tree Algorithms and meteorological data collected in Specific time. Algorithm has presented the best results to generate classification rules for the mean weather variables. The results showed that these data mining techniques can be enough for weather forecasting.

Using natural language processing techniques to inform research on nanotechnology

PubMed Central

Lewinski, Nastassja A

2015-01-01

Summary Literature in the field of nanotechnology is exponentially increasing with more and more engineered nanomaterials being created, characterized, and tested for performance and safety. With the deluge of published data, there is a need for natural language processing approaches to semi-automate the cataloguing of engineered nanomaterials and their associated physico-chemical properties, performance, exposure scenarios, and biological effects. In this paper, we review the different informatics methods that have been applied to patent mining, nanomaterial/device characterization, nanomedicine, and environmental risk assessment. Nine natural language processing (NLP)-based tools were identified: NanoPort, NanoMapper, TechPerceptor, a Text Mining Framework, a Nanodevice Analyzer, a Clinical Trial Document Classifier, Nanotoxicity Searcher, NanoSifter, and NEIMiner. We conclude with recommendations for sharing NLP-related tools through online repositories to broaden participation in nanoinformatics. PMID:26199848

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.

Association mining of dependency between time series

NASA Astrophysics Data System (ADS)

Hafez, Alaaeldin

2001-03-01

Time series analysis is considered as a crucial component of strategic control over a broad variety of disciplines in business, science and engineering. Time series data is a sequence of observations collected over intervals of time. Each time series describes a phenomenon as a function of time. Analysis on time series data includes discovering trends (or patterns) in a time series sequence. In the last few years, data mining has emerged and been recognized as a new technology for data analysis. Data Mining is the process of discovering potentially valuable patterns, associations, trends, sequences and dependencies in data. Data mining techniques can discover information that many traditional business analysis and statistical techniques fail to deliver. In this paper, we adapt and innovate data mining techniques to analyze time series data. By using data mining techniques, maximal frequent patterns are discovered and used in predicting future sequences or trends, where trends describe the behavior of a sequence. In order to include different types of time series (e.g. irregular and non- systematic), we consider past frequent patterns of the same time sequences (local patterns) and of other dependent time sequences (global patterns). We use the word 'dependent' instead of the word 'similar' for emphasis on real life time series where two time series sequences could be completely different (in values, shapes, etc.), but they still react to the same conditions in a dependent way. In this paper, we propose the Dependence Mining Technique that could be used in predicting time series sequences. The proposed technique consists of three phases: (a) for all time series sequences, generate their trend sequences, (b) discover maximal frequent trend patterns, generate pattern vectors (to keep information of frequent trend patterns), use trend pattern vectors to predict future time series sequences.

Literature mining, gene-set enrichment and pathway analysis for target identification in Behçet's disease.

PubMed

Wilson, Paul; Larminie, Christopher; Smith, Rona

2016-01-01

To use literature mining to catalogue Behçet's associated genes, and advanced computational methods to improve the understanding of the pathways and signalling mechanisms that lead to the typical clinical characteristics of Behçet's patients. To extend this technique to identify potential treatment targets for further experimental validation. Text mining methods combined with gene enrichment tools, pathway analysis and causal analysis algorithms. This approach identified 247 human genes associated with Behçet's disease and the resulting disease map, comprising 644 nodes and 19220 edges, captured important details of the relationships between these genes and their associated pathways, as described in diverse data repositories. Pathway analysis has identified how Behçet's associated genes are likely to participate in innate and adaptive immune responses. Causal analysis algorithms have identified a number of potential therapeutic strategies for further investigation. Computational methods have captured pertinent features of the prominent disease characteristics presented in Behçet's disease and have highlighted NOD2, ICOS and IL18 signalling as potential therapeutic strategies.

Text Mining of Journal Articles for Sleep Disorder Terminologies.

PubMed

Lam, Calvin; Lai, Fu-Chih; Wang, Chia-Hui; Lai, Mei-Hsin; Hsu, Nanly; Chung, Min-Huey

2016-01-01

Research on publication trends in journal articles on sleep disorders (SDs) and the associated methodologies by using text mining has been limited. The present study involved text mining for terms to determine the publication trends in sleep-related journal articles published during 2000-2013 and to identify associations between SD and methodology terms as well as conducting statistical analyses of the text mining findings. SD and methodology terms were extracted from 3,720 sleep-related journal articles in the PubMed database by using MetaMap. The extracted data set was analyzed using hierarchical cluster analyses and adjusted logistic regression models to investigate publication trends and associations between SD and methodology terms. MetaMap had a text mining precision, recall, and false positive rate of 0.70, 0.77, and 11.51%, respectively. The most common SD term was breathing-related sleep disorder, whereas narcolepsy was the least common. Cluster analyses showed similar methodology clusters for each SD term, except narcolepsy. The logistic regression models showed an increasing prevalence of insomnia, parasomnia, and other sleep disorders but a decreasing prevalence of breathing-related sleep disorder during 2000-2013. Different SD terms were positively associated with different methodology terms regarding research design terms, measure terms, and analysis terms. Insomnia-, parasomnia-, and other sleep disorder-related articles showed an increasing publication trend, whereas those related to breathing-related sleep disorder showed a decreasing trend. Furthermore, experimental studies more commonly focused on hypersomnia and other SDs and less commonly on insomnia, breathing-related sleep disorder, narcolepsy, and parasomnia. Thus, text mining may facilitate the exploration of the publication trends in SDs and the associated methodologies.

Text Mining to Support Gene Ontology Curation and Vice Versa.

PubMed

Ruch, Patrick

2017-01-01

In this chapter, we explain how text mining can support the curation of molecular biology databases dealing with protein functions. We also show how curated data can play a disruptive role in the developments of text mining methods. We review a decade of efforts to improve the automatic assignment of Gene Ontology (GO) descriptors, the reference ontology for the characterization of genes and gene products. To illustrate the high potential of this approach, we compare the performances of an automatic text categorizer and show a large improvement of +225 % in both precision and recall on benchmarked data. We argue that automatic text categorization functions can ultimately be embedded into a Question-Answering (QA) system to answer questions related to protein functions. Because GO descriptors can be relatively long and specific, traditional QA systems cannot answer such questions. A new type of QA system, so-called Deep QA which uses machine learning methods trained with curated contents, is thus emerging. Finally, future advances of text mining instruments are directly dependent on the availability of high-quality annotated contents at every curation step. Databases workflows must start recording explicitly all the data they curate and ideally also some of the data they do not curate.

[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.

The Hazards of Data Mining in Healthcare.

PubMed

Househ, Mowafa; Aldosari, Bakheet

2017-01-01

From the mid-1990s, data mining methods have been used to explore and find patterns and relationships in healthcare data. During the 1990s and early 2000's, data mining was a topic of great interest to healthcare researchers, as data mining showed some promise in the use of its predictive techniques to help model the healthcare system and improve the delivery of healthcare services. However, it was soon discovered that mining healthcare data had many challenges relating to the veracity of healthcare data and limitations around predictive modelling leading to failures of data mining projects. As the Big Data movement has gained momentum over the past few years, there has been a reemergence of interest in the use of data mining techniques and methods to analyze healthcare generated Big Data. Much has been written on the positive impacts of data mining on healthcare practice relating to issues of best practice, fraud detection, chronic disease management, and general healthcare decision making. Little has been written about the limitations and challenges of data mining use in healthcare. In this review paper, we explore some of the limitations and challenges in the use of data mining techniques in healthcare. Our results show that the limitations of data mining in healthcare include reliability of medical data, data sharing between healthcare organizations, inappropriate modelling leading to inaccurate predictions. We conclude that there are many pitfalls in the use of data mining in healthcare and more work is needed to show evidence of its utility in facilitating healthcare decision-making for healthcare providers, managers, and policy makers and more evidence is needed on data mining's overall impact on healthcare services and patient care.

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.

Recent progress in automatically extracting information from the pharmacogenomic literature

PubMed Central

Garten, Yael; Coulet, Adrien; Altman, Russ B

2011-01-01

The biomedical literature holds our understanding of pharmacogenomics, but it is dispersed across many journals. In order to integrate our knowledge, connect important facts across publications and generate new hypotheses we must organize and encode the contents of the literature. By creating databases of structured pharmocogenomic knowledge, we can make the value of the literature much greater than the sum of the individual reports. We can, for example, generate candidate gene lists or interpret surprising hits in genome-wide association studies. Text mining automatically adds structure to the unstructured knowledge embedded in millions of publications, and recent years have seen a surge in work on biomedical text mining, some specific to pharmacogenomics literature. These methods enable extraction of specific types of information and can also provide answers to general, systemic queries. In this article, we describe the main tasks of text mining in the context of pharmacogenomics, summarize recent applications and anticipate the next phase of text mining applications. PMID:21047206

A case-based reasoning tool for breast cancer knowledge management with data mining concepts and techniques

NASA Astrophysics Data System (ADS)

Demigha, Souâd.

2016-03-01

The paper presents a Case-Based Reasoning Tool for Breast Cancer Knowledge Management to improve breast cancer screening. To develop this tool, we combine both concepts and techniques of Case-Based Reasoning (CBR) and Data Mining (DM). Physicians and radiologists ground their diagnosis on their expertise (past experience) based on clinical cases. Case-Based Reasoning is the process of solving new problems based on the solutions of similar past problems and structured as cases. CBR is suitable for medical use. On the other hand, existing traditional hospital information systems (HIS), Radiological Information Systems (RIS) and Picture Archiving Information Systems (PACS) don't allow managing efficiently medical information because of its complexity and heterogeneity. Data Mining is the process of mining information from a data set and transform it into an understandable structure for further use. Combining CBR to Data Mining techniques will facilitate diagnosis and decision-making of medical experts.

Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods.

PubMed

Patel, Tejal A; Puppala, Mamta; Ogunti, Richard O; Ensor, Joe E; He, Tiancheng; Shewale, Jitesh B; Ankerst, Donna P; Kaklamani, Virginia G; Rodriguez, Angel A; Wong, Stephen T C; Chang, Jenny C

2017-01-01

A key challenge to mining electronic health records for mammography research is the preponderance of unstructured narrative text, which strikingly limits usable output. The imaging characteristics of breast cancer subtypes have been described previously, but without standardization of parameters for data mining. The authors searched the enterprise-wide data warehouse at the Houston Methodist Hospital, the Methodist Environment for Translational Enhancement and Outcomes Research (METEOR), for patients with Breast Imaging Reporting and Data System (BI-RADS) category 5 mammogram readings performed between January 2006 and May 2015 and an available pathology report. The authors developed natural language processing (NLP) software algorithms to automatically extract mammographic and pathologic findings from free text mammogram and pathology reports. The correlation between mammographic imaging features and breast cancer subtype was analyzed using one-way analysis of variance and the Fisher exact test. The NLP algorithm was able to obtain key characteristics for 543 patients who met the inclusion criteria. Patients with estrogen receptor-positive tumors were more likely to have spiculated margins (P = .0008), and those with tumors that overexpressed human epidermal growth factor receptor 2 (HER2) were more likely to have heterogeneous and pleomorphic calcifications (P = .0078 and P = .0002, respectively). Mammographic imaging characteristics, obtained from an automated text search and the extraction of mammogram reports using NLP techniques, correlated with pathologic breast cancer subtype. The results of the current study validate previously reported trends assessed by manual data collection. Furthermore, NLP provides an automated means with which to scale up data extraction and analysis for clinical decision support. Cancer 2017;114-121. © 2016 American Cancer Society. © 2016 American Cancer Society.

QTLTableMiner++: semantic mining of QTL tables in scientific articles.

PubMed

Singh, Gurnoor; Kuzniar, Arnold; van Mulligen, Erik M; Gavai, Anand; Bachem, Christian W; Visser, Richard G F; Finkers, Richard

2018-05-25

A quantitative trait locus (QTL) is a genomic region that correlates with a phenotype. Most of the experimental information about QTL mapping studies is described in tables of scientific publications. Traditional text mining techniques aim to extract information from unstructured text rather than from tables. We present QTLTableMiner ++ (QTM), a table mining tool that extracts and semantically annotates QTL information buried in (heterogeneous) tables of plant science literature. QTM is a command line tool written in the Java programming language. This tool takes scientific articles from the Europe PMC repository as input, extracts QTL tables using keyword matching and ontology-based concept identification. The tables are further normalized using rules derived from table properties such as captions, column headers and table footers. Furthermore, table columns are classified into three categories namely column descriptors, properties and values based on column headers and data types of cell entries. Abbreviations found in the tables are expanded using the Schwartz and Hearst algorithm. Finally, the content of QTL tables is semantically enriched with domain-specific ontologies (e.g. Crop Ontology, Plant Ontology and Trait Ontology) using the Apache Solr search platform and the results are stored in a relational database and a text file. The performance of the QTM tool was assessed by precision and recall based on the information retrieved from two manually annotated corpora of open access articles, i.e. QTL mapping studies in tomato (Solanum lycopersicum) and in potato (S. tuberosum). In summary, QTM detected QTL statements in tomato with 74.53% precision and 92.56% recall and in potato with 82.82% precision and 98.94% recall. QTM is a unique tool that aids in providing QTL information in machine-readable and semantically interoperable formats.

CARIBIAM: constrained Association Rules using Interactive Biological IncrementAl Mining.

PubMed

Rahal, Imad; Rahhal, Riad; Wang, Baoying; Perrizo, William

2008-01-01

This paper analyses annotated genome data by applying a very central data-mining technique known as Association Rule Mining (ARM) with the aim of discovering rules and hypotheses capable of yielding deeper insights into this type of data. In the literature, ARM has been noted for producing an overwhelming number of rules. This work proposes a new technique capable of using domain knowledge in the form of queries in order to efficiently mine only the subset of the associations that are of interest to investigators in an incremental and interactive manner.

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

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.

Technique for predicting ground-water discharge to surface coal mines and resulting changes in head

USGS Publications Warehouse

Weiss, L.S.; Galloway, D.L.; Ishii, Audrey L.

1986-01-01

Changes in seepage flux and head (groundwater level) from groundwater drainage into a surface coal mine can be predicted by a technique that considers drainage from the unsaturated zone. The user applies site-specific data to precalculated head and seepage-flux profiles. Groundwater flow through hypothetical aquifer cross sections was simulated using the U.S. Geological Survey finite-difference model, VS2D, which considers variably saturated two-dimensional flow. Conceptual models considered were (1) drainage to a first cut, and (2) drainage to multiple cuts, which includes drainage effects of an area surface mine. Dimensionless head and seepage flux profiles from 246 simulations are presented. Step-by-step instructions and examples are presented. Users are required to know aquifer characteristics and to estimate size and timing of the mine operation at a proposed site. Calculated groundwater drainage to the mine is from one excavated face only. First cut considers confined and unconfined aquifers of a wide range of permeabilities; multiple cuts considers unconfined aquifers of higher permeabilities only. The technique, developed for Illinois coal-mining regions that use area surface mining and evaluated with an actual field example, will be useful in assessing potential hydrologic impacts of mining. Application is limited to hydrogeologic settings and mine operations similar to those considered. Fracture flow, recharge, and leakage are nor considered. (USGS)

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.

Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery.

PubMed

Gonzalez, Graciela H; Tahsin, Tasnia; Goodale, Britton C; Greene, Anna C; Greene, Casey S

2016-01-01

Precision medicine will revolutionize the way we treat and prevent disease. A major barrier to the implementation of precision medicine that clinicians and translational scientists face is understanding the underlying mechanisms of disease. We are starting to address this challenge through automatic approaches for information extraction, representation and analysis. Recent advances in text and data mining have been applied to a broad spectrum of key biomedical questions in genomics, pharmacogenomics and other fields. We present an overview of the fundamental methods for text and data mining, as well as recent advances and emerging applications toward precision medicine. © The Author 2015. Published by Oxford University Press.

Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery

PubMed Central

Gonzalez, Graciela H.; Tahsin, Tasnia; Goodale, Britton C.; Greene, Anna C.

2016-01-01

Precision medicine will revolutionize the way we treat and prevent disease. A major barrier to the implementation of precision medicine that clinicians and translational scientists face is understanding the underlying mechanisms of disease. We are starting to address this challenge through automatic approaches for information extraction, representation and analysis. Recent advances in text and data mining have been applied to a broad spectrum of key biomedical questions in genomics, pharmacogenomics and other fields. We present an overview of the fundamental methods for text and data mining, as well as recent advances and emerging applications toward precision medicine. PMID:26420781

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.

Individual Profiling Using Text Analysis

DTIC Science & Technology

2016-04-15

Mining a Text for Errors. . . . on Knowledge discovery in data mining , pages 624–628, 2005. [12] Michal Kosinski, David Stillwell, and Thore Graepel...AFRL-AFOSR-UK-TR-2016-0011 Individual Profiling using Text Analysis 140333 Mark Stevenson UNIVERSITY OF SHEFFIELD, DEPARTMENT OF PSYCHOLOGY Final...REPORT TYPE      Final 3.  DATES COVERED (From - To)      15 Sep 2014 to 14 Sep 2015 4.  TITLE AND SUBTITLE Individual Profiling using Text Analysis

Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques.

PubMed

Sanmiquel, Lluís; Bascompta, Marc; Rossell, Josep M; Anticoi, Hernán Francisco; Guash, Eduard

2018-03-07

An analysis of occupational accidents in the mining sector was conducted using the data from the Spanish Ministry of Employment and Social Safety between 2005 and 2015, and data-mining techniques were applied. Data was processed with the software Weka. Two scenarios were chosen from the accidents database: surface and underground mining. The most important variables involved in occupational accidents and their association rules were determined. These rules are composed of several predictor variables that cause accidents, defining its characteristics and context. This study exposes the 20 most important association rules in the sector-either surface or underground mining-based on the statistical confidence levels of each rule as obtained by Weka. The outcomes display the most typical immediate causes, along with the percentage of accidents with a basis in each association rule. The most important immediate cause is body movement with physical effort or overexertion, and the type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident are different between the two scenarios. Data-mining techniques were chosen as a useful tool to find out the root cause of the accidents.

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.

Data Mining: Going beyond Traditional Statistics

ERIC Educational Resources Information Center

Zhao, Chun-Mei; Luan, Jing

2006-01-01

The authors provide an overview of data mining, giving special attention to the relationship between data mining and statistics to unravel some misunderstandings about the two techniques. (Contains 1 figure.)

40 CFR 372.23 - SIC and NAICS codes to which this Part applies.

Code of Federal Regulations, 2010 CFR

2010-07-01

... facilities primarily engaged in reproducing text, drawings, plans, maps, or other copy, by blueprinting...)); 212324Kaolin and Ball Clay Mining Limited to facilities operating without a mine or quarry and that are...)); 212393Other Chemical and Fertilizer Mineral Mining Limited to facilities operating without a mine or quarry...

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.

Mine Water Treatment in Hongai Coal Mines

NASA Astrophysics Data System (ADS)

Dang, Phuong Thao; Dang, Vu Chi

2018-03-01

Acid mine drainage (AMD) is recognized as one of the most serious environmental problem associated with mining industry. Acid water, also known as acid mine drainage forms when iron sulfide minerals found in the rock of coal seams are exposed to oxidizing conditions in coal mining. Until 2009, mine drainage in Hongai coal mines was not treated, leading to harmful effects on humans, animals and aquatic ecosystem. This report has examined acid mine drainage problem and techniques for acid mine drainage treatment in Hongai coal mines. In addition, selection and criteria for the design of the treatment systems have been presented.

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.

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.

In-situ identification of anti-personnel mines using acoustic resonant spectroscopy

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

Perry, R L; Roberts, R S

1999-02-01

A new technique for identifying buried Anti-Personnel Mines is described, and a set of preliminary experiments designed to assess the feasibility of this technique is presented. Analysis of the experimental results indicates that the technique has potential, but additional work is required to bring the technique to fruition. In addition to the experimental results presented here, a technique used to characterize the sensor employed in the experiments is detailed.

Responses of Terrestrial Herpetofauna to Persistent, Novel Ecosystems Resulting from Mountaintop Removal Mining

Treesearch

Jennifer M. Williams; Donald J. Brown; Petra B. Wood

2017-01-01

Mountaintop removal mining is a large-scale surface mining technique that removes entire floral and faunal communities, along with soil horizons located above coal seams. In West Virginia, the majority of this mining occurs on forested mountaintops. However, after mining ceases the land is typically reclaimed to grasslands and shrublands, resulting in novel ecosystems...

The Labour Welfare Fund Laws (Amendment) Act, 1987 (No. 15 of 1987), 22 May 1987.

PubMed

1987-01-01

This Act authorizes funds constituted under the Mica Mines Labour Welfare Fund Act, 1946, the Limestone and Dolomite Mines Labour Welfare Fund Act, 1972, the Iron Ore Mines, Manganese Ore Mines and Chrome Mines Labour Welfare Fund Act, 1976, and the Beedi Workers Welfare Fund Act, 1976, to be applied for the provision of family welfare, including family planning education and services. full text

Data-Mining Technologies for Diabetes: A Systematic Review

PubMed Central

Marinov, Miroslav; Mosa, Abu Saleh Mohammad; Yoo, Illhoi; Boren, Suzanne Austin

2011-01-01

Background The objective of this study is to conduct a systematic review of applications of data-mining techniques in the field of diabetes research. Method We searched the MEDLINE database through PubMed. We initially identified 31 articles by the search, and selected 17 articles representing various data-mining methods used for diabetes research. Our main interest was to identify research goals, diabetes types, data sets, data-mining methods, data-mining software and technologies, and outcomes. Results The applications of data-mining techniques in the selected articles were useful for extracting valuable knowledge and generating new hypothesis for further scientific research/experimentation and improving health care for diabetes patients. The results could be used for both scientific research and real-life practice to improve the quality of health care diabetes patients. Conclusions Data mining has played an important role in diabetes research. Data mining would be a valuable asset for diabetes researchers because it can unearth hidden knowledge from a huge amount of diabetes-related data. We believe that data mining can significantly help diabetes research and ultimately improve the quality of health care for diabetes patients. PMID:22226277

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

Automated assessment of medical training evaluation text.

PubMed

Zhang, Rui; Pakhomov, Serguei; Gladding, Sophia; Aylward, Michael; Borman-Shoap, Emily; Melton, Genevieve B

2012-01-01

Medical post-graduate residency training and medical student training increasingly utilize electronic systems to evaluate trainee performance based on defined training competencies with quantitative and qualitative data, the later of which typically consists of text comments. Medical education is concomitantly becoming a growing area of clinical research. While electronic systems have proliferated in number, little work has been done to help manage and analyze qualitative data from these evaluations. We explored the use of text-mining techniques to assist medical education researchers in sentiment analysis and topic analysis of residency evaluations with a sample of 812 evaluation statements. While comments were predominantly positive, sentiment analysis improved the ability to discriminate statements with 93% accuracy. Similar to other domains, Latent Dirichlet Analysis and Information Gain revealed groups of core subjects and appear to be useful for identifying topics from this data.

Surface mining

Treesearch

Robert Leopold; Bruce Rowland; Reed Stalder

1979-01-01

The surface mining process consists of four phases: (1) exploration; (2) development; (3) production; and (4) reclamation. A variety of surface mining methods has been developed, including strip mining, auger, area strip, open pit, dredging, and hydraulic. Sound planning and design techniques are essential to implement alternatives to meet the myriad of laws,...

