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Download Special Issue Advances in Bioinformatics Literature-Mining Solutions for Life Science Research Guest Editors: Jörg Hakenberg, Goran Nenadic, Dietrich Rebholz-Schuhman, and Jin-Dong Kim Literature-Mining Solutions for Life Science Research Advances in Bioinformatics Literature-Mining Solutions for Life Science Research Guest Editors: Jorg¨ Hakenberg, Goran Nenadic, Dietrich Rebholz-Schuhman, and Jin-Dong Kim Copyright © 2013 Hindawi Publishing Corporation. All rights reserved. This is a special issue published in “Advances in Bioinformatics.” All articles are open access articles distributed under the Creative Com- mons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Editorial Board Shandar Ahmad, Japan Paul Harrison, USA Jagath Rajapakse, Singapore Tatsuya Akutsu, Japan Huixiao Hong, USA Marcel Reinders, The Netherlands Rolf Backofen, Germany David Jones, UK P. Rouze, Belgium Craig Benham, USA George Karypis, USA Alejandro A. Scha¨ffer, USA Mark Borodovsky, USA Jie Liang, USA E. L. Sonnhammer, Sweden Rita Casadio, Italy Guohui Lin, Canada Sandor Vajda, USA Ming Chen, China Pietro Lio,´ UK Yves Van de Peer, Belgium David Corne, UK Dennis Livesay, USA Antoine van Kampen, The Netherlands Bhaskar Dasgupta, USA Satoru Miyano, Japan Alexander Zelikovsky, USA Ramana Davuluri, USA Burkhard Morgenstern, Germany Zhongming Zhao, USA J. Dopazo, Spain Masha Niv, Israel Yi Ming Zou, USA Anton Enright, UK Florencio Pazos, Spain Stavros Hamodrakas, Greece David Posada, Spain Contents Literature-Mining Solutions for Life Science Research,Jorg¨ Hakenberg, Goran Nenadic, Dietrich Rebholz-Schuhman, and Jin-Dong Kim Volume 2013, Article ID 320436, 2 pages Do Peers See More in a Paper Than Its Authors?, Anna Divoli, Preslav Nakov, and Marti A. Hearst Volume 2012, Article ID 750214, 15 pages Literature Retrieval and Mining in Bioinformatics: State of the Art and Challenges, Andrea Manconi, Eloisa Vargiu, Giuliano Armano, and Luciano Milanesi Volume 2012, Article ID 573846, 10 pages Exploring Biomolecular Literature with EVEX: Connecting Genes through Events, Homology, and Indirect Associations, Sofie Van Landeghem, Kai Hakala, Samuel Ronnqvist,¨ Tapio Salakoski, Yves Van de Peer, and Filip Ginter Volume 2012, Article ID 582765, 12 pages BioEve Search: A Novel Framework to Facilitate Interactive Literature Search, Syed Toufeeq Ahmed, Hasan Davulcu, Sukru Tikves, Radhika Nair, and Zhongming Zhao Volume 2012, Article ID 509126, 12 pages Applications of Natural Language Processing in Biodiversity Science, Anne E. Thessen, Hong Cui, and Dmitry Mozzherin Volume 2012, Article ID 391574, 17 pages Hindawi Publishing Corporation Advances in Bioinformatics Volume 2013, Article ID 320436, 2 pages http://dx.doi.org/10.1155/2013/320436 Editorial Literature Mining Solutions for Life Science Research Jörg Hakenberg,1 Goran Nenadic,2 Dietrich Rebholz-Schuhmann,3, 4 and Jin-Dong Kim5 1 Disease Translational Informatics, F. Hoffmann-La Roche Inc., Nutley, NJ 07110, USA 2 School of Computer Science and Manchester Institute of Biotechnology, University of Manchester, Manchester M13 9PL, UK 3 European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK 4 Institut für Computerlinguistik, Universität Zürich, 8050 Zürich, Switzerland 5 Database Center for Life Science (DBCLS), Research Organization of Information and Systems (ROIS), Tokyo 113-0032, Japan Correspondence should be addressed to Jörg Hakenberg; [email protected] Received 11 December 2012; Accepted 11 December 2012 Copyright © 2013 Jörg Hakenberg et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Research and development in the area of biomedical litera- is special issue of Advances in Bioinformatics presents ture analysis aims at providing life science researchers with overviews and examples of end-user-oriented biomedical text effective means to access and exploit knowledge contained in mining tools for bioinformaticians, molecular biologists, bio- scienti�c publications. Virtually all journal publications and chemists, clinicians, pharmacologists, and other researchers many conference proceedings are nowadays readily available in life sciences. in an electronic form—for instance, as abstracts through We start with A. Manconi et al. survey on “Literature the MEDLINE citation index or as full-text articles through retrieval and mining in bioinformatics: state of the art and PubMed Central. Nevertheless, keeping up to date with and challenges.” e authors introduce the major concepts that searching for recent �ndings in a research domain remains a life science researchers should be familiar with getting the tedious task hampered by inefficient and