A Survey of Educational Data-Mining Research

ERIC Educational Resources Information Center

Huebner, Richard A.

2013-01-01

Educational data mining (EDM) is an emerging discipline that focuses on applying data mining tools and techniques to educationally related data. The discipline focuses on analyzing educational data to develop models for improving learning experiences and improving institutional effectiveness. A literature review on educational data mining topics…

A Comparative Study of Data Mining Techniques on Football Match Prediction

NASA Astrophysics Data System (ADS)

Rosli, Che Mohamad Firdaus Che Mohd; Zainuri Saringat, Mohd; Razali, Nazim; Mustapha, Aida

2018-05-01

Data prediction have become a trend in today’s business or organization. This paper is set to predict match outcomes for association football from the perspective of football club managers and coaches. This paper explored different data mining techniques used for predicting the match outcomes where the target class is win, draw and lose. The main objective of this research is to find the most accurate data mining technique that fits the nature of football data. The techniques tested are Decision Trees, Neural Networks, Bayesian Network, and k-Nearest Neighbors. The results from the comparative experiments showed that Decision Trees produced the highest average prediction accuracy in the domain of football match prediction by 99.56%.

Automated Text Data Mining Analysis of Five Decades of Educational Leadership Research Literature: Probabilistic Topic Modeling of "EAQ" Articles From 1965 to 2014

ERIC Educational Resources Information Center

Wang, Yinying; Bowers, Alex J.; Fikis, David J.

2017-01-01

Purpose: The purpose of this study is to describe the underlying topics and the topic evolution in the 50-year history of educational leadership research literature. Method: We used automated text data mining with probabilistic latent topic models to examine the full text of the entire publication history of all 1,539 articles published in…

Post-acquisition data mining techniques for LC-MS/MS-acquired data in drug metabolite identification.

PubMed

Dhurjad, Pooja Sukhdev; Marothu, Vamsi Krishna; Rathod, Rajeshwari

2017-08-01

Metabolite identification is a crucial part of the drug discovery process. LC-MS/MS-based metabolite identification has gained widespread use, but the data acquired by the LC-MS/MS instrument is complex, and thus the interpretation of data becomes troublesome. Fortunately, advancements in data mining techniques have simplified the process of data interpretation with improved mass accuracy and provide a potentially selective, sensitive, accurate and comprehensive way for metabolite identification. In this review, we have discussed the targeted (extracted ion chromatogram, mass defect filter, product ion filter, neutral loss filter and isotope pattern filter) and untargeted (control sample comparison, background subtraction and metabolomic approaches) post-acquisition data mining techniques, which facilitate the drug metabolite identification. We have also discussed the importance of integrated data mining strategy.

Knowledge Discovery and Data Mining: An Overview

NASA Technical Reports Server (NTRS)

Fayyad, U.

1995-01-01

The process of knowledge discovery and data mining is the process of information extraction from very large databases. Its importance is described along with several techniques and considerations for selecting the most appropriate technique for extracting information from a particular data set.

Application of Information-Theoretic Data Mining Techniques in a National Ambulatory Practice Outcomes Research Network

PubMed Central

Wright, Adam; Ricciardi, Thomas N.; Zwick, Martin

2005-01-01

The Medical Quality Improvement Consortium data warehouse contains de-identified data on more than 3.6 million patients including their problem lists, test results, procedures and medication lists. This study uses reconstructability analysis, an information-theoretic data mining technique, on the MQIC data warehouse to empirically identify risk factors for various complications of diabetes including myocardial infarction and microalbuminuria. The risk factors identified match those risk factors identified in the literature, demonstrating the utility of the MQIC data warehouse for outcomes research, and RA as a technique for mining clinical data warehouses. PMID:16779156

Prediction of pork quality parameters by applying fractals and data mining on MRI.

PubMed

Caballero, Daniel; Pérez-Palacios, Trinidad; Caro, Andrés; Amigo, José Manuel; Dahl, Anders B; ErsbØll, Bjarne K; Antequera, Teresa

2017-09-01

This work firstly investigates the use of MRI, fractal algorithms and data mining techniques to determine pork quality parameters non-destructively. The main objective was to evaluate the capability of fractal algorithms (Classical Fractal algorithm, CFA; Fractal Texture Algorithm, FTA and One Point Fractal Texture Algorithm, OPFTA) to analyse MRI in order to predict quality parameters of loin. In addition, the effect of the sequence acquisition of MRI (Gradient echo, GE; Spin echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple linear regression, MLR) were analysed. Both fractal algorithm, FTA and OPFTA are appropriate to analyse MRI of loins. The sequence acquisition, the fractal algorithm and the data mining technique seems to influence on the prediction results. For most physico-chemical parameters, prediction equations with moderate to excellent correlation coefficients were achieved by using the following combinations of acquisition sequences of MRI, fractal algorithms and data mining techniques: SE-FTA-MLR, SE-OPFTA-IR, GE-OPFTA-MLR, SE-OPFTA-MLR, with the last one offering the best prediction results. Thus, SE-OPFTA-MLR could be proposed as an alternative technique to determine physico-chemical traits of fresh and dry-cured loins in a non-destructive way with high accuracy. Copyright © 2017. Published by Elsevier Ltd.

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.

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

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

Developing a hybrid dictionary-based bio-entity recognition technique.

PubMed

Song, Min; Yu, Hwanjo; Han, Wook-Shin

2015-01-01

Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall.

Developing a hybrid dictionary-based bio-entity recognition technique

PubMed Central

2015-01-01

Background Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. Methods This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. Results The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. Conclusions The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall. PMID:26043907

Uncovering text mining: A survey of current work on web-based epidemic intelligence

PubMed Central

Collier, Nigel

2012-01-01

Real world pandemics such as SARS 2002 as well as popular fiction like the movie Contagion graphically depict the health threat of a global pandemic and the key role of epidemic intelligence (EI). While EI relies heavily on established indicator sources a new class of methods based on event alerting from unstructured digital Internet media is rapidly becoming acknowledged within the public health community. At the heart of automated information gathering systems is a technology called text mining. My contribution here is to provide an overview of the role that text mining technology plays in detecting epidemics and to synthesise my existing research on the BioCaster project. PMID:22783909

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.

Data Mining and Knowledge Management in Higher Education -Potential Applications.

ERIC Educational Resources Information Center

Luan, Jing

This paper introduces a new decision support tool, data mining, in the context of knowledge management. The most striking features of data mining techniques are clustering and prediction. The clustering aspect of data mining offers comprehensive characteristics analysis of students, while the predicting function estimates the likelihood for a…

Content Abstract Classification Using Naive Bayes

NASA Astrophysics Data System (ADS)

Latif, Syukriyanto; Suwardoyo, Untung; Aldrin Wihelmus Sanadi, Edwin

2018-03-01

This study aims to classify abstract content based on the use of the highest number of words in an abstract content of the English language journals. This research uses a system of text mining technology that extracts text data to search information from a set of documents. Abstract content of 120 data downloaded at www.computer.org. Data grouping consists of three categories: DM (Data Mining), ITS (Intelligent Transport System) and MM (Multimedia). Systems built using naive bayes algorithms to classify abstract journals and feature selection processes using term weighting to give weight to each word. Dimensional reduction techniques to reduce the dimensions of word counts rarely appear in each document based on dimensional reduction test parameters of 10% -90% of 5.344 words. The performance of the classification system is tested by using the Confusion Matrix based on comparative test data and test data. The results showed that the best classification results were obtained during the 75% training data test and 25% test data from the total data. Accuracy rates for categories of DM, ITS and MM were 100%, 100%, 86%. respectively with dimension reduction parameters of 30% and the value of learning rate between 0.1-0.5.

30 CFR 900.2 - Objectives.

Code of Federal Regulations, 2010 CFR

2010-07-01

... texts of State and Federal cooperative agreements for regulation of mining on Federal lands. The... Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE INTRODUCTION § 900.2 Objectives. The objective of...

76 FR 40649 - Indiana Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-11

... at 312 IAC 25-6-30 Surface mining; explosives; general requirements. The full text of the program... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 914... Mining Reclamation and Enforcement, Interior. ACTION: Proposed rule; public comment period on proposed...

Complementing the Numbers: A Text Mining Analysis of College Course Withdrawals

ERIC Educational Resources Information Center

Michalski, Greg V.

2011-01-01

Excessive college course withdrawals are costly to the student and the institution in terms of time to degree completion, available classroom space, and other resources. Although generally well quantified, detailed analysis of the reasons given by students for course withdrawal is less common. To address this, a text mining analysis was performed…

Can abstract screening workload be reduced using text mining? User experiences of the tool Rayyan.

PubMed

Olofsson, Hanna; Brolund, Agneta; Hellberg, Christel; Silverstein, Rebecca; Stenström, Karin; Österberg, Marie; Dagerhamn, Jessica

2017-09-01

One time-consuming aspect of conducting systematic reviews is the task of sifting through abstracts to identify relevant studies. One promising approach for reducing this burden uses text mining technology to identify those abstracts that are potentially most relevant for a project, allowing those abstracts to be screened first. To examine the effectiveness of the text mining functionality of the abstract screening tool Rayyan. User experiences were collected. Rayyan was used to screen abstracts for 6 reviews in 2015. After screening 25%, 50%, and 75% of the abstracts, the screeners logged the relevant references identified. A survey was sent to users. After screening half of the search result with Rayyan, 86% to 99% of the references deemed relevant to the study were identified. Of those studies included in the final reports, 96% to 100% were already identified in the first half of the screening process. Users rated Rayyan 4.5 out of 5. The text mining function in Rayyan successfully helped reviewers identify relevant studies early in the screening process. Copyright © 2017 John Wiley & Sons, Ltd.

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.

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.

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.

76 FR 12849 - Kentucky Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-09

... (underground mining). The text of the Kentucky regulations can be found in the administrative record and online... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 917 [KY-252-FOR; OSM-2009-0011] Kentucky Regulatory Program AGENCY: Office of Surface Mining Reclamation...

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.

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.

A Proposed Data Fusion Architecture for Micro-Zone Analysis and Data Mining

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

Kevin McCarthy; Milos Manic

Data Fusion requires the ability to combine or “fuse” date from multiple data sources. Time Series Analysis is a data mining technique used to predict future values from a data set based upon past values. Unlike other data mining techniques, however, Time Series places special emphasis on periodicity and how seasonal and other time-based factors tend to affect trends over time. One of the difficulties encountered in developing generic time series techniques is the wide variability of the data sets available for analysis. This presents challenges all the way from the data gathering stage to results presentation. This paper presentsmore » an architecture designed and used to facilitate the collection of disparate data sets well suited to Time Series analysis as well as other predictive data mining techniques. Results show this architecture provides a flexible, dynamic framework for the capture and storage of a myriad of dissimilar data sets and can serve as a foundation from which to build a complete data fusion architecture.« less

Application of Three Existing Stope Boundary Optimisation Methods in an Operating Underground Mine

NASA Astrophysics Data System (ADS)

Erdogan, Gamze; Yavuz, Mahmut

2017-12-01

The underground mine planning and design optimisation process have received little attention because of complexity and variability of problems in underground mines. Although a number of optimisation studies and software tools are available and some of them, in special, have been implemented effectively to determine the ultimate-pit limits in an open pit mine, there is still a lack of studies for optimisation of ultimate stope boundaries in underground mines. The proposed approaches for this purpose aim at maximizing the economic profit by selecting the best possible layout under operational, technical and physical constraints. In this paper, the existing three heuristic techniques including Floating Stope Algorithm, Maximum Value Algorithm and Mineable Shape Optimiser (MSO) are examined for optimisation of stope layout in a case study. Each technique is assessed in terms of applicability, algorithm capabilities and limitations considering the underground mine planning challenges. Finally, the results are evaluated and compared.

Evaluation of the environmental contamination at an abandoned mining site using multivariate statistical techniques--the Rodalquilar (Southern Spain) mining district.

PubMed

Bagur, M G; Morales, S; López-Chicano, M

2009-11-15

Unsupervised and supervised pattern recognition techniques such as hierarchical cluster analysis, principal component analysis, factor analysis and linear discriminant analysis have been applied to water samples recollected in Rodalquilar mining district (Southern Spain) in order to identify different sources of environmental pollution caused by the abandoned mining industry. The effect of the mining activity on waters was monitored determining the concentration of eleven elements (Mn, Ba, Co, Cu, Zn, As, Cd, Sb, Hg, Au and Pb) by inductively coupled plasma mass spectrometry (ICP-MS). The Box-Cox transformation has been used to transform the data set in normal form in order to minimize the non-normal distribution of the geochemical data. The environmental impact is affected mainly by the mining activity developed in the zone, the acid drainage and finally by the chemical treatment used for the benefit of gold.

The structural and content aspects of abstracts versus bodies of full text journal articles are different

PubMed Central

2010-01-01

Background An increase in work on the full text of journal articles and the growth of PubMedCentral have the opportunity to create a major paradigm shift in how biomedical text mining is done. However, until now there has been no comprehensive characterization of how the bodies of full text journal articles differ from the abstracts that until now have been the subject of most biomedical text mining research. Results We examined the structural and linguistic aspects of abstracts and bodies of full text articles, the performance of text mining tools on both, and the distribution of a variety of semantic classes of named entities between them. We found marked structural differences, with longer sentences in the article bodies and much heavier use of parenthesized material in the bodies than in the abstracts. We found content differences with respect to linguistic features. Three out of four of the linguistic features that we examined were statistically significantly differently distributed between the two genres. We also found content differences with respect to the distribution of semantic features. There were significantly different densities per thousand words for three out of four semantic classes, and clear differences in the extent to which they appeared in the two genres. With respect to the performance of text mining tools, we found that a mutation finder performed equally well in both genres, but that a wide variety of gene mention systems performed much worse on article bodies than they did on abstracts. POS tagging was also more accurate in abstracts than in article bodies. Conclusions Aspects of structure and content differ markedly between article abstracts and article bodies. A number of these differences may pose problems as the text mining field moves more into the area of processing full-text articles. However, these differences also present a number of opportunities for the extraction of data types, particularly that found in parenthesized text, that is present in article bodies but not in article abstracts. PMID:20920264

Exploiting Social Media Sensor Networks through Novel Data Fusion Techniques

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

Kouri, Tina

Unprecedented amounts of data are continuously being generated by sensors (“hard” data) and by humans (“soft” data), and this data needs to be exploited to its full potential. The first step in exploiting this data is determine how the hard and soft data are related to each other. In this project we fuse hard and soft data, using the attributes of each (e.g., time and space), to gain more information about interesting events. Next, we attempt to use social networking textual data to predict the present (i.e., predict that an interesting event is occurring and details about the event) usingmore » data mining, machine learning, natural language processing, and text analysis techniques.« less

Introduction to the mining of clinical data.

PubMed

Harrison, James H

2008-03-01

The increasing volume of medical data online, including laboratory data, represents a substantial resource that can provide a foundation for improved understanding of disease presentation, response to therapy, and health care delivery processes. Data mining supports these goals by providing a set of techniques designed to discover similarities and relationships between data elements in large data sets. Currently, medical data have several characteristics that increase the difficulty of applying these techniques, although there have been notable medical data mining successes. Future developments in integrated medical data repositories, standardized data representation, and guidelines for the appropriate research use of medical data will decrease the barriers to mining projects.

Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques

PubMed Central

Sanmiquel, Lluís; Bascompta, Marc; Rossell, Josep M.; Anticoi, Hernán Francisco; Guash, Eduard

2018-01-01

An analysis of occupational accidents in the mining sector was conducted using the data from the Spanish Ministry of Employment and Social Safety between 2005 and 2015, and data-mining techniques were applied. Data was processed with the software Weka. Two scenarios were chosen from the accidents database: surface and underground mining. The most important variables involved in occupational accidents and their association rules were determined. These rules are composed of several predictor variables that cause accidents, defining its characteristics and context. This study exposes the 20 most important association rules in the sector—either surface or underground mining—based on the statistical confidence levels of each rule as obtained by Weka. The outcomes display the most typical immediate causes, along with the percentage of accidents with a basis in each association rule. The most important immediate cause is body movement with physical effort or overexertion, and the type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident are different between the two scenarios. Data-mining techniques were chosen as a useful tool to find out the root cause of the accidents. PMID:29518921

Deriving preference order of post-mining land-uses through MLSA framework: application of an outranking technique

NASA Astrophysics Data System (ADS)

Soltanmohammadi, Hossein; Osanloo, Morteza; Aghajani Bazzazi, Abbas

2009-08-01

This study intends to take advantage of a previously developed framework for mined land suitability analysis (MLSA) consisted of economical, social, technical and mine site factors to achieve a partial and also a complete pre-order of feasible post-mining land-uses. Analysis by an outranking multi-attribute decision-making (MADM) technique, called PROMETHEE (preference ranking organization method for enrichment evaluation), was taken into consideration because of its clear advantages on the field of MLSA as compared with MADM ranking techniques. Application of the proposed approach on a mined land can be completed through some successive steps. First, performance of the MLSA attributes is scored locally by each individual decision maker (DM). Then the assigned performance scores are normalized and the deviation amplitudes of non-dominated alternatives are calculated. Weights of the attributes are calculated by another MADM technique namely, analytical hierarchy process (AHP) in a separate procedure. Using the Gaussian preference function beside the weights, the preference indexes of the land-use alternatives are obtained. Calculation of the outgoing and entering flows of the alternatives and one by one comparison of these values will lead to partial pre-order of them and calculation of the net flows, will lead to a ranked preference for each land-use. At the final step, utilizing the PROMETHEE group decision support system which incorporates judgments of all the DMs, a consensual ranking can be derived. In this paper, preference order of post-mining land-uses for a hypothetical mined land has been derived according to judgments of one DM to reveal applicability of the proposed approach.

Application of remote-sensing techniques to hydrologic studies in selected coal-mine areas of southeastern Kansas

USGS Publications Warehouse

Kenny, J.F.; McCauley, J.R.

1983-01-01

Disturbances resulting from intensive coal mining in the Cherry Creek basin of southeastern Kansas were investigated using color and color-infrared aerial photography in conjunction with water-quality data from simultaneously acquired samples. Imagery was used to identify the type and extent of vegetative cover on strip-mined lands and the extent and success of reclamation practices. Drainage patterns, point sources of acid mine drainage, and recharge areas for underground mines were located for onsite inspection. Comparison of these interpretations with water-quality data illustrated differences between the eastern and western parts of the Cherry Creek basin. Contamination in the eastern part is due largely to circulation of water from unreclaimed strip mines and collapse features through the network of underground mines and subsequent discharge of acidic drainage through seeps. Contamination in the western part is primarily caused by runoff and seepage from strip-mined lands in which surfaces have frequently been graded and limed but are generally devoid of mature stands of soil-anchoring vegetation. The successful use of aerial photography in the study of Cherry Creek basin indicates the potential of using remote-sensing techniques in studies of other coal-mined regions. (USGS)

Implementation of a Flexible Tool for Automated Literature-Mining and Knowledgebase Development (DevToxMine)

EPA Science Inventory

Deriving novel relationships from the scientific literature is an important adjunct to datamining activities for complex datasets in genomics and high-throughput screening activities. Automated text-mining algorithms can be used to extract relevant content from the literature and...

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)

Mining the SDSS SkyServer SQL queries log

NASA Astrophysics Data System (ADS)

Hirota, Vitor M.; Santos, Rafael; Raddick, Jordan; Thakar, Ani

2016-05-01

SkyServer, the Internet portal for the Sloan Digital Sky Survey (SDSS) astronomic catalog, provides a set of tools that allows data access for astronomers and scientific education. One of SkyServer data access interfaces allows users to enter ad-hoc SQL statements to query the catalog. SkyServer also presents some template queries that can be used as basis for more complex queries. This interface has logged over 330 million queries submitted since 2001. It is expected that analysis of this data can be used to investigate usage patterns, identify potential new classes of queries, find similar queries, etc. and to shed some light on how users interact with the Sloan Digital Sky Survey data and how scientists have adopted the new paradigm of e-Science, which could in turn lead to enhancements on the user interfaces and experience in general. In this paper we review some approaches to SQL query mining, apply the traditional techniques used in the literature and present lessons learned, namely, that the general text mining approach for feature extraction and clustering does not seem to be adequate for this type of data, and, most importantly, we find that this type of analysis can result in very different queries being clustered together.

30 CFR 282.28 - Environmental protection measures.

Code of Federal Regulations, 2010 CFR

2010-07-01

... recent research or improved monitoring techniques. (5) When prototype test mining is proposed, the lessee...) The sampling techniques and procedures to be used to acquire the needed data and information; (ii) The... evaluation of the approved Delineation, Testing, or Mining Plan. The Director's review of the air quality...

Analyzing Teaching Performance of Instructors Using Data Mining Techniques

ERIC Educational Resources Information Center

Mardikyan, Sona; Badur, Bertain

2011-01-01

Student evaluations to measure the teaching effectiveness of instructor's are very frequently applied in higher education for many years. This study investigates the factors associated with the assessment of instructors teaching performance using two different data mining techniques; stepwise regression and decision trees. The data collected…

Monitoring and inversion on land subsidence over mining area with InSAR technique

USGS Publications Warehouse

Wang, Y.; Zhang, Q.; Zhao, C.; Lu, Z.; Ding, X.

2011-01-01

The Wulanmulun town, located in Inner Mongolia, is one of the main mining areas of Shendong Company such as Shangwan coal mine and Bulianta coal mine, which has been suffering serious mine collapse with the underground mine withdrawal. We use ALOS/PALSAR data to extract land deformation under these regions, in which Small Baseline Subsets (SBAS) method was applied. Then we compared InSAR results with the underground mining activities, and found high correlations between them. Lastly we applied Distributed Dislocation (Okada) model to invert the mine collapse mechanism. ?? 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

15 CFR 970.600 - General.

Code of Federal Regulations, 2012 CFR

2012-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 970.600 - General.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 970.600 - General.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2013 CFR

2013-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2012 CFR

2012-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2011 CFR

2011-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 971.500 - General.

Code of Federal Regulations, 2014 CFR

2014-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Resource Development § 971.500 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 970.600 - General.

Code of Federal Regulations, 2010 CFR

2010-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

15 CFR 970.600 - General.

Code of Federal Regulations, 2011 CFR

2011-01-01

... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Resource Development Concepts § 970.600 General. Several provisions in the Act relate to appropriate mining techniques or mining efficiency. These...

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

VRLane: a desktop virtual safety management program for underground coal mine

NASA Astrophysics Data System (ADS)

Li, Mei; Chen, Jingzhu; Xiong, Wei; Zhang, Pengpeng; Wu, Daozheng

2008-10-01

VR technologies, which generate immersive, interactive, and three-dimensional (3D) environments, are seldom applied to coal mine safety work management. In this paper, a new method that combined the VR technologies with underground mine safety management system was explored. A desktop virtual safety management program for underground coal mine, called VRLane, was developed. The paper mainly concerned about the current research advance in VR, system design, key techniques and system application. Two important techniques were introduced in the paper. Firstly, an algorithm was designed and implemented, with which the 3D laneway models and equipment models can be built on the basis of the latest mine 2D drawings automatically, whereas common VR programs established 3D environment by using 3DS Max or the other 3D modeling software packages with which laneway models were built manually and laboriously. Secondly, VRLane realized system integration with underground industrial automation. VRLane not only described a realistic 3D laneway environment, but also described the status of the coal mining, with functions of displaying the run states and related parameters of equipment, per-alarming the abnormal mining events, and animating mine cars, mine workers, or long-wall shearers. The system, with advantages of cheap, dynamic, easy to maintenance, provided a useful tool for safety production management in coal mine.

Application of LANDSAT data to monitor land reclamation progress in Belmont County, Ohio

NASA Technical Reports Server (NTRS)

Bloemer, H. H. L.; Brumfield, J. O.; Campbell, W. J.; Witt, R. G.; Bly, B. G.

1981-01-01

Strip and contour mining techniques are reviewed as well as some studies conducted to determine the applicability of LANDSAT and associated digital image processing techniques to the surficial problems associated with mining operations. A nontraditional unsupervised classification approach to multispectral data is considered which renders increased classification separability in land cover analysis of surface mined areas. The approach also reduces the dimensionality of the data and requires only minimal analytical skills in digital data processing.

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/).

Generative Topic Modeling in Image Data Mining and Bioinformatics Studies

ERIC Educational Resources Information Center

Chen, Xin

2012-01-01

Probabilistic topic models have been developed for applications in various domains such as text mining, information retrieval and computer vision and bioinformatics domain. In this thesis, we focus on developing novel probabilistic topic models for image mining and bioinformatics studies. Specifically, a probabilistic topic-connection (PTC) model…

78 FR 40496 - Notice of availability of the Final Environmental Impact Statement for the Proposed Hollister...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-05

... silver mining operation. Most of the infrastructure to support a mining operation was authorized and.... The Proposed Action consists of underground mining, constructing a new production shaft, improving.... Public comments resulted in the addition of clarifying text, but did not significantly change the...

Applying Web Usage Mining for Personalizing Hyperlinks in Web-Based Adaptive Educational Systems

ERIC Educational Resources Information Center

Romero, Cristobal; Ventura, Sebastian; Zafra, Amelia; de Bra, Paul

2009-01-01

Nowadays, the application of Web mining techniques in e-learning and Web-based adaptive educational systems is increasing exponentially. In this paper, we propose an advanced architecture for a personalization system to facilitate Web mining. A specific Web mining tool is developed and a recommender engine is integrated into the AHA! system in…

COMPARISON OF DATA FROM SYNTHETIC LEACHATE AND DIRECT SAMPLING OF ACID DRAINAGE FROM MINE WASTES: IMPLICATIONS FOR MERCURY TRANSPORT AND WASTE MANAGEMENT

EPA Science Inventory

The Sulphur Bank Mercury Mine (SBMM) in Lake County, California operated from the 1860s through the 1950's. Mining for sulfur started with surface operations and progressed to shaft, then open pit techniques to obtain mercury. Mining has resulted in deposition of approximately ...

Examining Online Learning Patterns with Data Mining Techniques in Peer-Moderated and Teacher-Moderated Courses

ERIC Educational Resources Information Center

Hung, Jui-Long; Crooks, Steven M.

2009-01-01

The student learning process is important in online learning environments. If instructors can "observe" online learning behaviors, they can provide adaptive feedback, adjust instructional strategies, and assist students in establishing patterns of successful learning activities. This study used data mining techniques to examine and…

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.

Site investigation report mine research project GUE 70-14.10, Guernsey, Ohio.

DOT National Transportation Integrated Search

2003-06-01

Geophysical investigative techniques can be a valuable supplement to standard subsurface investigations for the : evaluation of abandoned underground coal mine workings and their potential impacts at the ground surface. The GUE : 70 - 14.10 Mine Rese...

Analysis of Hospital Processes with Process Mining Techniques.

PubMed

Orellana García, Arturo; Pérez Alfonso, Damián; Larrea Armenteros, Osvaldo Ulises

2015-01-01

Process mining allows for discovery, monitoring, and improving processes identified in information systems from their event logs. In hospital environments, process analysis has been a crucial factor for cost reduction, control and proper use of resources, better patient care, and achieving service excellence. This paper presents a new component for event logs generation in the Hospital Information System or HIS, developed at University of Informatics Sciences. The event logs obtained are used for analysis of hospital processes with process mining techniques. The proposed solution intends to achieve the generation of event logs in the system with high quality. The performed analyses allowed for redefining functions in the system and proposed proper flow of information. The study exposed the need to incorporate process mining techniques in hospital systems to analyze the processes execution. Moreover, we illustrate its application for making clinical and administrative decisions for the management of hospital activities.

Text mining to decipher free-response consumer complaints: insights from the NHTSA vehicle owner's complaint database.

PubMed

Ghazizadeh, Mahtab; McDonald, Anthony D; Lee, John D

2014-09-01

This study applies text mining to extract clusters of vehicle problems and associated trends from free-response data in the National Highway Traffic Safety Administration's vehicle owner's complaint database. As the automotive industry adopts new technologies, it is important to systematically assess the effect of these changes on traffic safety. Driving simulators, naturalistic driving data, and crash databases all contribute to a better understanding of how drivers respond to changing vehicle technology, but other approaches, such as automated analysis of incident reports, are needed. Free-response data from incidents representing two severity levels (fatal incidents and incidents involving injury) were analyzed using a text mining approach: latent semantic analysis (LSA). LSA and hierarchical clustering identified clusters of complaints for each severity level, which were compared and analyzed across time. Cluster analysis identified eight clusters of fatal incidents and six clusters of incidents involving injury. Comparisons showed that although the airbag clusters across the two severity levels have the same most frequent terms, the circumstances around the incidents differ. The time trends show clear increases in complaints surrounding the Ford/Firestone tire recall and the Toyota unintended acceleration recall. Increases in complaints may be partially driven by these recall announcements and the associated media attention. Text mining can reveal useful information from free-response databases that would otherwise be prohibitively time-consuming and difficult to summarize manually. Text mining can extend human analysis capabilities for large free-response databases to support earlier detection of problems and more timely safety interventions.

Towards Phenotyping of Clinical Trial Eligibility Criteria.

PubMed

Löbe, Matthias; Stäubert, Sebastian; Goldberg, Colleen; Haffner, Ivonne; Winter, Alfred

2018-01-01

Medical plaintext documents contain important facts about patients, but they are rarely available for structured queries. The provision of structured information from natural language texts in addition to the existing structured data can significantly speed up the search for fulfilled inclusion criteria and thus improve the recruitment rate. This work is aimed at supporting clinical trial recruitment with text mining techniques to identify suitable subjects in hospitals. Based on the inclusion/exclusion criteria of 5 sample studies and a text corpus consisting of 212 doctor's letters and medical follow-up documentation from a university cancer center, a prototype was developed and technically evaluated using NLP procedures (UIMA) for the extraction of facts from medical free texts. It was found that although the extracted entities are not always correct (precision between 23% and 96%), they provide a decisive indication as to which patient file should be read preferentially. The prototype presented here demonstrates the technical feasibility. In order to find available, lucrative phenotypes, an in-depth evaluation is required.

The Determination of Children's Knowledge of Global Lunar Patterns from Online Essays Using Text Mining Analysis

ERIC Educational Resources Information Center

Cheon, Jongpil; Lee, Sangno; Smith, Walter; Song, Jaeki; Kim, Yongjin

2013-01-01

The purpose of this study was to use text mining analysis of early adolescents' online essays to determine their knowledge of global lunar patterns. Australian and American students in grades five to seven wrote about global lunar patterns they had discovered by sharing observations with each other via the Internet. These essays were analyzed for…

Impact of Text-Mining and Imitating Strategies on Lexical Richness, Lexical Diversity and General Success in Second Language Writing

ERIC Educational Resources Information Center

Çepni, Sevcan Bayraktar; Demirel, Elif Tokdemir

2016-01-01

This study aimed to find out the impact of "text mining and imitating" strategies on lexical richness, lexical diversity and general success of students in their compositions in second language writing. The participants were 98 students studying their first year in Karadeniz Technical University in English Language and Literature…

Science and Technology Text Mining: Text Mining of the Journal Cortex

DTIC Science & Technology

2004-01-01

Amnesia Retrograde Amnesia GENERAL Semantic Memory Episodic Memory Working Memory TEST Serial Position Curve...in Cortex can be reasonably divided into four categories (papers in each category in parenthesis): Semantic Memory (151); Handedness (145); Amnesia ... Semantic Memory (151) is divided into Verbal/ Numerical (76) and Visual/ Spatial (75). Amnesia (119) is divided into Amnesia Symptoms (50) and

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.

Data Mining Techniques for Customer Relationship Management

NASA Astrophysics Data System (ADS)

Guo, Feng; Qin, Huilin

2017-10-01

Data mining have made customer relationship management (CRM) a new area where firms can gain a competitive advantage, and play a key role in the firms’ management decision. In this paper, we first analyze the value and application fields of data mining techniques for CRM, and further explore how data mining applied to Customer churn analysis. A new business culture is developing today. The conventional production centered and sales purposed market strategy is gradually shifting to customer centered and service purposed. Customers’ value orientation is increasingly affecting the firms’. And customer resource has become one of the most important strategic resources. Therefore, understanding customers’ needs and discriminating the most contributed customers has become the driving force of most modern business.

30 CFR 282.23 - Testing Plan.

Code of Federal Regulations, 2011 CFR

2011-07-01

... Resources BUREAU OF OCEAN ENERGY MANAGEMENT, REGULATION, AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR... lessee needs more information to develop a detailed Mining Plan than is obtainable under an approved... techniques or technology or mining equipment, or to determine environmental effects by a pilot test mining...

Utilization of volume correlation filters for underwater mine identification in LIDAR imagery

NASA Astrophysics Data System (ADS)

Walls, Bradley

2008-04-01

Underwater mine identification persists as a critical technology pursued aggressively by the Navy for fleet protection. As such, new and improved techniques must continue to be developed in order to provide measurable increases in mine identification performance and noticeable reductions in false alarm rates. In this paper we show how recent advances in the Volume Correlation Filter (VCF) developed for ground based LIDAR systems can be adapted to identify targets in underwater LIDAR imagery. Current automated target recognition (ATR) algorithms for underwater mine identification employ spatial based three-dimensional (3D) shape fitting of models to LIDAR data to identify common mine shapes consisting of the box, cylinder, hemisphere, truncated cone, wedge, and annulus. VCFs provide a promising alternative to these spatial techniques by correlating 3D models against the 3D rendered LIDAR data.

Intelligent and integrated techniques for coalbed methane (CBM) recovery and reduction of greenhouse gas emission.

PubMed

Qianting, Hu; Yunpei, Liang; Han, Wang; Quanle, Zou; Haitao, Sun

2017-07-01

Coalbed methane (CBM) recovery is a crucial approach to realize the exploitation of a clean energy and the reduction of the greenhouse gas emission. In the past 10 years, remarkable achievements on CBM recovery have been obtained in China. However, some key difficulties still exist such as long borehole drilling in complicated geological condition, and poor gas drainage effect due to low permeability. In this study, intelligent and integrated techniques for CBM recovery are introduced. These integrated techniques mainly include underground CBM recovery techniques and ground well CBM recovery techniques. The underground CBM recovery techniques consist of the borehole formation technique, gas concentration improvement technique, and permeability enhancement technique. According to the division of mining-induced disturbance area, the ground well arrangement area and well structure type in mining-induced disturbance developing area and mining-induced disturbance stable area are optimized to significantly improve the ground well CBM recovery. Besides, automatic devices such as drilling pipe installation device are also developed to achieve remote control of data recording, which makes the integrated techniques intelligent. These techniques can provide key solutions to some long-term difficulties in CBM recovery.

Assessment of hospital processes using a process mining technique: Outpatient process analysis at a tertiary hospital.

PubMed

Yoo, Sooyoung; Cho, Minsu; Kim, Eunhye; Kim, Seok; Sim, Yerim; Yoo, Donghyun; Hwang, Hee; Song, Minseok

2016-04-01

Many hospitals are increasing their efforts to improve processes because processes play an important role in enhancing work efficiency and reducing costs. However, to date, a quantitative tool has not been available to examine the before and after effects of processes and environmental changes, other than the use of indirect indicators, such as mortality rate and readmission rate. This study used process mining technology to analyze process changes based on changes in the hospital environment, such as the construction of a new building, and to measure the effects of environmental changes in terms of consultation wait time, time spent per task, and outpatient care processes. Using process mining technology, electronic health record (EHR) log data of outpatient care before and after constructing a new building were analyzed, and the effectiveness of the technology in terms of the process was evaluated. Using the process mining technique, we found that the total time spent in outpatient care did not increase significantly compared to that before the construction of a new building, considering that the number of outpatients increased, and the consultation wait time decreased. These results suggest that the operation of the outpatient clinic was effective after changes were implemented in the hospital environment. We further identified improvements in processes using the process mining technique, thereby demonstrating the usefulness of this technique for analyzing complex hospital processes at a low cost. This study confirmed the effectiveness of process mining technology at an actual hospital site. In future studies, the use of process mining technology will be expanded by applying this approach to a larger variety of process change situations. Copyright © 2016. Published by Elsevier Ireland Ltd.

Biomedical hypothesis generation by text mining and gene prioritization.

PubMed

Petric, Ingrid; Ligeti, Balazs; Gyorffy, Balazs; Pongor, Sandor

2014-01-01

Text mining methods can facilitate the generation of biomedical hypotheses by suggesting novel associations between diseases and genes. Previously, we developed a rare-term model called RaJoLink (Petric et al, J. Biomed. Inform. 42(2): 219-227, 2009) in which hypotheses are formulated on the basis of terms rarely associated with a target domain. Since many current medical hypotheses are formulated in terms of molecular entities and molecular mechanisms, here we extend the methodology to proteins and genes, using a standardized vocabulary as well as a gene/protein network model. The proposed enhanced RaJoLink rare-term model combines text mining and gene prioritization approaches. Its utility is illustrated by finding known as well as potential gene-disease associations in ovarian cancer using MEDLINE abstracts and the STRING database.

The Functional Genomics Network in the evolution of biological text mining over the past decade.

PubMed

Blaschke, Christian; Valencia, Alfonso

2013-03-25

Different programs of The European Science Foundation (ESF) have contributed significantly to connect researchers in Europe and beyond through several initiatives. This support was particularly relevant for the development of the areas related with extracting information from papers (text-mining) because it supported the field in its early phases long before it was recognized by the community. We review the historical development of text mining research and how it was introduced in bioinformatics. Specific applications in (functional) genomics are described like it's integration in genome annotation pipelines and the support to the analysis of high-throughput genomics experimental data, and we highlight the activities of evaluation of methods and benchmarking for which the ESF programme support was instrumental. Copyright © 2013 Elsevier B.V. All rights reserved.

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

SEASONAL VARIATIONS OF DISSOLVED MERCURY CONCENTRATIONS AT THE SULPHUR BANK MERCURY MINE, CLEAR LAKE, CALIFORNIA: IMPLICATIONS FOR MINE DRAINAGE MONITORING

EPA Science Inventory

The Sulphur Bank Mercury Mine in Lake County, California (SBMM) was operated from the 1860s through the 1950s. Mining for sulfur started with surface operations and then progressed to shaft and later open pit techniques to obtain mercury. SBMM is located adjacent to the shore o...

Data Mining in Social Media

NASA Astrophysics Data System (ADS)

Barbier, Geoffrey; Liu, Huan

The rise of online social media is providing a wealth of social network data. Data mining techniques provide researchers and practitioners the tools needed to analyze large, complex, and frequently changing social media data. This chapter introduces the basics of data mining, reviews social media, discusses how to mine social media data, and highlights some illustrative examples with an emphasis on social networking sites and blogs.

Supporting the annotation of chronic obstructive pulmonary disease (COPD) phenotypes with text mining workflows.

PubMed

Fu, Xiao; Batista-Navarro, Riza; Rak, Rafal; Ananiadou, Sophia

2015-01-01

Chronic obstructive pulmonary disease (COPD) is a life-threatening lung disorder whose recent prevalence has led to an increasing burden on public healthcare. Phenotypic information in electronic clinical records is essential in providing suitable personalised treatment to patients with COPD. However, as phenotypes are often "hidden" within free text in clinical records, clinicians could benefit from text mining systems that facilitate their prompt recognition. This paper reports on a semi-automatic methodology for producing a corpus that can ultimately support the development of text mining tools that, in turn, will expedite the process of identifying groups of COPD patients. A corpus of 30 full-text papers was formed based on selection criteria informed by the expertise of COPD specialists. We developed an annotation scheme that is aimed at producing fine-grained, expressive and computable COPD annotations without burdening our curators with a highly complicated task. This was implemented in the Argo platform by means of a semi-automatic annotation workflow that integrates several text mining tools, including a graphical user interface for marking up documents. When evaluated using gold standard (i.e., manually validated) annotations, the semi-automatic workflow was shown to obtain a micro-averaged F-score of 45.70% (with relaxed matching). Utilising the gold standard data to train new concept recognisers, we demonstrated that our corpus, although still a work in progress, can foster the development of significantly better performing COPD phenotype extractors. We describe in this work the means by which we aim to eventually support the process of COPD phenotype curation, i.e., by the application of various text mining tools integrated into an annotation workflow. Although the corpus being described is still under development, our results thus far are encouraging and show great potential in stimulating the development of further automatic COPD phenotype extractors.

78 FR 64397 - Mississippi Regulatory Program

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-29

... text of the program amendment available at www.regulations.gov . A. Mississippi Surface Coal Mining... DEPARTMENT OF THE INTERIOR Office of Surface Mining Reclamation and Enforcement 30 CFR Part 924...; S2D2SSS08011000SX066A00033F13XS501520] Mississippi Regulatory Program AGENCY: Office of Surface Mining Reclamation and Enforcement...

Recommendation in Higher Education Using Data Mining Techniques

ERIC Educational Resources Information Center

Vialardi, Cesar; Bravo, Javier; Shafti, Leila; Ortigosa, Alvaro

2009-01-01

One of the main problems faced by university students is to take the right decision in relation to their academic itinerary based on available information (for example courses, schedules, sections, classrooms and professors). In this context, this work proposes the use of a recommendation system based on data mining techniques to help students to…

Physics Mining of Multi-Source Data Sets

NASA Technical Reports Server (NTRS)

Helly, John; Karimabadi, Homa; Sipes, Tamara

2012-01-01

Powerful new parallel data mining algorithms can produce diagnostic and prognostic numerical models and analyses from observational data. These techniques yield higher-resolution measures than ever before of environmental parameters by fusing synoptic imagery and time-series measurements. These techniques are general and relevant to observational data, including raster, vector, and scalar, and can be applied in all Earth- and environmental science domains. Because they can be highly automated and are parallel, they scale to large spatial domains and are well suited to change and gap detection. This makes it possible to analyze spatial and temporal gaps in information, and facilitates within-mission replanning to optimize the allocation of observational resources. The basis of the innovation is the extension of a recently developed set of algorithms packaged into MineTool to multi-variate time-series data. MineTool is unique in that it automates the various steps of the data mining process, thus making it amenable to autonomous analysis of large data sets. Unlike techniques such as Artificial Neural Nets, which yield a blackbox solution, MineTool's outcome is always an analytical model in parametric form that expresses the output in terms of the input variables. This has the advantage that the derived equation can then be used to gain insight into the physical relevance and relative importance of the parameters and coefficients in the model. This is referred to as physics-mining of data. The capabilities of MineTool are extended to include both supervised and unsupervised algorithms, handle multi-type data sets, and parallelize it.

Uranium aqueous speciation in the vicinity of the former uranium mining sites using the diffusive gradients in thin films and ultrafiltration techniques.

PubMed

Drozdzak, Jagoda; Leermakers, Martine; Gao, Yue; Elskens, Marc; Phrommavanh, Vannapha; Descostes, Michael

2016-03-24

The performance of the Diffusive Gradients in Thin films (DGT) technique with Chelex(®)-100, Metsorb™ and Diphonix(®) as binding phases was evaluated in the vicinity of the former uranium mining sites of Chardon and L'Ecarpière (Loire-Atlantique department in western France). This is the first time that the DGT technique with three different binding agents was employed for the aqueous U determination in the context of uranium mining environments. The fractionation and speciation of uranium were investigated using a multi-methodological approach using filtration (0.45 μm, 0.2 μm), ultrafiltration (500 kDa, 100 kDa and 10 kDa) coupled to geochemical speciation modelling (PhreeQC) and the DGT technique. The ultrafiltration data showed that at each sampling point uranium was present mostly in the 10 kDa truly dissolved fraction and the geochemical modelling speciation calculations indicated that U speciation was markedly predominated by CaUO2(CO3)3(2-). In natural waters, no significant difference was observed in terms of U uptake between Chelex(®)-100 and Metsorb™, while similar or inferior U uptake was observed on Diphonix(®) resin. In turn, at mining influenced sampling spots, the U accumulation on DGT-Diphonix(®) was higher than on DGT-Chelex(®)-100 and DGT-Metsorb™, probably because their performance was disturbed by the extreme composition of the mining waters. The use of Diphonix(®) resin leads to a significant advance in the application and development of the DGT technique for determination of U in mining influenced environments. This investigation demonstrated that such multi-technique approach provides a better picture of U speciation and enables to assess more accurately the potentially bioavailable U pool. Copyright © 2016 Elsevier B.V. All rights reserved.

Context-Aware Adaptive Hybrid Semantic Relatedness in Biomedical Science

NASA Astrophysics Data System (ADS)

Emadzadeh, Ehsan

Text mining of biomedical literature and clinical notes is a very active field of research in biomedical science. Semantic analysis is one of the core modules for different Natural Language Processing (NLP) solutions. Methods for calculating semantic relatedness of two concepts can be very useful in solutions solving different problems such as relationship extraction, ontology creation and question / answering [1--6]. Several techniques exist in calculating semantic relatedness of two concepts. These techniques utilize different knowledge sources and corpora. So far, researchers attempted to find the best hybrid method for each domain by combining semantic relatedness techniques and data sources manually. In this work, attempts were made to eliminate the needs for manually combining semantic relatedness methods targeting any new contexts or resources through proposing an automated method, which attempted to find the best combination of semantic relatedness techniques and resources to achieve the best semantic relatedness score in every context. This may help the research community find the best hybrid method for each context considering the available algorithms and resources.

Process Mining Online Assessment Data

ERIC Educational Resources Information Center

Pechenizkiy, Mykola; Trcka, Nikola; Vasilyeva, Ekaterina; van der Aalst, Wil; De Bra, Paul

2009-01-01

Traditional data mining techniques have been extensively applied to find interesting patterns, build descriptive and predictive models from large volumes of data accumulated through the use of different information systems. The results of data mining can be used for getting a better understanding of the underlying educational processes, for…

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

Unsupervised discovery of information structure in biomedical documents.