ineffective means best out of existing text mining solutions, and survey key tools and research. In a dedicated second part of their survey, for access and exploitation. Biomedical text analysis aims the authors address the major challenges both life science to improve access to unstructured knowledge by alleviating researchers and solution developers are facing at this point. searches, providing auto generated summaries of documents e reader will �nd plenty of references to existing search and topics, linking and integrating publications with struc- tools, resources, and research papers. tured resources, visualizing content for better understanding, A. E. essen et al. focus on a particular domain, and guiding researchers to novel hypotheses and into knowl- presenting an overview of “Applications of natural language edge discovery. processing in biodiversity science.” e authors review the Focused research over recent years has improved fun- application of natural processing and machine learning for damental solutions for biomedical text mining, such as biological information extraction regarding cellular pro- document retrieval, named entity recognition, normalization cesses, taxonomic names, and morphological characters. You and grounding, and extraction of relationships, with levels will �nd detailed examples, a summary of all steps involved of accuracy that reach human annotators when considering in information extraction, and lots of references to existing inter annotator agreement. Consequently, more and more tools and resources. integrative analysis tools were put forward by the text mining S. T. Ahmed et al. introduce their semantic faceted search community targeting a broad audience of end users: generic engine, BioEve, in “A novel framework to facilitate interac- and task-speci�c search engines for life science researchers, tive literature search.” ey couple an automated extraction interfaces for networks synthesis based on textual evidences, system with a cognitive search and navigation service, to or more specialized tools searching for transcription factors, alleviate the process of searching and browsing huge amounts or primer sequences. of literature such as provided/delivered by MEDLINE. BioEve 2 Advances in Bioinformatics enables interactive query re�nement and suggests concepts and entities (like genes, drugs, and diseases) to quickly �lter and modify search directions, thereby achieving semantic enrichment that improves insight gains while searching literature. S. V. Landeghem et al. present their EVEX resource in “Exploring biomolecular literature with EVEX: connecting genes through events, homology, and indirect associations.” e authors extracted more than 20 million biomolecular events involving genes and proteins, such as phosphoryla- tion and gene regulation, from MEDLINE. e online tool generates a summary on the searched gene denoting all regulated genes, binding partners, subcellular locations, and other related data linked to the searched gene. We conclude this special issue with the paper by A. Divoli et al., discussing whether “DoPeersseemoreinapaperthan its authors.” In a meta-analysis using automatic text analysis, they address questions such as how informative an abstract iscomparedtothefulltext;andhowpeersandauthors might view the major contributions of a paper differently. eir analyses are comparing the information content of an abstract, as written by the paper’s authors, to sentences that mention the paper as a reference, written by peers. Using this strategy, A. Divoli et al. found, for example, that citing sentences contain 20 additional concepts (likely important contributions) that were not mentioned in the abstract of the paper referred to, but% maybe should have been to help attract even more peers. Acknowledgment e guest editors wish to thank all authors for their contribu- tions to this special issue, as well as the numerous reviewers who supported the authors and us with their invaluable feedback. Jörg Hakenberg Goran Nenadic Dietrich Rebholz-Schuhmann Jin-Dong Kim Hindawi Publishing Corporation Advances in Bioinformatics Volume 2012, Article ID 750214, 15 pages doi:10.1155/2012/750214 Research Article Do Peers See More in a Paper Than Its Authors? Anna Divoli,1 Preslav Nakov,2 and Marti A. Hearst3 1 Pingar Research, Pingar, Auckland 1010, New Zealand 2 Qatar Computing Research Institute, Qatar Foundation, Tornado Tower, Floor 10, P.O. Box 5825, Doha, Qatar 3 School of Information, University of California at Berkeley, CA 94720, USA Correspondence should be addressed to Anna Divoli, [email protected] Received 16 December 2011; Revised
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