PubMed

Kiela, Douwe; Guo, Yufan; Stenius, Ulla; Korhonen, Anna

2015-04-01

Information structure (IS) analysis is a text mining technique, which classifies text in biomedical articles into categories that capture different types of information, such as objectives, methods, results and conclusions of research. It is a highly useful technique that can support a range of Biomedical Text Mining tasks and can help readers of biomedical literature find information of interest faster, accelerating the highly time-consuming process of literature review. Several approaches to IS analysis have been presented in the past, with promising results in real-world biomedical tasks. However, all existing approaches, even weakly supervised ones, require several hundreds of hand-annotated training sentences specific to the domain in question. Because biomedicine is subject to considerable domain variation, such annotations are expensive to obtain. This makes the application of IS analysis across biomedical domains difficult. In this article, we investigate an unsupervised approach to IS analysis and evaluate the performance of several unsupervised methods on a large corpus of biomedical abstracts collected from PubMed. Our best unsupervised algorithm (multilevel-weighted graph clustering algorithm) performs very well on the task, obtaining over 0.70 F scores for most IS categories when applied to well-known IS schemes. This level of performance is close to that of lightly supervised IS methods and has proven sufficient to aid a range of practical tasks. Thus, using an unsupervised approach, IS could be applied to support a wide range of tasks across sub-domains of biomedicine. We also demonstrate that unsupervised learning brings novel insights into IS of biomedical literature and discovers information categories that are not present in any of the existing IS schemes. The annotated corpus and software are available at http://www.cl.cam.ac.uk/∼dk427/bio14info.html. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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.

Contribution to understanding the post-mining landscape - Application of airborn LiDAR and historical maps at the example from Silesian Upland (Poland)

NASA Astrophysics Data System (ADS)

Gawior, D.; Rutkiewicz, P.; Malik, I.; Wistuba, M.

2017-11-01

LiDAR data provide new insights into the historical development of mining industry recorded in the topography and landscape. In the study on the lead ore mining in the 13th-17th century we identified remnants of mining activity in relief that are normally obscured by dense vegetation. The industry in Tarnowice Plateau was based on exploitation of galena from the bedrock. New technologies, including DEM from airborne LiDAR provide show that present landscape and relief of post-mining area under study developed during several, subsequent phases of exploitation when different techniques of exploitation were used and probably different types of ores were exploited. Study conducted on the Tarnowice Plateau proved that combining GIS visualization techniques with historical maps, among all geological maps, is a promising approach in reconstructing development of anthropogenic relief and landscape..

Exploring patterns of epigenetic information with data mining techniques.

PubMed

Aguiar-Pulido, Vanessa; Seoane, José A; Gestal, Marcos; Dorado, Julián

2013-01-01

Data mining, a part of the Knowledge Discovery in Databases process (KDD), is the process of extracting patterns from large data sets by combining methods from statistics and artificial intelligence with database management. Analyses of epigenetic data have evolved towards genome-wide and high-throughput approaches, thus generating great amounts of data for which data mining is essential. Part of these data may contain patterns of epigenetic information which are mitotically and/or meiotically heritable determining gene expression and cellular differentiation, as well as cellular fate. Epigenetic lesions and genetic mutations are acquired by individuals during their life and accumulate with ageing. Both defects, either together or individually, can result in losing control over cell growth and, thus, causing cancer development. Data mining techniques could be then used to extract the previous patterns. This work reviews some of the most important applications of data mining to epigenetics.

Application of Ferulic Acid for Alzheimer’s Disease: Combination of Text Mining and Experimental Validation

PubMed Central

Meng, Guilin; Meng, Xiulin; Ma, Xiaoye; Zhang, Gengping; Hu, Xiaolin; Jin, Aiping; Liu, Xueyuan

2018-01-01

Alzheimer’s disease (AD) is an increasing concern in human health. Despite significant research, highly effective drugs to treat AD are lacking. The present study describes the text mining process to identify drug candidates from a traditional Chinese medicine (TCM) database, along with associated protein target mechanisms. We carried out text mining to identify literatures that referenced both AD and TCM and focused on identifying compounds and protein targets of interest. After targeting one potential TCM candidate, corresponding protein-protein interaction (PPI) networks were assembled in STRING to decipher the most possible mechanism of action. This was followed by validation using Western blot and co-immunoprecipitation in an AD cell model. The text mining strategy using a vast amount of AD-related literature and the TCM database identified curcumin, whose major component was ferulic acid (FA). This was used as a key candidate compound for further study. Using the top calculated interaction score in STRING, BACE1 and MMP2 were implicated in the activity of FA in AD. Exposure of SHSY5Y-APP cells to FA resulted in the decrease in expression levels of BACE-1 and APP, while the expression of MMP-2 and MMP-9 increased in a dose-dependent manner. This suggests that FA induced BACE1 and MMP2 pathways maybe novel potential mechanisms involved in AD. The text mining of literature and TCM database related to AD suggested FA as a promising TCM ingredient for the treatment of AD. Potential mechanisms interconnected and integrated with Aβ aggregation inhibition and extracellular matrix remodeling underlying the activity of FA were identified using in vitro studies. PMID:29896095

Application of Ferulic Acid for Alzheimer's Disease: Combination of Text Mining and Experimental Validation.

PubMed

Meng, Guilin; Meng, Xiulin; Ma, Xiaoye; Zhang, Gengping; Hu, Xiaolin; Jin, Aiping; Zhao, Yanxin; Liu, Xueyuan

2018-01-01

Alzheimer's disease (AD) is an increasing concern in human health. Despite significant research, highly effective drugs to treat AD are lacking. The present study describes the text mining process to identify drug candidates from a traditional Chinese medicine (TCM) database, along with associated protein target mechanisms. We carried out text mining to identify literatures that referenced both AD and TCM and focused on identifying compounds and protein targets of interest. After targeting one potential TCM candidate, corresponding protein-protein interaction (PPI) networks were assembled in STRING to decipher the most possible mechanism of action. This was followed by validation using Western blot and co-immunoprecipitation in an AD cell model. The text mining strategy using a vast amount of AD-related literature and the TCM database identified curcumin, whose major component was ferulic acid (FA). This was used as a key candidate compound for further study. Using the top calculated interaction score in STRING, BACE1 and MMP2 were implicated in the activity of FA in AD. Exposure of SHSY5Y-APP cells to FA resulted in the decrease in expression levels of BACE-1 and APP, while the expression of MMP-2 and MMP-9 increased in a dose-dependent manner. This suggests that FA induced BACE1 and MMP2 pathways maybe novel potential mechanisms involved in AD. The text mining of literature and TCM database related to AD suggested FA as a promising TCM ingredient for the treatment of AD. Potential mechanisms interconnected and integrated with Aβ aggregation inhibition and extracellular matrix remodeling underlying the activity of FA were identified using in vitro studies.

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

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…

Data Mining: A Hybrid Methodology for Complex and Dynamic Research

ERIC Educational Resources Information Center

Lang, Susan; Baehr, Craig

2012-01-01

This article provides an overview of the ways in which data and text mining have potential as research methodologies in composition studies. It introduces data mining in the context of the field of composition studies and discusses ways in which this methodology can complement and extend our existing research practices by blending the best of what…

Data Mining for Financial Applications

NASA Astrophysics Data System (ADS)

Kovalerchuk, Boris; Vityaev, Evgenii

This chapter describes Data Mining in finance by discussing financial tasks, specifics of methodologies and techniques in this Data Mining area. It includes time dependence, data selection, forecast horizon, measures of success, quality of patterns, hypothesis evaluation, problem ID, method profile, attribute-based and relational methodologies. The second part of the chapter discusses Data Mining models and practice in finance. It covers use of neural networks in portfolio management, design of interpretable trading rules and discovering money laundering schemes using decision rules and relational Data Mining methodology.

Restoration of tropical moist forest on bauxite mined lands in the Brazilian Amazon

Treesearch

John A Parrotta; Oliver H. Knowles

1999-01-01

We evaluated forest structure and composition in 9- to 13-year-old stands established on a bauxite-mined site at Trombetas (Pará), Brazil, using four different reforestation techniques following initial site preparation and topsoil replacement. These techniques included reliance on natural forest regeneration, mixed commercial species plantings of mostly exotic timber...

Data-Mining Techniques in Detecting Factors Linked to Academic Achievement

ERIC Educational Resources Information Center

Martínez Abad, Fernando; Chaparro Caso López, Alicia A.

2017-01-01

In light of the emergence of statistical analysis techniques based on data mining in education sciences, and the potential they offer to detect non-trivial information in large databases, this paper presents a procedure used to detect factors linked to academic achievement in large-scale assessments. The study is based on a non-experimental,…

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.

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.

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)

Illustrated surface mining methods

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

Not Available

1979-01-01

This manual provides a visual synopsis of surface coal mining methods in the United States. The manual presents various surface mining methods and techniques through artist renderings and appropriate descriptions. The productive coal fields of the United States were divided into four regions according to geology and physiography. A glossay of terminology is included. (DP)

Analyzing Student Inquiry Data Using Process Discovery and Sequence Classification

ERIC Educational Resources Information Center

Emond, Bruno; Buffett, Scott

2015-01-01

This paper reports on results of applying process discovery mining and sequence classification mining techniques to a data set of semi-structured learning activities. The main research objective is to advance educational data mining to model and support self-regulated learning in heterogeneous environments of learning content, activities, and…

A Contextualized, Differential Sequence Mining Method to Derive Students' Learning Behavior Patterns

ERIC Educational Resources Information Center

Kinnebrew, John S.; Loretz, Kirk M.; Biswas, Gautam

2013-01-01

Computer-based learning environments can produce a wealth of data on student learning interactions. This paper presents an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs a novel combination of sequence mining techniques to identify deferentially…

Data mining-based coefficient of influence factors optimization of test paper reliability

NASA Astrophysics Data System (ADS)

Xu, Peiyao; Jiang, Huiping; Wei, Jieyao

2018-05-01

Test is a significant part of the teaching process. It demonstrates the final outcome of school teaching through teachers' teaching level and students' scores. The analysis of test paper is a complex operation that has the characteristics of non-linear relation in the length of the paper, time duration and the degree of difficulty. It is therefore difficult to optimize the coefficient of influence factors under different conditions in order to get text papers with clearly higher reliability with general methods [1]. With data mining techniques like Support Vector Regression (SVR) and Genetic Algorithm (GA), we can model the test paper analysis and optimize the coefficient of impact factors for higher reliability. It's easy to find that the combination of SVR and GA can get an effective advance in reliability from the test results. The optimal coefficient of influence factors optimization has a practicability in actual application, and the whole optimizing operation can offer model basis for test paper analysis.

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

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.

Integration of Artificial Market Simulation and Text Mining for Market Analysis

NASA Astrophysics Data System (ADS)

Izumi, Kiyoshi; Matsui, Hiroki; Matsuo, Yutaka

We constructed an evaluation system of the self-impact in a financial market using an artificial market and text-mining technology. Economic trends were first extracted from text data circulating in the real world. Then, the trends were inputted into the market simulation. Our simulation revealed that an operation by intervention could reduce over 70% of rate fluctuation in 1995. By the simulation results, the system was able to help for its user to find the exchange policy which can stabilize the yen-dollar rate.

BioC implementations in Go, Perl, Python and Ruby

PubMed Central

Liu, Wanli; Islamaj Doğan, Rezarta; Kwon, Dongseop; Marques, Hernani; Rinaldi, Fabio; Wilbur, W. John; Comeau, Donald C.

2014-01-01

As part of a communitywide effort for evaluating text mining and information extraction systems applied to the biomedical domain, BioC is focused on the goal of interoperability, currently a major barrier to wide-scale adoption of text mining tools. BioC is a simple XML format, specified by DTD, for exchanging data for biomedical natural language processing. With initial implementations in C++ and Java, BioC provides libraries of code for reading and writing BioC text documents and annotations. We extend BioC to Perl, Python, Go and Ruby. We used SWIG to extend the C++ implementation for Perl and one Python implementation. A second Python implementation and the Ruby implementation use native data structures and libraries. BioC is also implemented in the Google language Go. BioC modules are functional in all of these languages, which can facilitate text mining tasks. BioC implementations are freely available through the BioC site: http://bioc.sourceforge.net. Database URL: http://bioc.sourceforge.net/ PMID:24961236

Video mining using combinations of unsupervised and supervised learning techniques

NASA Astrophysics Data System (ADS)

Divakaran, Ajay; Miyahara, Koji; Peker, Kadir A.; Radhakrishnan, Regunathan; Xiong, Ziyou

2003-12-01

We discuss the meaning and significance of the video mining problem, and present our work on some aspects of video mining. A simple definition of video mining is unsupervised discovery of patterns in audio-visual content. Such purely unsupervised discovery is readily applicable to video surveillance as well as to consumer video browsing applications. We interpret video mining as content-adaptive or "blind" content processing, in which the first stage is content characterization and the second stage is event discovery based on the characterization obtained in stage 1. We discuss the target applications and find that using a purely unsupervised approach are too computationally complex to be implemented on our product platform. We then describe various combinations of unsupervised and supervised learning techniques that help discover patterns that are useful to the end-user of the application. We target consumer video browsing applications such as commercial message detection, sports highlights extraction etc. We employ both audio and video features. We find that supervised audio classification combined with unsupervised unusual event discovery enables accurate supervised detection of desired events. Our techniques are computationally simple and robust to common variations in production styles etc.

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

@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.

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.

miRTex: A Text Mining System for miRNA-Gene Relation Extraction

PubMed Central

Li, Gang; Ross, Karen E.; Arighi, Cecilia N.; Peng, Yifan; Wu, Cathy H.; Vijay-Shanker, K.

2015-01-01

MicroRNAs (miRNAs) regulate a wide range of cellular and developmental processes through gene expression suppression or mRNA degradation. Experimentally validated miRNA gene targets are often reported in the literature. In this paper, we describe miRTex, a text mining system that extracts miRNA-target relations, as well as miRNA-gene and gene-miRNA regulation relations. The system achieves good precision and recall when evaluated on a literature corpus of 150 abstracts with F-scores close to 0.90 on the three different types of relations. We conducted full-scale text mining using miRTex to process all the Medline abstracts and all the full-length articles in the PubMed Central Open Access Subset. The results for all the Medline abstracts are stored in a database for interactive query and file download via the website at http://proteininformationresource.org/mirtex. Using miRTex, we identified genes potentially regulated by miRNAs in Triple Negative Breast Cancer, as well as miRNA-gene relations that, in conjunction with kinase-substrate relations, regulate the response to abiotic stress in Arabidopsis thaliana. These two use cases demonstrate the usefulness of miRTex text mining in the analysis of miRNA-regulated biological processes. PMID:26407127

LimTox: a web tool for applied text mining of adverse event and toxicity associations of compounds, drugs and genes

PubMed Central

Cañada, Andres; Rabal, Obdulia; Oyarzabal, Julen; Valencia, Alfonso

2017-01-01

Abstract A considerable effort has been devoted to retrieve systematically information for genes and proteins as well as relationships between them. Despite the importance of chemical compounds and drugs as a central bio-entity in pharmacological and biological research, only a limited number of freely available chemical text-mining/search engine technologies are currently accessible. Here we present LimTox (Literature Mining for Toxicology), a web-based online biomedical search tool with special focus on adverse hepatobiliary reactions. It integrates a range of text mining, named entity recognition and information extraction components. LimTox relies on machine-learning, rule-based, pattern-based and term lookup strategies. This system processes scientific abstracts, a set of full text articles and medical agency assessment reports. Although the main focus of LimTox is on adverse liver events, it enables also basic searches for other organ level toxicity associations (nephrotoxicity, cardiotoxicity, thyrotoxicity and phospholipidosis). This tool supports specialized search queries for: chemical compounds/drugs, genes (with additional emphasis on key enzymes in drug metabolism, namely P450 cytochromes—CYPs) and biochemical liver markers. The LimTox website is free and open to all users and there is no login requirement. LimTox can be accessed at: http://limtox.bioinfo.cnio.es PMID:28531339

Text mining a self-report back-translation.

PubMed

Blanch, Angel; Aluja, Anton

2016-06-01

There are several recommendations about the routine to undertake when back translating self-report instruments in cross-cultural research. However, text mining methods have been generally ignored within this field. This work describes a text mining innovative application useful to adapt a personality questionnaire to 12 different languages. The method is divided in 3 different stages, a descriptive analysis of the available back-translated instrument versions, a dissimilarity assessment between the source language instrument and the 12 back-translations, and an item assessment of item meaning equivalence. The suggested method contributes to improve the back-translation process of self-report instruments for cross-cultural research in 2 significant intertwined ways. First, it defines a systematic approach to the back translation issue, allowing for a more orderly and informed evaluation concerning the equivalence of different versions of the same instrument in different languages. Second, it provides more accurate instrument back-translations, which has direct implications for the reliability and validity of the instrument's test scores when used in different cultures/languages. In addition, this procedure can be extended to the back-translation of self-reports measuring psychological constructs in clinical assessment. Future research works could refine the suggested methodology and use additional available text mining tools. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

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.

Haneş and Valea Vinului (Romania) closed mines Acid Mine Drainages (AMDs)--actual condition and passive treatment remediation proposal.

PubMed

Măicăneanu, Andrada; Bedelean, Horea; Ardelean, Marius; Burcă, Silvia; Stanca, Maria

2013-10-01

Acid Mine Drainages (AMDs) from Haneş and Valea Vinului (Romania) closed mines were considered for characterization and treatment using a local zeolitic volcanic tuff, ZVT, (Măcicaş, Cluj County, Romania). Water samples were collected from two locations, before and after discharging point in case of Haneş mine, and on three horizons in case of Valea Vinului mine. Physico-chemical (pH, total solid, heavy metal ions concentration) analyses showed that the environment is strongly affected by these AMD discharges even if the mines were closed years ago. Iron, manganese and zinc were the main pollutants identified in Haneş mine AMD, while zinc is the one mainly present in case of Valea Vinului AMD. A batch technique (no stirring) in which the ZVT was put in contact with the AMD sample was proposed as a passive remediation technique. ZVT successfully remove heavy metal ion from AMD. According to heavy metal ion concentrations, removal efficiencies are reaching 100%, varying as follows, Fe(2+)>Zn(2+)>Mn(2+). When the ZVT was compared with two cationic resins (strong, SAR and weak acid, WAR) the following series was depicted, SAR>ZVT>WAR. Copyright © 2013 Elsevier Ltd. All rights reserved.

Simulation of patient flow in multiple healthcare units using process and data mining techniques for model identification.

PubMed

Kovalchuk, Sergey V; Funkner, Anastasia A; Metsker, Oleg G; Yakovlev, Aleksey N

2018-06-01

An approach to building a hybrid simulation of patient flow is introduced with a combination of data-driven methods for automation of model identification. The approach is described with a conceptual framework and basic methods for combination of different techniques. The implementation of the proposed approach for simulation of the acute coronary syndrome (ACS) was developed and used in an experimental study. A combination of data, text, process mining techniques, and machine learning approaches for the analysis of electronic health records (EHRs) with discrete-event simulation (DES) and queueing theory for the simulation of patient flow was proposed. The performed analysis of EHRs for ACS patients enabled identification of several classes of clinical pathways (CPs) which were used to implement a more realistic simulation of the patient flow. The developed solution was implemented using Python libraries (SimPy, SciPy, and others). The proposed approach enables more a realistic and detailed simulation of the patient flow within a group of related departments. An experimental study shows an improved simulation of patient length of stay for ACS patient flow obtained from EHRs in Almazov National Medical Research Centre in Saint Petersburg, Russia. The proposed approach, methods, and solutions provide a conceptual, methodological, and programming framework for the implementation of a simulation of complex and diverse scenarios within a flow of patients for different purposes: decision making, training, management optimization, and others. Copyright © 2018 Elsevier Inc. All rights reserved.

Biogeochemical behaviour and bioremediation of uranium in waters of abandoned mines.

PubMed

Mkandawire, Martin

2013-11-01

The discharges of uranium and associated radionuclides as well as heavy metals and metalloids from waste and tailing dumps in abandoned uranium mining and processing sites pose contamination risks to surface and groundwater. Although many more are being planned for nuclear energy purposes, most of the abandoned uranium mines are a legacy of uranium production that fuelled arms race during the cold war of the last century. Since the end of cold war, there have been efforts to rehabilitate the mining sites, initially, using classical remediation techniques based on high chemical and civil engineering. Recently, bioremediation technology has been sought as alternatives to the classical approach due to reasons, which include: (a) high demand of sites requiring remediation; (b) the economic implication of running and maintaining the facilities due to high energy and work force demand; and (c) the pattern and characteristics of contaminant discharges in most of the former uranium mining and processing sites prevents the use of classical methods. This review discusses risks of uranium contamination from abandoned uranium mines from the biogeochemical point of view and the potential and limitation of uranium bioremediation technique as alternative to classical approach in abandoned uranium mining and processing sites.

Privacy Preserving Technique for Euclidean Distance Based Mining Algorithms Using a Wavelet Related Transform

NASA Astrophysics Data System (ADS)

Kadampur, Mohammad Ali; D. v. L. N., Somayajulu

Privacy preserving data mining is an art of knowledge discovery without revealing the sensitive data of the data set. In this paper a data transformation technique using wavelets is presented for privacy preserving data mining. Wavelets use well known energy compaction approach during data transformation and only the high energy coefficients are published to the public domain instead of the actual data proper. It is found that the transformed data preserves the Eucleadian distances and the method can be used in privacy preserving clustering. Wavelets offer the inherent improved time complexity.

Survey of Analysis of Crime Detection Techniques Using Data Mining and Machine Learning

NASA Astrophysics Data System (ADS)

Prabakaran, S.; Mitra, Shilpa

2018-04-01

Data mining is the field containing procedures for finding designs or patterns in a huge dataset, it includes strategies at the convergence of machine learning and database framework. It can be applied to various fields like future healthcare, market basket analysis, education, manufacturing engineering, crime investigation etc. Among these, crime investigation is an interesting application to process crime characteristics to help the society for a better living. This paper survey various data mining techniques used in this domain. This study may be helpful in designing new strategies for crime prediction and analysis.

Supporting Solar Physics Research via Data Mining

NASA Astrophysics Data System (ADS)

Angryk, Rafal; Banda, J.; Schuh, M.; Ganesan Pillai, K.; Tosun, H.; Martens, P.

2012-05-01

In this talk we will briefly introduce three pillars of data mining (i.e. frequent patterns discovery, classification, and clustering), and discuss some possible applications of known data mining techniques which can directly benefit solar physics research. In particular, we plan to demonstrate applicability of frequent patterns discovery methods for the verification of hypotheses about co-occurrence (in space and time) of filaments and sigmoids. We will also show how classification/machine learning algorithms can be utilized to verify human-created software modules to discover individual types of solar phenomena. Finally, we will discuss applicability of clustering techniques to image data processing.

Searching for 'Unknown Unknowns'

NASA Technical Reports Server (NTRS)

Parsons, Vickie S.

2005-01-01

The NASA Engineering and Safety Center (NESC) was established to improve safety through engineering excellence within NASA programs and projects. As part of this goal, methods are being investigated to enable the NESC to become proactive in identifying areas that may be precursors to future problems. The goal is to find unknown indicators of future problems, not to duplicate the program-specific trending efforts. The data that is critical for detecting these indicators exist in a plethora of dissimilar non-conformance and other databases (without a common format or taxonomy). In fact, much of the data is unstructured text. However, one common database is not required if the right standards and electronic tools are employed. Electronic data mining is a particularly promising tool for this effort into unsupervised learning of common factors. This work in progress began with a systematic evaluation of available data mining software packages, based on documented decision techniques using weighted criteria. The four packages, which were perceived to have the most promise for NASA applications, are being benchmarked and evaluated by independent contractors. Preliminary recommendations for "best practices" in data mining and trending are provided. Final results and recommendations should be available in the Fall 2005. This critical first step in identifying "unknown unknowns" before they become problems is applicable to any set of engineering or programmatic data.

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 Quality Phrases from Massive Text Corpora

PubMed Central

Liu, Jialu; Shang, Jingbo; Wang, Chi; Ren, Xiang; Han, Jiawei

2015-01-01

Text data are ubiquitous and play an essential role in big data applications. However, text data are mostly unstructured. Transforming unstructured text into structured units (e.g., semantically meaningful phrases) will substantially reduce semantic ambiguity and enhance the power and efficiency at manipulating such data using database technology. Thus mining quality phrases is a critical research problem in the field of databases. In this paper, we propose a new framework that extracts quality phrases from text corpora integrated with phrasal segmentation. The framework requires only limited training but the quality of phrases so generated is close to human judgment. Moreover, the method is scalable: both computation time and required space grow linearly as corpus size increases. Our experiments on large text corpora demonstrate the quality and efficiency of the new method. PMID:26705375

Data mining and visualization techniques

DOEpatents

Wong, Pak Chung [Richland, WA; Whitney, Paul [Richland, WA; Thomas, Jim [Richland, WA

2004-03-23

Disclosed are association rule identification and visualization methods, systems, and apparatus. An association rule in data mining is an implication of the form X.fwdarw.Y where X is a set of antecedent items and Y is the consequent item. A unique visualization technique that provides multiple antecedent, consequent, confidence, and support information is disclosed to facilitate better presentation of large quantities of complex association rules.

[Analysis of syndrome discipline of generalized anxiety disorder using data mining techniques].

PubMed

Tang, Qi-sheng; Sun, Wen-jun; Qu, Miao; Guo, Dong-fang

2012-09-01

To study the use of data mining techniques in analyzing the syndrome discipline of generalized anxiety disorder (GAD). From August 1, 2009 to July 31, 2010, 705 patients with GAD in 10 hospitals of Beijing were investigated over one year. Data mining techniques, such as Bayes net and cluster analysis, were used to analyze the syndrome discipline of GAD. A total of 61 symptoms of GAD were screened out. By using Bayes net, nine syndromes of GAD were abstracted based on the symptoms. Eight syndromes were abstracted by cluster analysis. After screening for duplicate syndromes and combining the experts' experience and traditional Chinese medicine theory, six syndromes of GAD were defined. These included depressed liver qi transforming into fire, phlegm-heat harassing the heart, liver depression and spleen deficiency, heart-kidney non-interaction, dual deficiency of the heart and spleen, and kidney deficiency and liver yang hyperactivity. Based on the results, the draft of Syndrome Diagnostic Criteria for Generalized Anxiety Disorder was developed. Data mining techniques such as Bayes net and cluster analysis have certain future potential for establishing syndrome models and analyzing syndrome discipline, thus they are suitable for the research of syndrome differentiation.

Data mining in child welfare.

PubMed

Schoech, D; Quinn, A; Rycraft, J R

2000-01-01

Data mining is the sifting through of voluminous data to extract knowledge for decision making. This article illustrates the context, concepts, processes, techniques, and tools of data mining, using statistical and neural network analyses on a dataset concerning employee turnover. The resulting models and their predictive capability, advantages and disadvantages, and implications for decision support are highlighted.

Reforestation of mined land in the northeastern and north-central U.S.

Treesearch

Walter H. Davidson; Russell J. Hutnik; Delbert E. Parr

1984-01-01

This paper reviews the state of the art of surface mine reclamation for forestry in Pennsylvania, Maryland, West Virginia, Ohio, Indiana, and Illinois. Legislative constraints, socioeconomic issues, factors limiting the success of reforestation efforts, post-mining land-use trends, species options, and establishment techniques are discussed. Sources of assistance to...

Identifying Learning Behaviors by Contextualizing Differential Sequence Mining with Action Features and Performance Evolution

ERIC Educational Resources Information Center

Kinnebrew, John S.; Biswas, Gautam

2012-01-01

Our learning-by-teaching environment, Betty's Brain, captures a wealth of data on students' learning interactions as they teach a virtual agent. This paper extends an exploratory data mining methodology for assessing and comparing students' learning behaviors from these interaction traces. The core algorithm employs sequence mining techniques to…

Increasing the Reliability of the Work of Artificial Filtering Arrays for the Purification of Quarry Waste Water

NASA Astrophysics Data System (ADS)

Tyulenev, Maxim; Lesin, Yury; Litvin, Oleg; Maliukhina, Elena; Abay, Asmelash

2017-11-01

Features of geological structure of the Kuznetsk coal basin stipulate the application of a low-cost open technique of coal mining, which is more advantageous both from the economic standpoint, and by safety criteria of mining. However, open mining affects significantly the water resources of region. Intensive pollution of reservoirs and water courses, exhaustion of the underground water-bearing layers, violation of a hydrographic network, etc. be-long to the main disadvantages of an open technique of coal mining. Besides, the volume of the water coming into the mining producers exceeds signi-ficantly the needed quantity. According to the data of annual reports of ecology and natural resources department, 348.277 million m3 of water were ta-ken away during production of soft coal, brown coal and lignum fossil from waters of Kemerovo region in 2013 (mostly from underground water objects (96,5%) when draining of mine openings). At the same time, only 87.018 million m3 of water (25%) has been used within a year.

Methods and costs of thin-seam mining. Final report, 25 September 1977-24 January 1979. [Thin seam in association with a thick seam

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

Finch, T.E.; Fidler, E.L.

1981-02-01

This report defines the state of the art (circa 1978) in removing thin coal seams associated with vastly thicker seams found in the surface coal mines of the western United States. New techniques are evaluated and an innovative method and machine is proposed. Western states resource recovery regulations are addressed and representative mining operations are examined. Thin seam recovery is investigated through its effect on (1) overburden removal, (2) conventional seam extraction methods, and (3) innovative techniques. Equations and graphs are used to accommodate the variable stratigraphic positions in the mining sequence on which thin seams occur. Industrial concern andmore » agency regulations provided the impetus for this study of total resource recovery. The results are a compendium of thin seam removal methods and costs. The work explains how the mining industry recovers thin coal seams in western surface mines where extremely thick seams naturally hold the most attention. It explains what new developments imply and where to look for new improvements and their probable adaptability.« less

Field test of an alternative longwall gate road design

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

Cox, R.M.; Vandergrift, T.L.; McDonnell, J.P.

1994-01-01

The US Bureau of Mines (USBM) MULSIM/ML modeling technique has been used to analyze anticipated stress distributions for a proposed alternative longwall gate road design for a western Colorado coal mine. The model analyses indicated that the alternative gate road design would reduce stresses in the headgate entry. To test the validity of the alternative gate road design under actual mining conditions, a test section of the alternative system was incorporated into a subsequent set of gate roads developed at the mine. The alternative gate road test section was instrumented with borehole pressure cells, as part of an ongoing USBMmore » research project to monitor ground pressure changes as longwall mining progressed. During the excavation of the adjacent longwall panels, the behavior of the alternative gate road system was monitored continuously using the USBM computer-assisted Ground Control Management System. During these field tests, the alternative gate road system was first monitored and evaluated as a headgate, and later monitored and evaluated as a tailgate. The results of the field tests confirmed the validity of using the MULSIM/NL modeling technique to evaluate mine designs.« less

All-Optical Fibre Networks For Coal Mines

NASA Astrophysics Data System (ADS)

Zientkiewicz, Jacek K.

1987-09-01

A topic of the paper is fiber-optic integrated network (FOIN) suited to the most hostile environments existing in coal mines. The use of optical fibres for transmission of mine instrumentation data offers the prospects of improved safety and immunity to electromagnetic interference (EMI). The feasibility of optically powered sensors has opened up new opportunities for research into optical signal processing architectures. This article discusses a new fibre-optic sensor network involving a time domain multiplexing(TDM)scheme and optical signal processing techniques. The pros and cons of different FOIN topologies with respect to coal mine applications are considered. The emphasis has been placed on a recently developed all-optical fibre network using spread spectrum code division multiple access (COMA) techniques. The all-optical networks have applications in explosive environments where electrical isolation is required.

Issues of data governance associated with data mining in medical research: experiences from an empirical study.

PubMed

Nahar, Jesmin; Imam, Tasadduq; Tickle, Kevin S; Garcia-Alonso, Debora

2013-01-01

This chapter is a review of data mining techniques used in medical research. It will cover the existing applications of these techniques in the identification of diseases, and also present the authors' research experiences in medical disease diagnosis and analysis. A computational diagnosis approach can have a significant impact on accurate diagnosis and result in time and cost effective solutions. The chapter will begin with an overview of computational intelligence concepts, followed by details on different classification algorithms. Use of association learning, a well recognised data mining procedure, will also be discussed. Many of the datasets considered in existing medical data mining research are imbalanced, and the chapter focuses on this issue as well. Lastly, the chapter outlines the need of data governance in this research domain.

A semantic-based method for extracting concept definitions from scientific publications: evaluation in the autism phenotype domain.

PubMed

Hassanpour, Saeed; O'Connor, Martin J; Das, Amar K

2013-08-12

A variety of informatics approaches have been developed that use information retrieval, NLP and text-mining techniques to identify biomedical concepts and relations within scientific publications or their sentences. These approaches have not typically addressed the challenge of extracting more complex knowledge such as biomedical definitions. In our efforts to facilitate knowledge acquisition of rule-based definitions of autism phenotypes, we have developed a novel semantic-based text-mining approach that can automatically identify such definitions within text. Using an existing knowledge base of 156 autism phenotype definitions and an annotated corpus of 26 source articles containing such definitions, we evaluated and compared the average rank of correctly identified rule definition or corresponding rule template using both our semantic-based approach and a standard term-based approach. We examined three separate scenarios: (1) the snippet of text contained a definition already in the knowledge base; (2) the snippet contained an alternative definition for a concept in the knowledge base; and (3) the snippet contained a definition not in the knowledge base. Our semantic-based approach had a higher average rank than the term-based approach for each of the three scenarios (scenario 1: 3.8 vs. 5.0; scenario 2: 2.8 vs. 4.9; and scenario 3: 4.5 vs. 6.2), with each comparison significant at the p-value of 0.05 using the Wilcoxon signed-rank test. Our work shows that leveraging existing domain knowledge in the information extraction of biomedical definitions significantly improves the correct identification of such knowledge within sentences. Our method can thus help researchers rapidly acquire knowledge about biomedical definitions that are specified and evolving within an ever-growing corpus of scientific publications.

Non-destructive analysis of sensory traits of dry-cured loins by MRI-computer vision techniques and data mining.

PubMed

Caballero, Daniel; Antequera, Teresa; Caro, Andrés; Ávila, María Del Mar; G Rodríguez, Pablo; Perez-Palacios, Trinidad

2017-07-01

Magnetic resonance imaging (MRI) combined with computer vision techniques have been proposed as an alternative or complementary technique to determine the quality parameters of food in a non-destructive way. The aim of this work was to analyze the sensory attributes of dry-cured loins using this technique. For that, different MRI acquisition sequences (spin echo, gradient echo and turbo 3D), algorithms for MRI analysis (GLCM, NGLDM, GLRLM and GLCM-NGLDM-GLRLM) and predictive data mining techniques (multiple linear regression and isotonic regression) were tested. The correlation coefficient (R) and mean absolute error (MAE) were used to validate the prediction results. The combination of spin echo, GLCM and isotonic regression produced the most accurate results. In addition, the MRI data from dry-cured loins seems to be more suitable than the data from fresh loins. The application of predictive data mining techniques on computational texture features from the MRI data of loins enables the determination of the sensory traits of dry-cured loins in a non-destructive way. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Multisensor fusion for the detection of mines and minelike targets

NASA Astrophysics Data System (ADS)

Hanshaw, Terilee

1995-06-01

The US Army's Communications and Electronics Command through the auspices of its Night Vision and Electronics Sensors Directorate (CECOM-NVESD) is actively applying multisensor techniques to the detection of mine targets. This multisensor research results from the 'detection activity' with its broad range of operational conditions and targets. Multisensor operation justifies significant attention by yielding high target detection and low false alarm statistics. Furthermore, recent advances in sensor and computing technologies make its practical application realistic and affordable. The mine detection field-of-endeavor has since its WWI baptismal investigated the known spectra for applicable mine observation phenomena. Countless sensors, algorithms, processors, networks, and other techniques have been investigated to determine candidacy for mine detection. CECOM-NVESD efforts have addressed a wide range of sensors spanning the spectrum from gravity field perturbations, magentic field disturbances, seismic sounding, electromagnetic fields, earth penetrating radar imagery, and infrared/visible/ultraviolet surface imaging technologies. Supplementary analysis has considered sensor candidate applicability by testing under field conditions (versus laboratory), in determination of fieldability. As these field conditions directly effect the probability of detection and false alarms, sensor employment and design must be considered. Consequently, as a given sensor's performance is influenced directly by the operational conditions, tradeoffs are necessary. At present, mass produced and fielded mine detection techniques are limited to those incorporating a single sensor/processor methodology such as, pulse induction and megnetometry, as found in hand held detectors. The most sensitive fielded systems can detect minute metal components in small mine targets but result in very high false alarm rates reducing velocity in operation environments. Furthermore, the actual speed of advance for the entire mission (convoy, movement to engagement, etc.) is determined by the level of difficulty presented in clearance or avoidance activities required in response to the potential 'targets' marked throughout a detection activity. Therefore the application of fielded hand held systems to convoy operations in clearly impractical. CECOM-NVESD efforts are presently seeking to overcome these operational limitations by substantially increasing speed of detection while reducing the false alarm rate through the application of multisensor techniques. The CECOM-NVESD application of multisensor techniques through integration/fusion methods will be defined in this paper.

GENERAL EXTERIOR VIEW, LOOKING NORTHEAST, OF THE SURFACE PLANT WITH ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

GENERAL EXTERIOR VIEW, LOOKING NORTHEAST, OF THE SURFACE PLANT WITH CONVEYORS. JIM WALTER RESOURCES INC. MINING DIVISION OPERATES FOUR UNDERGROUND COAL MINES IN THE BLUE CREEK COAL FIELD OF BIRMINGHAM DISTRICT, THREE IN TUSCALOOSA COUNTY AND ONE IN JEFFERSON COUNTY. TOTAL ANNUAL PRODUCTION IS 8,000,000 TONS. AT 2,300 DEEP, JIM WALTER'S BROOKWOOD MINES ARE THE DEEPEST UNDERGROUND COAL MINES IN NORTH AMERICA. THEY PRODUCE A HIGH-GRADE MEDIUM VOLATILE LOW SULPHUR METALLURGICAL COAL. THE BROOKWOOD NO. 5 MINE (PICTURED IN THIS PHOTOGRAPH) EMPLOYS THE LONGWALL MINING TECHNIQUES WITH BELTS CONVEYING COAL FROM UNDERGROUND OPERATIONS TO THE SURFACE. - JIm Walter Resources, Incorporated, Brookwood No. 5 Mine, 12972 Lock 17 Road, Brookwood, Tuscaloosa County, AL

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).

Use of an automatic earth resistivity system for detection of abandoned mine workings

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

Peters, W.R.; Burdick, R.

1982-04-01

Under the sponsorship of the US Bureau of Mines, a surface-operated automatic high resolution earth resistivity system and associated computer data processing techniques have been designed and constructed for use as a potential means of detecting abandoned coal mine workings. The hardware and software aspects of the new system are described together with applications of the method to the survey and mapping of abandoned mine workings.

Exploratory analysis of textual data from the Mother and Child Handbook using the text-mining method: Relationships with maternal traits and post-partum depression.

PubMed

Matsuda, Yoshio; Manaka, Tomoko; Kobayashi, Makiko; Sato, Shuhei; Ohwada, Michitaka

2016-06-01

The aim of the present study was to examine the possibility of screening apprehensive pregnant women and mothers at risk for post-partum depression from an analysis of the textual data in the Mother and Child Handbook by using the text-mining method. Uncomplicated pregnant women (n = 58) were divided into two groups according to State-Trait Anxiety Inventory grade (high trait [group I, n = 21] and low trait [group II, n = 37]) or Edinburgh Postnatal Depression Scale score (high score [group III, n = 15] and low score [group IV, n = 43]). An exploratory analysis of the textual data from the Maternal and Child Handbook was conducted using the text-mining method with the Word Miner software program. A comparison of the 'structure elements' was made between the two groups. The number of structure elements extracted by separated words from text data was 20 004 and the number of structure elements with a threshold of 2 or more as an initial value was 1168. Fifteen key words related to maternal anxiety, and six key words related to post-partum depression were extracted. The text-mining method is useful for the exploratory analysis of textual data obtained from pregnant woman, and this screening method has been suggested to be useful for apprehensive pregnant women and mothers at risk for post-partum depression. © 2016 Japan Society of Obstetrics and Gynecology.

An Integrated Suite of Text and Data Mining Tools - Phase II

DTIC Science & Technology

2005-08-30

Riverside, CA, USA Mazda Motor Corp, Jpn Univ of Darmstadt, Darmstadt, Ger Navy Center for Applied Research in Artificial Intelligence Univ of...with Georgia Tech Research Corporation developed a desktop text-mining software tool named TechOASIS (known commercially as VantagePoint). By the...of this dataset and groups the Corporate Source items that co-occur with the found items. He decides he is only interested in the institutions

Automated Assessment of Patients' Self-Narratives for Posttraumatic Stress Disorder Screening Using Natural Language Processing and Text Mining.

PubMed

He, Qiwei; Veldkamp, Bernard P; Glas, Cees A W; de Vries, Theo

2017-03-01

Patients' narratives about traumatic experiences and symptoms are useful in clinical screening and diagnostic procedures. In this study, we presented an automated assessment system to screen patients for posttraumatic stress disorder via a natural language processing and text-mining approach. Four machine-learning algorithms-including decision tree, naive Bayes, support vector machine, and an alternative classification approach called the product score model-were used in combination with n-gram representation models to identify patterns between verbal features in self-narratives and psychiatric diagnoses. With our sample, the product score model with unigrams attained the highest prediction accuracy when compared with practitioners' diagnoses. The addition of multigrams contributed most to balancing the metrics of sensitivity and specificity. This article also demonstrates that text mining is a promising approach for analyzing patients' self-expression behavior, thus helping clinicians identify potential patients from an early stage.

Characterization of particulate emissions from Australian open-cut coal mines: Toward improved emission estimates.

PubMed

Richardson, Claire; Rutherford, Shannon; Agranovski, Igor

2018-06-01

Given the significance of mining as a source of particulates, accurate characterization of emissions is important for the development of appropriate emission estimation techniques for use in modeling predictions and to inform regulatory decisions. The currently available emission estimation methods for Australian open-cut coal mines relate primarily to total suspended particulates and PM 10 (particulate matter with an aerodynamic diameter <10 μm), and limited data are available relating to the PM 2.5 (<2.5 μm) size fraction. To provide an initial analysis of the appropriateness of the currently available emission estimation techniques, this paper presents results of sampling completed at three open-cut coal mines in Australia. The monitoring data demonstrate that the particulate size fraction varies for different mining activities, and that the region in which the mine is located influences the characteristics of the particulates emitted to the atmosphere. The proportion of fine particulates in the sample increased with distance from the source, with the coarse fraction being a more significant proportion of total suspended particulates close to the source of emissions. In terms of particulate composition, the results demonstrate that the particulate emissions are predominantly sourced from naturally occurring geological material, and coal comprises less than 13% of the overall emissions. The size fractionation exhibited by the sampling data sets is similar to that adopted in current Australian emission estimation methods but differs from the size fractionation presented in the U.S. Environmental Protection Agency methodology. Development of region-specific emission estimation techniques for PM 10 and PM 2.5 from open-cut coal mines is necessary to allow accurate prediction of particulate emissions to inform regulatory decisions and for use in modeling predictions. Development of region-specific emission estimation techniques for PM 10 and PM 2.5 from open-cut coal mines is necessary to allow accurate prediction of particulate emissions to inform regulatory decisions and for use in modeling predictions. Comprehensive air quality monitoring was undertaken, and corresponding recommendations were provided.

The structure and infrastructure of the global nanotechnology literature

NASA Astrophysics Data System (ADS)

Kostoff, Ronald N.; Stump, Jesse A.; Johnson, Dustin; Murday, James S.; Lau, Clifford G. Y.; Tolles, William M.

2006-08-01

Text mining is the extraction of useful information from large volumes of text. A text mining analysis of the global open nanotechnology literature was performed. Records from the Science Citation Index (SCI)/Social SCI were analyzed to provide the infrastructure of the global nanotechnology literature (prolific authors/journals/institutions/countries, most cited authors/papers/journals) and the thematic structure (taxonomy) of the global nanotechnology literature, from a science perspective. Records from the Engineering Compendex (EC) were analyzed to provide a taxonomy from a technology perspective. The Far Eastern countries have expanded nanotechnology publication output dramatically in the past decade.

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/.

A systems biology approach to the global analysis of transcription factors in colorectal cancer.

PubMed

Pradhan, Meeta P; Prasad, Nagendra K A; Palakal, Mathew J

2012-08-01

Biological entities do not perform in isolation, and often, it is the nature and degree of interactions among numerous biological entities which ultimately determines any final outcome. Hence, experimental data on any single biological entity can be of limited value when considered only in isolation. To address this, we propose that augmenting individual entity data with the literature will not only better define the entity's own significance but also uncover relationships with novel biological entities.To test this notion, we developed a comprehensive text mining and computational methodology that focused on discovering new targets of one class of molecular entities, transcription factors (TF), within one particular disease, colorectal cancer (CRC). We used 39 molecular entities known to be associated with CRC along with six colorectal cancer terms as the bait list, or list of search terms, for mining the biomedical literature to identify CRC-specific genes and proteins. Using the literature-mined data, we constructed a global TF interaction network for CRC. We then developed a multi-level, multi-parametric methodology to identify TFs to CRC. The small bait list, when augmented with literature-mined data, identified a large number of biological entities associated with CRC. The relative importance of these TF and their associated modules was identified using functional and topological features. Additional validation of these highly-ranked TF using the literature strengthened our findings. Some of the novel TF that we identified were: SLUG, RUNX1, IRF1, HIF1A, ATF-2, ABL1, ELK-1 and GATA-1. Some of these TFs are associated with functional modules in known pathways of CRC, including the Beta-catenin/development, immune response, transcription, and DNA damage pathways. Our methodology of using text mining data and a multi-level, multi-parameter scoring technique was able to identify both known and novel TF that have roles in CRC. Starting with just one TF (SMAD3) in the bait list, the literature mining process identified an additional 116 CRC-associated TFs. Our network-based analysis showed that these TFs all belonged to any of 13 major functional groups that are known to play important roles in CRC. Among these identified TFs, we obtained a novel six-node module consisting of ATF2-P53-JNK1-ELK1-EPHB2-HIF1A, from which the novel JNK1-ELK1 association could potentially be a significant marker for CRC.

Learning in the context of distribution drift

DTIC Science & Technology

2017-05-09

published in the leading data mining journal, Data Mining and Knowledge Discovery (Webb et. al., 2016)1. We have shown that the previous qualitative...learner Low-bias learner Aggregated classifier Figure 7: Architecture for learning fr m streaming data in th co text of variable or unknown...Learning limited dependence Bayesian classifiers, in Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD

Enhancements for a Dynamic Data Warehousing and Mining System for Large-scale HSCB Data

DTIC Science & Technology

2016-07-20

Intelligent Automation Incorporated Enhancements for a Dynamic Data Warehousing and Mining ...Page | 2 Intelligent Automation Incorporated Monthly Report No. 4 Enhancements for a Dynamic Data Warehousing and Mining System Large-Scale HSCB...including Top Videos, Top Users, Top Words, and Top Languages, and also applied NER to the text associated with YouTube posts. We have also developed UI for

Enhancements for a Dynamic Data Warehousing and Mining System for Large-Scale HSCB Data

DTIC Science & Technology

2016-07-20

Intelligent Automation Incorporated Enhancements for a Dynamic Data Warehousing and Mining ...Page | 2 Intelligent Automation Incorporated Monthly Report No. 4 Enhancements for a Dynamic Data Warehousing and Mining System Large-Scale HSCB...including Top Videos, Top Users, Top Words, and Top Languages, and also applied NER to the text associated with YouTube posts. We have also developed UI for

Learning Semantic Tags from Big Data for Clinical Text Representation.

PubMed

Li, Yanpeng; Liu, Hongfang

2015-01-01

In clinical text mining, it is one of the biggest challenges to represent medical terminologies and n-gram terms in sparse medical reports using either supervised or unsupervised methods. Addressing this issue, we propose a novel method for word and n-gram representation at semantic level. We first represent each word by its distance with a set of reference features calculated by reference distance estimator (RDE) learned from labeled and unlabeled data, and then generate new features using simple techniques of discretization, random sampling and merging. The new features are a set of binary rules that can be interpreted as semantic tags derived from word and n-grams. We show that the new features significantly outperform classical bag-of-words and n-grams in the task of heart disease risk factor extraction in i2b2 2014 challenge. It is promising to see that semantics tags can be used to replace the original text entirely with even better prediction performance as well as derive new rules beyond lexical level.

Mapping informal small-scale mining features in a data-sparse tropical environment with a small UAS

USGS Publications Warehouse

Chirico, Peter G.; Dewitt, Jessica D.

2017-01-01

This study evaluates the use of a small unmanned aerial system (UAS) to collect imagery over artisanal mining sites in West Africa. The purpose of this study is to consider how very high-resolution imagery and digital surface models (DSMs) derived from structure-from-motion (SfM) photogrammetric techniques from a small UAS can fill the gap in geospatial data collection between satellite imagery and data gathered during field work to map and monitor informal mining sites in tropical environments. The study compares both wide-angle and narrow field of view camera systems in the collection and analysis of high-resolution orthoimages and DSMs of artisanal mining pits. The results of the study indicate that UAS imagery and SfM photogrammetric techniques permit DSMs to be produced with a high degree of precision and relative accuracy, but highlight the challenges of mapping small artisanal mining pits in remote and data sparse terrain.

Computer-aided visual assessment in mine planning and design

Treesearch

Michael Hatfield; A. J. LeRoy Balzer; Roger E. Nelson

1979-01-01

A computer modeling technique is described for evaluating the visual impact of a proposed surface mine located within the viewshed of a national park. A computer algorithm analyzes digitized USGS baseline topography and identifies areas subject to surface disturbance visible from the park. Preliminary mine and reclamation plan information is used to describe how the...

Learner Typologies Development Using OIndex and Data Mining Based Clustering Techniques

ERIC Educational Resources Information Center

Luan, Jing

2004-01-01

This explorative data mining project used distance based clustering algorithm to study 3 indicators, called OIndex, of student behavioral data and stabilized at a 6-cluster scenario following an exhaustive explorative study of 4, 5, and 6 cluster scenarios produced by K-Means and TwoStep algorithms. Using principles in data mining, the study…

A Quantitative Analysis of Organizational Factors That Relate to Data Mining Success

ERIC Educational Resources Information Center

Huebner, Richard A.

2017-01-01

The ubiquity of data in various forms has fueled the need for advanced data-mining techniques within organizations. The advent of data mining methods used to uncover hidden nuggets of information buried within large data sets has also fueled the need for determining how these unique projects can be successful. There are many challenges associated…

Educational Data Mining Applications and Tasks: A Survey of the Last 10 Years

ERIC Educational Resources Information Center

Bakhshinategh, Behdad; Zaiane, Osmar R.; ElAtia, Samira; Ipperciel, Donald

2018-01-01

Educational Data Mining (EDM) is the field of using data mining techniques in educational environments. There exist various methods and applications in EDM which can follow both applied research objectives such as improving and enhancing learning quality, as well as pure research objectives, which tend to improve our understanding of the learning…

Database citation in full text biomedical articles.

PubMed

Kafkas, Şenay; Kim, Jee-Hyub; McEntyre, Johanna R

2013-01-01

Molecular biology and literature databases represent essential infrastructure for life science research. Effective integration of these data resources requires that there are structured cross-references at the level of individual articles and biological records. Here, we describe the current patterns of how database entries are cited in research articles, based on analysis of the full text Open Access articles available from Europe PMC. Focusing on citation of entries in the European Nucleotide Archive (ENA), UniProt and Protein Data Bank, Europe (PDBe), we demonstrate that text mining doubles the number of structured annotations of database record citations supplied in journal articles by publishers. Many thousands of new literature-database relationships are found by text mining, since these relationships are also not present in the set of articles cited by database records. We recommend that structured annotation of database records in articles is extended to other databases, such as ArrayExpress and Pfam, entries from which are also cited widely in the literature. The very high precision and high-throughput of this text-mining pipeline makes this activity possible both accurately and at low cost, which will allow the development of new integrated data services.

Database Citation in Full Text Biomedical Articles

PubMed Central

Kafkas, Şenay; Kim, Jee-Hyub; McEntyre, Johanna R.

2013-01-01

Molecular biology and literature databases represent essential infrastructure for life science research. Effective integration of these data resources requires that there are structured cross-references at the level of individual articles and biological records. Here, we describe the current patterns of how database entries are cited in research articles, based on analysis of the full text Open Access articles available from Europe PMC. Focusing on citation of entries in the European Nucleotide Archive (ENA), UniProt and Protein Data Bank, Europe (PDBe), we demonstrate that text mining doubles the number of structured annotations of database record citations supplied in journal articles by publishers. Many thousands of new literature-database relationships are found by text mining, since these relationships are also not present in the set of articles cited by database records. We recommend that structured annotation of database records in articles is extended to other databases, such as ArrayExpress and Pfam, entries from which are also cited widely in the literature. The very high precision and high-throughput of this text-mining pipeline makes this activity possible both accurately and at low cost, which will allow the development of new integrated data services. PMID:23734176

HPIminer: A text mining system for building and visualizing human protein interaction networks and pathways.

PubMed

Subramani, Suresh; Kalpana, Raja; Monickaraj, Pankaj Moses; Natarajan, Jeyakumar

2015-04-01

The knowledge on protein-protein interactions (PPI) and their related pathways are equally important to understand the biological functions of the living cell. Such information on human proteins is highly desirable to understand the mechanism of several diseases such as cancer, diabetes, and Alzheimer's disease. Because much of that information is buried in biomedical literature, an automated text mining system for visualizing human PPI and pathways is highly desirable. In this paper, we present HPIminer, a text mining system for visualizing human protein interactions and pathways from biomedical literature. HPIminer extracts human PPI information and PPI pairs from biomedical literature, and visualize their associated interactions, networks and pathways using two curated databases HPRD and KEGG. To our knowledge, HPIminer is the first system to build interaction networks from literature as well as curated databases. Further, the new interactions mined only from literature and not reported earlier in databases are highlighted as new. A comparative study with other similar tools shows that the resultant network is more informative and provides additional information on interacting proteins and their associated networks. Copyright © 2015 Elsevier Inc. All rights reserved.

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.

Facilitating Decision Making, Re-Use and Collaboration: A Knowledge Management Approach to Acquisition Program Self-Awareness

DTIC Science & Technology

2009-06-01

capabilities: web-based, relational/multi-dimensional, client/server, and metadata (data about data) inclusion (pp. 39-40). Text mining, on the other...and Organizational Systems ( CASOS ) (Carley, 2005). Although AutoMap can be used to conduct text-mining, it was utilized only for its visualization...provides insight into how the GMCOI is using the terms, and where there might be redundant terms and need for de -confliction and standardization

VIOLENT FRAMES IN ACTION

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

Sanfilippo, Antonio P.; McGrath, Liam R.; Whitney, Paul D.

2011-11-17

We present a computational approach to radical rhetoric that leverages the co-expression of rhetoric and action features in discourse to identify violent intent. The approach combines text mining and machine learning techniques with insights from Frame Analysis and theories that explain the emergence of violence in terms of moral disengagement, the violation of sacred values and social isolation in order to build computational models that identify messages from terrorist sources and estimate their proximity to an attack. We discuss a specific application of this approach to a body of documents from and about radical and terrorist groups in the Middlemore » East and present the results achieved.« less

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

Stopping Antidepressants and Anxiolytics as Major Concerns Reported in Online Health Communities: A Text Mining Approach.

PubMed

Abbe, Adeline; Falissard, Bruno

2017-10-23

Internet is a particularly dynamic way to quickly capture the perceptions of a population in real time. Complementary to traditional face-to-face communication, online social networks help patients to improve self-esteem and self-help. The aim of this study was to use text mining on material from an online forum exploring patients' concerns about treatment (antidepressants and anxiolytics). Concerns about treatment were collected from discussion titles in patients' online community related to antidepressants and anxiolytics. To examine the content of these titles automatically, we used text mining methods, such as word frequency in a document-term matrix and co-occurrence of words using a network analysis. It was thus possible to identify topics discussed on the forum. The forum included 2415 discussions on antidepressants and anxiolytics over a period of 3 years. After a preprocessing step, the text mining algorithm identified the 99 most frequently occurring words in titles, among which were escitalopram, withdrawal, antidepressant, venlafaxine, paroxetine, and effect. Patients' concerns were related to antidepressant withdrawal, the need to share experience about symptoms, effects, and questions on weight gain with some drugs. Patients' expression on the Internet is a potential additional resource in addressing patients' concerns about treatment. Patient profiles are close to that of patients treated in psychiatry. ©Adeline Abbe, Bruno Falissard. Originally published in JMIR Mental Health (http://mental.jmir.org), 23.10.2017.

Coronary artery disease risk assessment from unstructured electronic health records using text mining.

PubMed

Jonnagaddala, Jitendra; Liaw, Siaw-Teng; Ray, Pradeep; Kumar, Manish; Chang, Nai-Wen; Dai, Hong-Jie

2015-12-01

Coronary artery disease (CAD) often leads to myocardial infarction, which may be fatal. Risk factors can be used to predict CAD, which may subsequently lead to prevention or early intervention. Patient data such as co-morbidities, medication history, social history and family history are required to determine the risk factors for a disease. However, risk factor data are usually embedded in unstructured clinical narratives if the data is not collected specifically for risk assessment purposes. Clinical text mining can be used to extract data related to risk factors from unstructured clinical notes. This study presents methods to extract Framingham risk factors from unstructured electronic health records using clinical text mining and to calculate 10-year coronary artery disease risk scores in a cohort of diabetic patients. We developed a rule-based system to extract risk factors: age, gender, total cholesterol, HDL-C, blood pressure, diabetes history and smoking history. The results showed that the output from the text mining system was reliable, but there was a significant amount of missing data to calculate the Framingham risk score. A systematic approach for understanding missing data was followed by implementation of imputation strategies. An analysis of the 10-year Framingham risk scores for coronary artery disease in this cohort has shown that the majority of the diabetic patients are at moderate risk of CAD. Copyright © 2015 Elsevier Inc. All rights reserved.

LimTox: a web tool for applied text mining of adverse event and toxicity associations of compounds, drugs and genes.

PubMed

Cañada, Andres; Capella-Gutierrez, Salvador; Rabal, Obdulia; Oyarzabal, Julen; Valencia, Alfonso; Krallinger, Martin

2017-07-03

A considerable effort has been devoted to retrieve systematically information for genes and proteins as well as relationships between them. Despite the importance of chemical compounds and drugs as a central bio-entity in pharmacological and biological research, only a limited number of freely available chemical text-mining/search engine technologies are currently accessible. Here we present LimTox (Literature Mining for Toxicology), a web-based online biomedical search tool with special focus on adverse hepatobiliary reactions. It integrates a range of text mining, named entity recognition and information extraction components. LimTox relies on machine-learning, rule-based, pattern-based and term lookup strategies. This system processes scientific abstracts, a set of full text articles and medical agency assessment reports. Although the main focus of LimTox is on adverse liver events, it enables also basic searches for other organ level toxicity associations (nephrotoxicity, cardiotoxicity, thyrotoxicity and phospholipidosis). This tool supports specialized search queries for: chemical compounds/drugs, genes (with additional emphasis on key enzymes in drug metabolism, namely P450 cytochromes-CYPs) and biochemical liver markers. The LimTox website is free and open to all users and there is no login requirement. LimTox can be accessed at: http://limtox.bioinfo.cnio.es. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Application of Modern Tools and Techniques for Mine Safety & Disaster Management

NASA Astrophysics Data System (ADS)

Kumar, Dheeraj

2016-04-01

The implementation of novel systems and adoption of improvised equipment in mines help mining companies in two important ways: enhanced mine productivity and improved worker safety. There is a substantial need for adoption of state-of-the-art automation technologies in the mines to ensure the safety and to protect health of mine workers. With the advent of new autonomous equipment used in the mine, the inefficiencies are reduced by limiting human inconsistencies and error. The desired increase in productivity at a mine can sometimes be achieved by changing only a few simple variables. Significant developments have been made in the areas of surface and underground communication, robotics, smart sensors, tracking systems, mine gas monitoring systems and ground movements etc. Advancement in information technology in the form of internet, GIS, remote sensing, satellite communication, etc. have proved to be important tools for hazard reduction and disaster management. This paper is mainly focused on issues pertaining to mine safety and disaster management and some of the recent innovations in the mine automations that could be deployed in mines for safe mining operations and for avoiding any unforeseen mine disaster.

Text Mining for Drugs and Chemical Compounds: Methods, Tools and Applications.

PubMed

Vazquez, Miguel; Krallinger, Martin; Leitner, Florian; Valencia, Alfonso

2011-06-01

Providing prior knowledge about biological properties of chemicals, such as kinetic values, protein targets, or toxic effects, can facilitate many aspects of drug development. Chemical information is rapidly accumulating in all sorts of free text documents like patents, industry reports, or scientific articles, which has motivated the development of specifically tailored text mining applications. Despite the potential gains, chemical text mining still faces significant challenges. One of the most salient is the recognition of chemical entities mentioned in text. To help practitioners contribute to this area, a good portion of this review is devoted to this issue, and presents the basic concepts and principles underlying the main strategies. The technical details are introduced and accompanied by relevant bibliographic references. Other tasks discussed are retrieving relevant articles, identifying relationships between chemicals and other entities, or determining the chemical structures of chemicals mentioned in text. This review also introduces a number of published applications that can be used to build pipelines in topics like drug side effects, toxicity, and protein-disease-compound network analysis. We conclude the review with an outlook on how we expect the field to evolve, discussing its possibilities and its current limitations. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Mining free-text medical records for companion animal enteric syndrome surveillance.

PubMed

Anholt, R M; Berezowski, J; Jamal, I; Ribble, C; Stephen, C

2014-03-01

Large amounts of animal health care data are present in veterinary electronic medical records (EMR) and they present an opportunity for companion animal disease surveillance. Veterinary patient records are largely in free-text without clinical coding or fixed vocabulary. Text-mining, a computer and information technology application, is needed to identify cases of interest and to add structure to the otherwise unstructured data. In this study EMR's were extracted from veterinary management programs of 12 participating veterinary practices and stored in a data warehouse. Using commercially available text-mining software (WordStat™), we developed a categorization dictionary that could be used to automatically classify and extract enteric syndrome cases from the warehoused electronic medical records. The diagnostic accuracy of the text-miner for retrieving cases of enteric syndrome was measured against human reviewers who independently categorized a random sample of 2500 cases as enteric syndrome positive or negative. Compared to the reviewers, the text-miner retrieved cases with enteric signs with a sensitivity of 87.6% (95%CI, 80.4-92.9%) and a specificity of 99.3% (95%CI, 98.9-99.6%). Automatic and accurate detection of enteric syndrome cases provides an opportunity for community surveillance of enteric pathogens in companion animals. Copyright © 2014 Elsevier B.V. All rights reserved.

Challenges in recovering resources from acid mine drainage

USGS Publications Warehouse

Nordstrom, D. Kirk; Bowell, Robert J.; Campbell, Kate M.; Alpers, Charles N.

2017-01-01

Metal recovery from mine waters and effluents is not a new approach but one that has occurred largely opportunistically over the last four millennia. Due to the need for low-cost resources and increasingly stringent environmental conditions, mine waters are being considered in a fresh light with a designed, deliberate approach to resource recovery often as part of a larger water treatment evaluation. Mine water chemistry is highly dependent on many factors including geology, ore deposit composition and mineralogy, mining methods, climate, site hydrology, and others. Mine waters are typically Ca-Mg-SO4±Al±Fe with a broad range in pH and metal content. The main issue in recovering components of these waters having potential economic value, such as base metals or rare earth elements, is the separation of these from more reactive metals such as Fe and Al. Broad categories of methods for separating and extracting substances from acidic mine drainage are chemical and biological. Chemical methods include solution, physicochemical, and electrochemical technologies. Advances in membrane techniques such as reverse osmosis have been substantial and the technique is both physical and chemical. Biological methods may be further divided into microbiological and macrobiological, but only the former is considered here as a recovery method, as the latter is typically used as a passive form of water treatment.

Data mining in radiology

PubMed Central

Kharat, Amit T; Singh, Amarjit; Kulkarni, Vilas M; Shah, Digish

2014-01-01

Data mining facilitates the study of radiology data in various dimensions. It converts large patient image and text datasets into useful information that helps in improving patient care and provides informative reports. Data mining technology analyzes data within the Radiology Information System and Hospital Information System using specialized software which assesses relationships and agreement in available information. By using similar data analysis tools, radiologists can make informed decisions and predict the future outcome of a particular imaging finding. Data, information and knowledge are the components of data mining. Classes, Clusters, Associations, Sequential patterns, Classification, Prediction and Decision tree are the various types of data mining. Data mining has the potential to make delivery of health care affordable and ensure that the best imaging practices are followed. It is a tool for academic research. Data mining is considered to be ethically neutral, however concerns regarding privacy and legality exists which need to be addressed to ensure success of data mining. PMID:25024513

Analyzing asset management data using data and text mining.

DOT National Transportation Integrated Search

2014-07-01

Predictive models using text from a sample competitively bid California highway projects have been used to predict a construction : projects likely level of cost overrun. A text description of the project and the text of the five largest project line...

Data mining and medical world: breast cancers' diagnosis, treatment, prognosis and challenges.

PubMed

Oskouei, Rozita Jamili; Kor, Nasroallah Moradi; Maleki, Saeid Abbasi

2017-01-01

The amount of data in electronic and real world is constantly on the rise. Therefore, extracting useful knowledge from the total available data is very important and time consuming task. Data mining has various techniques for extracting valuable information or knowledge from data. These techniques are applicable for all data that are collected inall fields of science. Several research investigations are published about applications of data mining in various fields of sciences such as defense, banking, insurances, education, telecommunications, medicine and etc. This investigation attempts to provide a comprehensive survey about applications of data mining techniques in breast cancer diagnosis, treatment & prognosis till now. Further, the main challenges in these area is presented in this investigation. Since several research studies currently are going on in this issues, therefore, it is necessary to have a complete survey about all researches which are completed up to now, along with the results of those studies and important challenges which are currently exist in this area for helping young researchers and presenting to them the main problems that are still exist in this area.

Data mining and medical world: breast cancers’ diagnosis, treatment, prognosis and challenges

PubMed Central

Oskouei, Rozita Jamili; Kor, Nasroallah Moradi; Maleki, Saeid Abbasi

2017-01-01

The amount of data in electronic and real world is constantly on the rise. Therefore, extracting useful knowledge from the total available data is very important and time consuming task. Data mining has various techniques for extracting valuable information or knowledge from data. These techniques are applicable for all data that are collected inall fields of science. Several research investigations are published about applications of data mining in various fields of sciences such as defense, banking, insurances, education, telecommunications, medicine and etc. This investigation attempts to provide a comprehensive survey about applications of data mining techniques in breast cancer diagnosis, treatment & prognosis till now. Further, the main challenges in these area is presented in this investigation. Since several research studies currently are going on in this issues, therefore, it is necessary to have a complete survey about all researches which are completed up to now, along with the results of those studies and important challenges which are currently exist in this area for helping young researchers and presenting to them the main problems that are still exist in this area. PMID:28401016

BioC implementations in Go, Perl, Python and Ruby.

PubMed

Liu, Wanli; Islamaj Doğan, Rezarta; Kwon, Dongseop; Marques, Hernani; Rinaldi, Fabio; Wilbur, W John; Comeau, Donald C

2014-01-01

As part of a communitywide effort for evaluating text mining and information extraction systems applied to the biomedical domain, BioC is focused on the goal of interoperability, currently a major barrier to wide-scale adoption of text mining tools. BioC is a simple XML format, specified by DTD, for exchanging data for biomedical natural language processing. With initial implementations in C++ and Java, BioC provides libraries of code for reading and writing BioC text documents and annotations. We extend BioC to Perl, Python, Go and Ruby. We used SWIG to extend the C++ implementation for Perl and one Python implementation. A second Python implementation and the Ruby implementation use native data structures and libraries. BioC is also implemented in the Google language Go. BioC modules are functional in all of these languages, which can facilitate text mining tasks. BioC implementations are freely available through the BioC site: http://bioc.sourceforge.net. Database URL: http://bioc.sourceforge.net/ Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

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.

Text mining for metabolic pathways, signaling cascades, and protein networks.

PubMed

Hoffmann, Robert; Krallinger, Martin; Andres, Eduardo; Tamames, Javier; Blaschke, Christian; Valencia, Alfonso

2005-05-10

The complexity of the information stored in databases and publications on metabolic and signaling pathways, the high throughput of experimental data, and the growing number of publications make it imperative to provide systems to help the researcher navigate through these interrelated information resources. Text-mining methods have started to play a key role in the creation and maintenance of links between the information stored in biological databases and its original sources in the literature. These links will be extremely useful for database updating and curation, especially if a number of technical problems can be solved satisfactorily, including the identification of protein and gene names (entities in general) and the characterization of their types of interactions. The first generation of openly accessible text-mining systems, such as iHOP (Information Hyperlinked over Proteins), provides additional functions to facilitate the reconstruction of protein interaction networks, combine database and text information, and support the scientist in the formulation of novel hypotheses. The next challenge is the generation of comprehensive information regarding the general function of signaling pathways and protein interaction networks.

Application of the Deformation Information System for automated analysis and mapping of mining terrain deformations - case study from SW Poland

NASA Astrophysics Data System (ADS)

Blachowski, Jan; Grzempowski, Piotr; Milczarek, Wojciech; Nowacka, Anna

2015-04-01

Monitoring, mapping and modelling of mining induced terrain deformations are important tasks for quantifying and minimising threats that arise from underground extraction of useful minerals and affect surface infrastructure, human safety, the environment and security of the mining operation itself. The number of methods and techniques used for monitoring and analysis of mining terrain deformations is wide and expanding with the progress in geographical information technologies. These include for example: terrestrial geodetic measurements, Global Navigation Satellite Systems, remote sensing, GIS based modelling and spatial statistics, finite element method modelling, geological modelling, empirical modelling using e.g. the Knothe theory, artificial neural networks, fuzzy logic calculations and other. The presentation shows the results of numerical modelling and mapping of mining terrain deformations for two cases of underground mining sites in SW Poland, hard coal one (abandoned) and copper ore (active) using the functionalities of the Deformation Information System (DIS) (Blachowski et al, 2014 @ http://meetingorganizer.copernicus.org/EGU2014/EGU2014-7949.pdf). The functionalities of the spatial data modelling module of DIS have been presented and its applications in modelling, mapping and visualising mining terrain deformations based on processing of measurement data (geodetic and GNSS) for these two cases have been characterised and compared. These include, self-developed and implemented in DIS, automation procedures for calculating mining terrain subsidence with different interpolation techniques, calculation of other mining deformation parameters (i.e. tilt, horizontal displacement, horizontal strain and curvature), as well as mapping mining terrain categories based on classification of the values of these parameters as used in Poland. Acknowledgments. This work has been financed from the National Science Centre Project "Development of a numerical method of mining ground deformation modelling in complex geological and mining conditions" UMO-2012/07/B/ST10/04297 executed at the Faculty of Geoengineering, Mining and Geology of the Wroclaw University of Technology (Poland).

Estimating natural background groundwater chemistry, Questa molybdenum mine, New Mexico

USGS Publications Warehouse

Verplanck, Phillip L.; Nordstrom, D. Kirk; Plumlee, Geoffrey S.; Walker, Bruce M.; Morgan, Lisa A.; Quane, Steven L.

2010-01-01

This 2 1/2 day field trip will present an overview of a U.S. Geological Survey (USGS) project whose objective was to estimate pre-mining groundwater chemistry at the Questa molybdenum mine, New Mexico. Because of intense debate among stakeholders regarding pre-mining groundwater chemistry standards, the New Mexico Environment Department and Chevron Mining Inc. (formerly Molycorp) agreed that the USGS should determine pre-mining groundwater quality at the site. In 2001, the USGS began a 5-year, multidisciplinary investigation to estimate pre-mining groundwater chemistry utilizing a detailed assessment of a proximal natural analog site and applied an interdisciplinary approach to infer pre-mining conditions. The trip will include a surface tour of the Questa mine and key locations in the erosion scar areas and along the Red River. The trip will provide participants with a detailed understanding of geochemical processes that influence pre-mining environmental baselines in mineralized areas and estimation techniques for determining pre-mining baseline conditions.

Unmanned Mine of the 21st Centuries

NASA Astrophysics Data System (ADS)

Semykina, Irina; Grigoryev, Aleksandr; Gargayev, Andrey; Zavyalov, Valeriy

2017-11-01

The article is analytical. It considers the construction principles of the automation system structure which realize the concept of «unmanned mine». All of these principles intend to deal with problems caused by a continuous complication of mining-and-geological conditions at coalmine such as the labor safety and health protection, the weak integration of different mining automation subsystems and the deficiency of optimal balance between a quantity of resource and energy consumed by mining machines and their throughput. The authors describe the main problems and neck stage of mining machines autonomation and automation subsystem. The article makes a general survey of the applied «unmanned technology» in the field of mining such as the remotely operated autonomous complexes, the underground positioning systems of mining machines using infrared radiation in mine workings etc. The concept of «unmanned mine» is considered with an example of the robotic road heading machine. In the final, the authors analyze the techniques and methods that could solve the task of underground mining without human labor.

Data Mining Methods for Recommender Systems

NASA Astrophysics Data System (ADS)

Amatriain, Xavier; Jaimes*, Alejandro; Oliver, Nuria; Pujol, Josep M.

In this chapter, we give an overview of the main Data Mining techniques used in the context of Recommender Systems. We first describe common preprocessing methods such as sampling or dimensionality reduction. Next, we review the most important classification techniques, including Bayesian Networks and Support Vector Machines. We describe the k-means clustering algorithm and discuss several alternatives. We also present association rules and related algorithms for an efficient training process. In addition to introducing these techniques, we survey their uses in Recommender Systems and present cases where they have been successfully applied.

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.

Integrated mined-area reclamation and land-use planning. Volume 3C. A case study of surface mining and reclamation planning: Georgia Kaolin Company Clay Mines, Washington County, Georgia

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

Guernsey, J L; Brown, L A; Perry, A O

1978-02-01

This case study examines the reclamation practices of the Georgia Kaolin's American Industrial Clay Company Division, a kaolin producer centered in Twiggs, Washington, and Wilkinson Counties, Georgia. The State of Georgia accounts for more than one-fourth of the world's kaolin production and about three-fourths of U.S. kaolin output. The mining of kaolin in Georgia illustrates the effects of mining and reclaiming lands disturbed by area surface mining. The disturbed areas are reclaimed under the rules and regulations of the Georgia Surface Mining Act of 1968. The natural conditions influencing the reclamation methodologies and techniques are markedly unique from those ofmore » other mining operations. The environmental disturbances and procedures used in reclaiming the kaolin mined lands are reviewed and implications for planners are noted.« less

Mining the Text: 34 Text Features that Can Ease or Obstruct Text Comprehension and Use

ERIC Educational Resources Information Center

White, Sheida

2012-01-01

This article presents 34 characteristics of texts and tasks ("text features") that can make continuous (prose), noncontinuous (document), and quantitative texts easier or more difficult for adolescents and adults to comprehend and use. The text features were identified by examining the assessment tasks and associated texts in the national…

Use of sentiment analysis for capturing patient experience from free-text comments posted online.

PubMed

Greaves, Felix; Ramirez-Cano, Daniel; Millett, Christopher; Darzi, Ara; Donaldson, Liam

2013-11-01

There are large amounts of unstructured, free-text information about quality of health care available on the Internet in blogs, social networks, and on physician rating websites that are not captured in a systematic way. New analytical techniques, such as sentiment analysis, may allow us to understand and use this information more effectively to improve the quality of health care. We attempted to use machine learning to understand patients' unstructured comments about their care. We used sentiment analysis techniques to categorize online free-text comments by patients as either positive or negative descriptions of their health care. We tried to automatically predict whether a patient would recommend a hospital, whether the hospital was clean, and whether they were treated with dignity from their free-text description, compared to the patient's own quantitative rating of their care. We applied machine learning techniques to all 6412 online comments about hospitals on the English National Health Service website in 2010 using Weka data-mining software. We also compared the results obtained from sentiment analysis with the paper-based national inpatient survey results at the hospital level using Spearman rank correlation for all 161 acute adult hospital trusts in England. There was 81%, 84%, and 89% agreement between quantitative ratings of care and those derived from free-text comments using sentiment analysis for cleanliness, being treated with dignity, and overall recommendation of hospital respectively (kappa scores: .40-.74, P<.001 for all). We observed mild to moderate associations between our machine learning predictions and responses to the large patient survey for the three categories examined (Spearman rho 0.37-0.51, P<.001 for all). The prediction accuracy that we have achieved using this machine learning process suggests that we are able to predict, from free-text, a reasonably accurate assessment of patients' opinion about different performance aspects of a hospital and that these machine learning predictions are associated with results of more conventional surveys.

Relation extraction for biological pathway construction using node2vec.

PubMed

Kim, Munui; Baek, Seung Han; Song, Min

2018-06-13

Systems biology is an important field for understanding whole biological mechanisms composed of interactions between biological components. One approach for understanding complex and diverse mechanisms is to analyze biological pathways. However, because these pathways consist of important interactions and information on these interactions is disseminated in a large number of biomedical reports, text-mining techniques are essential for extracting these relationships automatically. In this study, we applied node2vec, an algorithmic framework for feature learning in networks, for relationship extraction. To this end, we extracted genes from paper abstracts using pkde4j, a text-mining tool for detecting entities and relationships. Using the extracted genes, a co-occurrence network was constructed and node2vec was used with the network to generate a latent representation. To demonstrate the efficacy of node2vec in extracting relationships between genes, performance was evaluated for gene-gene interactions involved in a type 2 diabetes pathway. Moreover, we compared the results of node2vec to those of baseline methods such as co-occurrence and DeepWalk. Node2vec outperformed existing methods in detecting relationships in the type 2 diabetes pathway, demonstrating that this method is appropriate for capturing the relatedness between pairs of biological entities involved in biological pathways. The results demonstrated that node2vec is useful for automatic pathway construction.

Use of an automatic resistivity system for detecting abandoned mine workings

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

Peters, W.R.; Burdick, R.G.

1983-01-01

A high-resolution earth resistivity system has been designed and constructed for use as a means of detecting abandoned coal mine workings. The automatic pole-dipole earth resistivity technique has already been applied to the detection of subsurface voids for military applications. The hardware and software of the system are described, together with applications for surveying and mapping abandoned coal mine workings. Field tests are presented to illustrate the detection of both air-filled and water-filled mine workings.

Preventing spontaneous combustion after mine closing

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

Lewicki, G.

1987-11-01

The author explains how the Northern Coal Company and a Houston-based firefighting firm developed an innovative technique to reduce the risk of spontaneous combustion after mine closing in its Rienau number2 Mine. The ''Light Water TM'' ATC series of firefighting foam concentrates were designed for extinguishing flammable liquid fires. By slightly altering the chemicals, the concentrates could be used to seal the coal ribs, floor, and roof, reducing the risk of combustion. Subsequent monitoring of the mine has identified no signs of heating.

Artisanal and Small-Scale Gold Mining Without Mercury

EPA Pesticide Factsheets

Mercury-free techniques are safer for miners, their families and local communities. They can also help miners qualify for certification under fair-mined standards, potentially allowing them to market their gold at higher prices.

The Pollution Detectives: Part II. Lead and Zinc Mining.

ERIC Educational Resources Information Center

Sanderson, P. L.

1988-01-01

Describes a field trip taken to an old mining area to study water pollution. Discussed are methods for silt analysis, reagent preparation, color charts, techniques, fieldwork, field results, and a laboratory study. (CW)

A demonstration of ERTS-1 analog and digital techniques applied to strip mining in Maryland and West Virginia

NASA Technical Reports Server (NTRS)

Anderson, A. T.; Schubert, J.

1974-01-01

The largest contour strip mining operations in western Maryland and West Virginia are located within the Georges Creek and the Upper Potomac Basins. These two coal basins lie within the Georges Creek (Wellersburg) syncline. The disturbed strip mine areas were delineated with the surrounding geological and vegetation features using ERTS-1 data in both analog (imagery) and digital form. The two digital systems used were: (1) the ERTS-Analysis system, a point-by-point digital analysis of spectral signatures based on known spectral values, and (2) the LARS Automatic Data Processing System. The digital techniques being developed will later be incorporated into a data base for land use planning. These two systems aided in efforts to determine the extent and state of strip mining in this region. Aircraft data, ground verification information, and geological field studies also aided in the application of ERTS-1 imagery to perform an integrated analysis that assessed the adverse effects of strip mining. The results indicated that ERTS can both monitor and map the extent of strip mining to determine immediately the acreage affected and indicate where future reclamation and revegetation may be necessary.

An integrated environment monitoring system for underground coal mines--Wireless Sensor Network subsystem with multi-parameter monitoring.

PubMed

Zhang, Yu; Yang, Wei; Han, Dongsheng; Kim, Young-Il

2014-07-21

Environment monitoring is important for the safety of underground coal mine production, and it is also an important application of Wireless Sensor Networks (WSNs). We put forward an integrated environment monitoring system for underground coal mine, which uses the existing Cable Monitoring System (CMS) as the main body and the WSN with multi-parameter monitoring as the supplementary technique. As CMS techniques are mature, this paper mainly focuses on the WSN and the interconnection between the WSN and the CMS. In order to implement the WSN for underground coal mines, two work modes are designed: periodic inspection and interrupt service; the relevant supporting technologies, such as routing mechanism, collision avoidance, data aggregation, interconnection with the CMS, etc., are proposed and analyzed. As WSN nodes are limited in energy supply, calculation and processing power, an integrated network management scheme is designed in four aspects, i.e., topology management, location management, energy management and fault management. Experiments were carried out both in a laboratory and in a real underground coal mine. The test results indicate that the proposed integrated environment monitoring system for underground coal mines is feasible and all designs performed well as expected.

75 FR 8316 - Office of Postsecondary Education; Overview Information; Erma Byrd Scholarship Program; Notice...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-24

... Transmittal of Applications: March 26, 2010. Full Text of Announcement I. Funding Opportunity Description... related to industrial health and safety: Mining and mineral engineering, industrial engineering... technology/technician, hazardous materials information systems technology/technician, mining technology...

Regional and temporal variability of the isotope composition (O, S) of atmospheric sulphate in the region of Freiberg, Germany, and consequences for dissolved sulphate in groundwater and river water.

PubMed

Tichomirowa, Marion; Heidel, Claudia

2012-01-01

The isotope composition of dissolved sulphate and strontium in atmospheric deposition, groundwater, mine water and river water in the region of Freiberg was investigated to better understand the fate of these components in the regional and global water cycle. Most of the isotope variations of dissolved sulphates in atmospheric deposition from three locations sampled bi- or tri-monthly can be explained by fractionation processes leading to lower [Formula: see text] (of about 2-3‰) and higher [Formula: see text] (of about 8-10‰) values in summer compared with the winter period. These samples showed a negative correlation between [Formula: see text] and [Formula: see text] values and a weak positive correlation between [Formula: see text] and [Formula: see text] values. They reflect the sulphate formed by aqueous oxidation from long-range transport in clouds. However, these isotope variations were superimposed by changes of the dominating atmospheric sulphate source. At two of the sampling points, large variations of mean annual [Formula: see text] values from atmospheric bulk deposition were recorded. From 2008 to 2009, the mean annual [Formula: see text] value increased by about 5‰; and decreased by about 4‰ from 2009 to 2010. A change in the dominating sulphate source or oxidation pathways of SO(2) in the atmosphere is proposed to cause these shifts. No changes were found in corresponding [Formula: see text] values. Groundwater, river water and some mine waters (where groundwater was the dominating sulphate source) also showed temporal shifts in their [Formula: see text] values corresponding to those of bulk atmospheric deposition, albeit to a lower degree. The mean transit time of atmospheric sulphur through the soil into the groundwater and river water was less than a year and therefore much shorter than previously suggested. Mining activities of about 800 years in the Freiberg region may have led to large subsurface areas with an enhanced groundwater flow along fractures and mined-refilled ore lodes which may shorten transit times of sulphate from precipitation through groundwater into river water.

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.

Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.

PubMed

Nikfarjam, Azadeh; Sarker, Abeed; O'Connor, Karen; Ginn, Rachel; Gonzalez, Graciela

2015-05-01

Social media is becoming increasingly popular as a platform for sharing personal health-related information. This information can be utilized for public health monitoring tasks, particularly for pharmacovigilance, via the use of natural language processing (NLP) techniques. However, the language in social media is highly informal, and user-expressed medical concepts are often nontechnical, descriptive, and challenging to extract. There has been limited progress in addressing these challenges, and thus far, advanced machine learning-based NLP techniques have been underutilized. Our objective is to design a machine learning-based approach to extract mentions of adverse drug reactions (ADRs) from highly informal text in social media. We introduce ADRMine, a machine learning-based concept extraction system that uses conditional random fields (CRFs). ADRMine utilizes a variety of features, including a novel feature for modeling words' semantic similarities. The similarities are modeled by clustering words based on unsupervised, pretrained word representation vectors (embeddings) generated from unlabeled user posts in social media using a deep learning technique. ADRMine outperforms several strong baseline systems in the ADR extraction task by achieving an F-measure of 0.82. Feature analysis demonstrates that the proposed word cluster features significantly improve extraction performance. It is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Our approach is particularly scalable, suitable for social media mining, as it relies on large volumes of unlabeled data, thus diminishing the need for large, annotated training data sets. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

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.

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.

Growth and Heavy Metal Accumulation of Koelreuteria Paniculata Seedlings and Their Potential for Restoring Manganese Mine Wastelands in Hunan, China

PubMed Central

Huang, Zhihong; Xiang, Wenhua; Ma, Yu’e; Lei, Pifeng; Tian, Dalun; Deng, Xiangwen; Yan, Wende; Fang, Xi

2015-01-01

The planting of trees on mine wastelands is an effective, long-term technique for phytoremediation of heavy metal-contaminated wastes. In this study, a pot experiment with seedlings of Koelreuteria paniculata under six treatments of local mine wastes was designed to determine the major constraints on tree establishment and to evaluate the feasibility of planting K. paniculata on manganese mine wastelands. Results showed that K. paniculata grew well in mine tailings, and also under a regime of equal amounts of mine tailings and soil provided in adjacent halves of pots. In contrast, mine sludge did not favor survival and growth because its clay texture limited fine root development. The bio-concentration factor and the translocation factor were mostly less than 1, indicating a low phytoextraction potential for K. paniculata. K. paniculata is suited to restore manganese mine sludge by mixing the mine sludge with local mine tailings or soil. PMID:25654773

Monitoring of the mercury mining site Almadén implementing remote sensing technologies.

PubMed

Schmid, Thomas; Rico, Celia; Rodríguez-Rastrero, Manuel; José Sierra, María; Javier Díaz-Puente, Fco; Pelayo, Marta; Millán, Rocio

2013-08-01

The Almadén area in Spain has a long history of mercury mining with prolonged human-induced activities that are related to mineral extraction and metallurgical processes before the closure of the mines and a more recent post period dominated by projects that reclaim the mine dumps and tailings and recuperating the entire mining area. Furthermore, socio-economic alternatives such as crop cultivation, livestock breeding and tourism are increasing in the area. Up till now, only scattered information on these activities is available from specific studies. However, improved acquisition systems using satellite borne data in the last decades opens up new possibilities to periodically study an area of interest. Therefore, comparing the influence of these activities on the environment and monitoring their impact on the ecosystem vastly improves decision making for the public policy makers to implement appropriate land management measures and control environmental degradation. The objective of this work is to monitor environmental changes affected by human-induced activities within the Almadén area occurring before, during and after the mine closure over a period of nearly three decades. To achieve this, data from numerous sources at different spatial scales and time periods are implemented into a methodology based on advanced remote sensing techniques. This includes field spectroradiometry measurements, laboratory analyses and satellite borne data of different surface covers to detect land cover and use changes throughout the mining area. Finally, monitoring results show that the distribution of areas affected by mercury mining is rapidly diminishing since activities ceased and that rehabilitated mining areas form a new landscape. This refers to mine tailings that have been sealed and revegetated as well as an open pit mine that has been converted to an "artificial" lake surface. Implementing a methodology based on remote sensing techniques that integrate data from several sources at different scales greatly improves the regional characterization and monitoring of an area dominated by mercury mining activities. Copyright © 2013 Elsevier Inc. All rights reserved.

Data Visualization in Information Retrieval and Data Mining (SIG VIS).

ERIC Educational Resources Information Center

Efthimiadis, Efthimis

2000-01-01

Presents abstracts that discuss using data visualization for information retrieval and data mining, including immersive information space and spatial metaphors; spatial data using multi-dimensional matrices with maps; TREC (Text Retrieval Conference) experiments; users' information needs in cartographic information retrieval; and users' relevance…

Macromolecule mass spectrometry: citation mining of user documents.

PubMed

Kostoff, Ronald N; Bedford, Clifford D; del Río, J Antonio; Cortes, Héctor D; Karypis, George

2004-03-01

Identifying research users, applications, and impact is important for research performers, managers, evaluators, and sponsors. Identification of the user audience and the research impact is complex and time consuming due to the many indirect pathways through which fundamental research can impact applications. This paper identified the literature pathways through which two highly-cited papers of 2002 Chemistry Nobel Laureates Fenn and Tanaka impacted research, technology development, and applications. Citation Mining, an integration of citation bibliometrics and text mining, was applied to the >1600 first generation Science Citation Index (SCI) citing papers to Fenn's 1989 Science paper on Electrospray Ionization for Mass Spectrometry, and to the >400 first generation SCI citing papers to Tanaka's 1988 Rapid Communications in Mass Spectrometry paper on Laser Ionization Time-of-Flight Mass Spectrometry. Bibliometrics was performed on the citing papers to profile the user characteristics. Text mining was performed on the citing papers to identify the technical areas impacted by the research, and the relationships among these technical areas.

Enhancements for a Dynamic Data Warehousing and Mining System for Large-Scale Human Social Cultural Behavioral (HSBC) Data

DTIC Science & Technology

2016-09-26

Intelligent Automation Incorporated Enhancements for a Dynamic Data Warehousing and Mining ...Enhancements for a Dynamic Data Warehousing and Mining System for N00014-16-P-3014 Large-Scale Human Social Cultural Behavioral (HSBC) Data 5b. GRANT NUMBER...Representative Media Gallery View. We perform Scraawl’s NER algorithm to the text associated with YouTube post, which classifies the named entities into

Detecting Malicious Tweets in Twitter Using Runtime Monitoring With Hidden Information

DTIC Science & Technology

2016-06-01

text mining using Twitter streaming API and python [Online]. Available: http://adilmoujahid.com/posts/2014/07/twitter-analytics/ [22] M. Singh, B...sites with 645,750,000 registered users [3] and has open source public tweets for data mining . 2. Malicious Users and Tweets In the modern world...want to data mine in Twitter, and presents the natural language assertions and corresponding rule patterns. It then describes the steps performed using

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.

Method to Select Technical Terms for Glossaries in Support of Joint Task Force Operations

DTIC Science & Technology

2012-01-01

have been prohibitively time-consuming. Instead, we identified two publicly available terminology extractor tools: TerMine (NaCTEM, 2011) and Alchemy ...and that from the latter, by high recall. The Alchemy approach contrasts with that used in TerMine in that Alchemy will process the text with...information categories, such as person, location, and organization, in addition to returning topic keywords. Output from both TerMine and Alchemy

Identifying Understudied Nuclear Reactions by Text-mining the EXFOR Experimental Nuclear Reaction Library

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

Hirdt, J.A.; Brown, D.A., E-mail: dbrown@bnl.gov

The EXFOR library contains the largest collection of experimental nuclear reaction data available as well as the data's bibliographic information and experimental details. We text-mined the REACTION and MONITOR fields of the ENTRYs in the EXFOR library in order to identify understudied reactions and quantities. Using the results of the text-mining, we created an undirected graph from the EXFOR datasets with each graph node representing a single reaction and quantity and graph links representing the various types of connections between these reactions and quantities. This graph is an abstract representation of the connections in EXFOR, similar to graphs of socialmore » networks, authorship networks, etc. We use various graph theoretical tools to identify important yet understudied reactions and quantities in EXFOR. Although we identified a few cross sections relevant for shielding applications and isotope production, mostly we identified charged particle fluence monitor cross sections. As a side effect of this work, we learn that our abstract graph is typical of other real-world graphs.« less

Systematic analysis of molecular mechanisms for HCC metastasis via text mining approach.

PubMed

Zhen, Cheng; Zhu, Caizhong; Chen, Haoyang; Xiong, Yiru; Tan, Junyuan; Chen, Dong; Li, Jin

2017-02-21

To systematically explore the molecular mechanism for hepatocellular carcinoma (HCC) metastasis and identify regulatory genes with text mining methods. Genes with highest frequencies and significant pathways related to HCC metastasis were listed. A handful of proteins such as EGFR, MDM2, TP53 and APP, were identified as hub nodes in PPI (protein-protein interaction) network. Compared with unique genes for HBV-HCCs, genes particular to HCV-HCCs were less, but may participate in more extensive signaling processes. VEGFA, PI3KCA, MAPK1, MMP9 and other genes may play important roles in multiple phenotypes of metastasis. Genes in abstracts of HCC-metastasis literatures were identified. Word frequency analysis, KEGG pathway and PPI network analysis were performed. Then co-occurrence analysis between genes and metastasis-related phenotypes were carried out. Text mining is effective for revealing potential regulators or pathways, but the purpose of it should be specific, and the combination of various methods will be more useful.

Unapparent Information Revelation: Text Mining for Counterterrorism

NASA Astrophysics Data System (ADS)

Srihari, Rohini K.

Unapparent information revelation (UIR) is a special case of text mining that focuses on detecting possible links between concepts across multiple text documents by generating an evidence trail explaining the connection. A traditional search involving, for example, two or more person names will attempt to find documents mentioning both these individuals. This research focuses on a different interpretation of such a query: what is the best evidence trail across documents that explains a connection between these individuals? For example, all may be good golfers. A generalization of this task involves query terms representing general concepts (e.g. indictment, foreign policy). Previous approaches to this problem have focused on graph mining involving hyperlinked documents, and link analysis exploiting named entities. A new robust framework is presented, based on (i) generating concept chain graphs, a hybrid content representation, (ii) performing graph matching to select candidate subgraphs, and (iii) subsequently using graphical models to validate hypotheses using ranked evidence trails. We adapt the DUC data set for cross-document summarization to evaluate evidence trails generated by this approach

Identifying Understudied Nuclear Reactions by Text-mining the EXFOR Experimental Nuclear Reaction Library

NASA Astrophysics Data System (ADS)

Hirdt, J. A.; Brown, D. A.

2016-01-01

The EXFOR library contains the largest collection of experimental nuclear reaction data available as well as the data's bibliographic information and experimental details. We text-mined the REACTION and MONITOR fields of the ENTRYs in the EXFOR library in order to identify understudied reactions and quantities. Using the results of the text-mining, we created an undirected graph from the EXFOR datasets with each graph node representing a single reaction and quantity and graph links representing the various types of connections between these reactions and quantities. This graph is an abstract representation of the connections in EXFOR, similar to graphs of social networks, authorship networks, etc. We use various graph theoretical tools to identify important yet understudied reactions and quantities in EXFOR. Although we identified a few cross sections relevant for shielding applications and isotope production, mostly we identified charged particle fluence monitor cross sections. As a side effect of this work, we learn that our abstract graph is typical of other real-world graphs.

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.

Assessing the Ability of Hyperspectral Data to Detect Lyngbya SPP.: A Potential Biological Indicator for Presence of Metal Objects in the Littoral Environment

DTIC Science & Technology

2006-12-01

environment. This concept would have potential benefits and applications in mine detection and countermeasure techniques. Using a USB2000 field...be distinguished by a different phycocyanin absorption, at 615-632 nm. 15. NUMBER OF PAGES 261 14. SUBJECT TERMS Hyperspectral... applications in mine detection and countermeasure techniques. Using a USB2000 field spectroradiometer, a spectral library was developed for the

The effects of different representations on static structure analysis of computer malware signatures.

PubMed

Narayanan, Ajit; Chen, Yi; Pang, Shaoning; Tao, Ban

2013-01-01

The continuous growth of malware presents a problem for internet computing due to increasingly sophisticated techniques for disguising malicious code through mutation and the time required to identify signatures for use by antiviral software systems (AVS). Malware modelling has focused primarily on semantics due to the intended actions and behaviours of viral and worm code. The aim of this paper is to evaluate a static structure approach to malware modelling using the growing malware signature databases now available. We show that, if malware signatures are represented as artificial protein sequences, it is possible to apply standard sequence alignment techniques in bioinformatics to improve accuracy of distinguishing between worm and virus signatures. Moreover, aligned signature sequences can be mined through traditional data mining techniques to extract metasignatures that help to distinguish between viral and worm signatures. All bioinformatics and data mining analysis were performed on publicly available tools and Weka.

The Effects of Different Representations on Static Structure Analysis of Computer Malware Signatures

PubMed Central

Narayanan, Ajit; Chen, Yi; Pang, Shaoning; Tao, Ban

2013-01-01

The continuous growth of malware presents a problem for internet computing due to increasingly sophisticated techniques for disguising malicious code through mutation and the time required to identify signatures for use by antiviral software systems (AVS). Malware modelling has focused primarily on semantics due to the intended actions and behaviours of viral and worm code. The aim of this paper is to evaluate a static structure approach to malware modelling using the growing malware signature databases now available. We show that, if malware signatures are represented as artificial protein sequences, it is possible to apply standard sequence alignment techniques in bioinformatics to improve accuracy of distinguishing between worm and virus signatures. Moreover, aligned signature sequences can be mined through traditional data mining techniques to extract metasignatures that help to distinguish between viral and worm signatures. All bioinformatics and data mining analysis were performed on publicly available tools and Weka. PMID:23983644

Large-Scale Event Extraction from Literature with Multi-Level Gene Normalization

PubMed Central

Wei, Chih-Hsuan; Hakala, Kai; Pyysalo, Sampo; Ananiadou, Sophia; Kao, Hung-Yu; Lu, Zhiyong; Salakoski, Tapio; Van de Peer, Yves; Ginter, Filip

2013-01-01

Text mining for the life sciences aims to aid database curation, knowledge summarization and information retrieval through the automated processing of biomedical texts. To provide comprehensive coverage and enable full integration with existing biomolecular database records, it is crucial that text mining tools scale up to millions of articles and that their analyses can be unambiguously linked to information recorded in resources such as UniProt, KEGG, BioGRID and NCBI databases. In this study, we investigate how fully automated text mining of complex biomolecular events can be augmented with a normalization strategy that identifies biological concepts in text, mapping them to identifiers at varying levels of granularity, ranging from canonicalized symbols to unique gene and proteins and broad gene families. To this end, we have combined two state-of-the-art text mining components, previously evaluated on two community-wide challenges, and have extended and improved upon these methods by exploiting their complementary nature. Using these systems, we perform normalization and event extraction to create a large-scale resource that is publicly available, unique in semantic scope, and covers all 21.9 million PubMed abstracts and 460 thousand PubMed Central open access full-text articles. This dataset contains 40 million biomolecular events involving 76 million gene/protein mentions, linked to 122 thousand distinct genes from 5032 species across the full taxonomic tree. Detailed evaluations and analyses reveal promising results for application of this data in database and pathway curation efforts. The main software components used in this study are released under an open-source license. Further, the resulting dataset is freely accessible through a novel API, providing programmatic and customized access (http://www.evexdb.org/api/v001/). Finally, to allow for large-scale bioinformatic analyses, the entire resource is available for bulk download from http://evexdb.org/download/, under the Creative Commons – Attribution – Share Alike (CC BY-SA) license. PMID:23613707

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.

On the classification techniques in data mining for microarray data classification

NASA Astrophysics Data System (ADS)

Aydadenta, Husna; Adiwijaya

2018-03-01

Cancer is one of the deadly diseases, according to data from WHO by 2015 there are 8.8 million more deaths caused by cancer, and this will increase every year if not resolved earlier. Microarray data has become one of the most popular cancer-identification studies in the field of health, since microarray data can be used to look at levels of gene expression in certain cell samples that serve to analyze thousands of genes simultaneously. By using data mining technique, we can classify the sample of microarray data thus it can be identified with cancer or not. In this paper we will discuss some research using some data mining techniques using microarray data, such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, k-Nearest Neighbor (kNN), and C4.5, and simulation of Random Forest algorithm with technique of reduction dimension using Relief. The result of this paper show performance measure (accuracy) from classification algorithm (SVM, ANN, Naive Bayes, kNN, C4.5, and Random Forets).The results in this paper show the accuracy of Random Forest algorithm higher than other classification algorithms (Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, k-Nearest Neighbor (kNN), and C4.5). It is hoped that this paper can provide some information about the speed, accuracy, performance and computational cost generated from each Data Mining Classification Technique based on microarray data.

Data mining for the identification of metabolic syndrome status

PubMed Central

Worachartcheewan, Apilak; Schaduangrat, Nalini; Prachayasittikul, Virapong; Nantasenamat, Chanin

2018-01-01

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS. PMID:29383020

Data mining for the identification of metabolic syndrome status.

PubMed

Worachartcheewan, Apilak; Schaduangrat, Nalini; Prachayasittikul, Virapong; Nantasenamat, Chanin

2018-01-01

Metabolic syndrome (MS) is a condition associated with metabolic abnormalities that are characterized by central obesity (e.g. waist circumference or body mass index), hypertension (e.g. systolic or diastolic blood pressure), hyperglycemia (e.g. fasting plasma glucose) and dyslipidemia (e.g. triglyceride and high-density lipoprotein cholesterol). It is also associated with the development of diabetes mellitus (DM) type 2 and cardiovascular disease (CVD). Therefore, the rapid identification of MS is required to prevent the occurrence of such diseases. Herein, we review the utilization of data mining approaches for MS identification. Furthermore, the concept of quantitative population-health relationship (QPHR) is also presented, which can be defined as the elucidation/understanding of the relationship that exists between health parameters and health status. The QPHR modeling uses data mining techniques such as artificial neural network (ANN), support vector machine (SVM), principal component analysis (PCA), decision tree (DT), random forest (RF) and association analysis (AA) for modeling and construction of predictive models for MS characterization. The DT method has been found to outperform other data mining techniques in the identification of MS status. Moreover, the AA technique has proved useful in the discovery of in-depth as well as frequently occurring health parameters that can be used for revealing the rules of MS development. This review presents the potential benefits on the applications of data mining as a rapid identification tool for classifying MS.

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.

Minehunting sonar system research and development

NASA Astrophysics Data System (ADS)

Ferguson, Brian

2002-05-01

Sea mines have the potential to threaten the freedom of the seas by disrupting maritime trade and restricting the freedom of maneuver of navies. The acoustic detection, localization, and classification of sea mines involves a sequence of operations starting with the transmission of a sonar pulse and ending with an operator interpreting the information on a sonar display. A recent improvement to the process stems from the application of neural networks to the computed aided detection of sea mines. The advent of ultrawideband sonar transducers together with pulse compression techniques offers a thousandfold increase in the bandwidth-time product of conventional minehunting sonar transmissions enabling stealth mines to be detected at longer ranges. These wideband signals also enable mines to be imaged at safe standoff distances by applying tomographic image reconstruction techniques. The coupling of wideband transducer technology with synthetic aperture processing enhances the resolution of side scan sonars in both the cross-track and along-track directions. The principles on which conventional and advanced minehunting sonars are based are reviewed and the results of applying novel sonar signal processing algorithms to high-frequency sonar data collected in Australian waters are presented.

Cooperative organic mine avoidance path planning

NASA Astrophysics Data System (ADS)

McCubbin, Christopher B.; Piatko, Christine D.; Peterson, Adam V.; Donnald, Creighton R.; Cohen, David

2005-06-01

The JHU/APL Path Planning team has developed path planning techniques to look for paths that balance the utility and risk associated with different routes through a minefield. Extending on previous years' efforts, we investigated real-world Naval mine avoidance requirements and developed a tactical decision aid (TDA) that satisfies those requirements. APL has developed new mine path planning techniques using graph based and genetic algorithms which quickly produce near-minimum risk paths for complicated fitness functions incorporating risk, path length, ship kinematics, and naval doctrine. The TDA user interface, a Java Swing application that obtains data via Corba interfaces to path planning databases, allows the operator to explore a fusion of historic and in situ mine field data, control the path planner, and display the planning results. To provide a context for the minefield data, the user interface also renders data from the Digital Nautical Chart database, a database created by the National Geospatial-Intelligence Agency containing charts of the world's ports and coastal regions. This TDA has been developed in conjunction with the COMID (Cooperative Organic Mine Defense) system. This paper presents a description of the algorithms, architecture, and application produced.

Slope stability radar for monitoring mine walls

NASA Astrophysics Data System (ADS)

Reeves, Bryan; Noon, David A.; Stickley, Glen F.; Longstaff, Dennis

2001-11-01

Determining slope stability in a mining operation is an important task. This is especially true when the mine workings are close to a potentially unstable slope. A common technique to determine slope stability is to monitor the small precursory movements, which occur prior to collapse. The slope stability radar has been developed to remotely scan a rock slope to continuously monitor the spatial deformation of the face. Using differential radar interferometry, the system can detect deformation movements of a rough wall with sub-millimeter accuracy, and with high spatial and temporal resolution. The effects of atmospheric variations and spurious signals can be reduced via signal processing means. The advantage of radar over other monitoring techniques is that it provides full area coverage without the need for mounted reflectors or equipment on the wall. In addition, the radar waves adequately penetrate through rain, dust and smoke to give reliable measurements, twenty-four hours a day. The system has been trialed at three open-cut coal mines in Australia, which demonstrated the potential for real-time monitoring of slope stability during active mining operations.

Fluid placement of fixated scrubber sludge to reduce surface subsidence and to abate acid mine drainage in abandoned underground coal mines

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

Meiers, R.J.; Golden, D.; Gray, R.

1995-12-31

Indianapolis Power and Light Company (IPL) began researching the use of fluid placement techniques of the fixated scrubber sludge (FSS) to reduce surface subsidence from underground coal mines to develop an economic alternative to low strength concrete grout. Abandoned underground coal mines surround property adjacent to IPL`s coal combustion by-product (CCBP) landfill at the Petersburg Generating Station. Landfill expansion into these areas is in question because of the high potential for sinkhole subsidence to develop. Sinkholes manifesting at the surface would put the integrity of a liner or runoff pond containment structure for a CCBP disposal facility at risk. Themore » fluid placement techniques of the FSS as a subsidence abatement technology was demonstrated during an eight week period in September, October, and November 1994 at the Petersburg Generating Station. The success of this technology will be determined by the percentage of the mine void filled, strength of the FSS placed, and the overall effects on the hydrogeologic environment. The complete report for this project will be finalized in early 1996.« less

Reproducibility of studies on text mining for citation screening in systematic reviews: Evaluation and checklist.

PubMed

Olorisade, Babatunde Kazeem; Brereton, Pearl; Andras, Peter

2017-09-01

Independent validation of published scientific results through study replication is a pre-condition for accepting the validity of such results. In computation research, full replication is often unrealistic for independent results validation, therefore, study reproduction has been justified as the minimum acceptable standard to evaluate the validity of scientific claims. The application of text mining techniques to citation screening in the context of systematic literature reviews is a relatively young and growing computational field with high relevance for software engineering, medical research and other fields. However, there is little work so far on reproduction studies in the field. In this paper, we investigate the reproducibility of studies in this area based on information contained in published articles and we propose reporting guidelines that could improve reproducibility. The study was approached in two ways. Initially we attempted to reproduce results from six studies, which were based on the same raw dataset. Then, based on this experience, we identified steps considered essential to successful reproduction of text mining experiments and characterized them to measure how reproducible is a study given the information provided on these steps. 33 articles were systematically assessed for reproducibility using this approach. Our work revealed that it is currently difficult if not impossible to independently reproduce the results published in any of the studies investigated. The lack of information about the datasets used limits reproducibility of about 80% of the studies assessed. Also, information about the machine learning algorithms is inadequate in about 27% of the papers. On the plus side, the third party software tools used are mostly free and available. The reproducibility potential of most of the studies can be significantly improved if more attention is paid to information provided on the datasets used, how they were partitioned and utilized, and how any randomization was controlled. We introduce a checklist of information that needs to be provided in order to ensure that a published study can be reproduced. Copyright © 2017 Elsevier Inc. All rights reserved.

Exploratory analysis of textual data from the Mother and Child Handbook using a text mining method (II): Monthly changes in the words recorded by mothers.

PubMed

Tagawa, Miki; Matsuda, Yoshio; Manaka, Tomoko; Kobayashi, Makiko; Ohwada, Michitaka; Matsubara, Shigeki

2017-01-01

The aim of the study was to examine the possibility of converting subjective textual data written in the free column space of the Mother and Child Handbook (MCH) into objective information using text mining and to compare any monthly changes in the words written by the mothers. Pregnant women without complications (n = 60) were divided into two groups according to State-Trait Anxiety Inventory grade: low trait anxiety (group I, n = 39) and high trait anxiety (group II, n = 21). Exploratory analysis of the textual data from the MCH was conducted by text mining using the Word Miner software program. Using 1203 structural elements extracted after processing, a comparison of monthly changes in the words used in the mothers' comments was made between the two groups. The data was mainly analyzed by a correspondence analysis. The structural elements in groups I and II were divided into seven and six clusters, respectively, by cluster analysis. Correspondence analysis revealed clear monthly changes in the words used in the mothers' comments as the pregnancy progressed in group I, whereas the association was not clear in group II. The text mining method was useful for exploratory analysis of the textual data obtained from pregnant women, and the monthly change in the words used in the mothers' comments as pregnancy progressed differed according to their degree of unease. © 2016 Japan Society of Obstetrics and Gynecology.

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.

Industrial application of semantic process mining

NASA Astrophysics Data System (ADS)

Espen Ingvaldsen, Jon; Atle Gulla, Jon

2012-05-01

Process mining relates to the extraction of non-trivial and useful information from information system event logs. It is a new research discipline that has evolved significantly since the early work on idealistic process logs. Over the last years, process mining prototypes have incorporated elements from semantics and data mining and targeted visualisation techniques that are more user-friendly to business experts and process owners. In this article, we present a framework for evaluating different aspects of enterprise process flows and address practical challenges of state-of-the-art industrial process mining. We also explore the inherent strengths of the technology for more efficient process optimisation.

LANDSAT inventory of surface-mined areas using extendible digital techniques

NASA Technical Reports Server (NTRS)

Anderson, A. T.; Schultz, D. T.; Buchman, N.

1975-01-01

Multispectral LANDSAT imagery was analyzed to provide a rapid and accurate means of identification, classification, and measurement of strip-mined surfaces in Western Maryland. Four band analysis allows distinction of a variety of strip-mine associated classes, but has limited extendibility. A method for surface area measurements of strip mines, which is both geographically and temporally extendible, has been developed using band-ratioed LANDSAT reflectance data. The accuracy of area measurement by this method, averaged over three LANDSAT scenes taken between September 1972 and July 1974, is greater than 93%. Total affected acreage of large (50 hectare/124 acre) mines can be measured to within 1.0%.

Open-source tools for data mining.

PubMed

Zupan, Blaz; Demsar, Janez

2008-03-01

With a growing volume of biomedical databases and repositories, the need to develop a set of tools to address their analysis and support knowledge discovery is becoming acute. The data mining community has developed a substantial set of techniques for computational treatment of these data. In this article, we discuss the evolution of open-source toolboxes that data mining researchers and enthusiasts have developed over the span of a few decades and review several currently available open-source data mining suites. The approaches we review are diverse in data mining methods and user interfaces and also demonstrate that the field and its tools are ready to be fully exploited in biomedical research.

Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research.

PubMed

Bravo, Àlex; Piñero, Janet; Queralt-Rosinach, Núria; Rautschka, Michael; Furlong, Laura I

2015-02-21

Current biomedical research needs to leverage and exploit the large amount of information reported in scientific publications. Automated text mining approaches, in particular those aimed at finding relationships between entities, are key for identification of actionable knowledge from free text repositories. We present the BeFree system aimed at identifying relationships between biomedical entities with a special focus on genes and their associated diseases. By exploiting morpho-syntactic information of the text, BeFree is able to identify gene-disease, drug-disease and drug-target associations with state-of-the-art performance. The application of BeFree to real-case scenarios shows its effectiveness in extracting information relevant for translational research. We show the value of the gene-disease associations extracted by BeFree through a number of analyses and integration with other data sources. BeFree succeeds in identifying genes associated to a major cause of morbidity worldwide, depression, which are not present in other public resources. Moreover, large-scale extraction and analysis of gene-disease associations, and integration with current biomedical knowledge, provided interesting insights on the kind of information that can be found in the literature, and raised challenges regarding data prioritization and curation. We found that only a small proportion of the gene-disease associations discovered by using BeFree is collected in expert-curated databases. Thus, there is a pressing need to find alternative strategies to manual curation, in order to review, prioritize and curate text-mining data and incorporate it into domain-specific databases. We present our strategy for data prioritization and discuss its implications for supporting biomedical research and applications. BeFree is a novel text mining system that performs competitively for the identification of gene-disease, drug-disease and drug-target associations. Our analyses show that mining only a small fraction of MEDLINE results in a large dataset of gene-disease associations, and only a small proportion of this dataset is actually recorded in curated resources (2%), raising several issues on data prioritization and curation. We propose that joint analysis of text mined data with data curated by experts appears as a suitable approach to both assess data quality and highlight novel and interesting information.

Knowledge Discovery and Data Mining in Iran's Climatic Researches

NASA Astrophysics Data System (ADS)

Karimi, Mostafa

2013-04-01

Advances in measurement technology and data collection is the database gets larger. Large databases require powerful tools for analysis data. Iterative process of acquiring knowledge from information obtained from data processing is done in various forms in all scientific fields. However, when the data volume large, and many of the problems the Traditional methods cannot respond. in the recent years, use of databases in various scientific fields, especially atmospheric databases in climatology expanded. in addition, increases in the amount of data generated by the climate models is a challenge for analysis of it for extraction of hidden pattern and knowledge. The approach to this problem has been made in recent years uses the process of knowledge discovery and data mining techniques with the use of the concepts of machine learning, artificial intelligence and expert (professional) systems is overall performance. Data manning is analytically process for manning in massive volume data. The ultimate goal of data mining is access to information and finally knowledge. climatology is a part of science that uses variety and massive volume data. Goal of the climate data manning is Achieve to information from variety and massive atmospheric and non-atmospheric data. in fact, Knowledge Discovery performs these activities in a logical and predetermined and almost automatic process. The goal of this research is study of uses knowledge Discovery and data mining technique in Iranian climate research. For Achieve This goal, study content (descriptive) analysis and classify base method and issue. The result shown that in climatic research of Iran most clustering, k-means and wards applied and in terms of issues precipitation and atmospheric circulation patterns most introduced. Although several studies in geography and climate issues with statistical techniques such as clustering and pattern extraction is done, Due to the nature of statistics and data mining, but cannot say for internal climate studies in data mining and knowledge discovery techniques are used. However, it is necessary to use the KDD Approach and DM techniques in the climatic studies, specific interpreter of climate modeling result.