Literature DB >> 14980013

Mining the biomedical literature in the genomic era: an overview.

Hagit Shatkay1, Ronen Feldman.   

Abstract

The past decade has seen a tremendous growth in the amount of experimental and computational biomedical data, specifically in the areas of genomics and proteomics. This growth is accompanied by an accelerated increase in the number of biomedical publications discussing the findings. In the last few years, there has been a lot of interest within the scientific community in literature-mining tools to help sort through this abundance of literature and find the nuggets of information most relevant and useful for specific analysis tasks. This paper provides a road map to the various literature-mining methods, both in general and within bioinformatics. It surveys the disciplines involved in unstructured-text analysis, categorizes current work in biomedical literature mining with respect to these disciplines, and provides examples of text analysis methods applied towards meeting some of the current challenges in bioinformatics.

Mesh:

Year:  2003        PMID: 14980013     DOI: 10.1089/106652703322756104

Source DB:  PubMed          Journal:  J Comput Biol        ISSN: 1066-5277            Impact factor:   1.479


  56 in total

1.  Quantitative evaluation of expression difference in report assignments between nursing and radiologic technology departments.

Authors:  Naoki Nishimoto; Yuki Yokooka; Ayako Yagahara; Masahito Uesugi; Katsuhiko Ogasawara
Journal:  Radiol Phys Technol       Date:  2010-09-10

2.  Immune modulators in disease: integrating knowledge from the biomedical literature and gene expression.

Authors:  Nophar Geifman; Sanchita Bhattacharya; Atul J Butte
Journal:  J Am Med Inform Assoc       Date:  2015-12-11       Impact factor: 4.497

3.  A statistical approach to scanning the biomedical literature for pharmacogenetics knowledge.

Authors:  Daniel L Rubin; Caroline F Thorn; Teri E Klein; Russ B Altman
Journal:  J Am Med Inform Assoc       Date:  2004-11-23       Impact factor: 4.497

Review 4.  Microbial metabolomics: replacing trial-and-error by the unbiased selection and ranking of targets.

Authors:  Mariët J van der Werf; Renger H Jellema; Thomas Hankemeier
Journal:  J Ind Microbiol Biotechnol       Date:  2005-05-14       Impact factor: 3.346

Review 5.  Ecologies, outreach, and the evolution of medical libraries.

Authors:  Bern Shen
Journal:  J Med Libr Assoc       Date:  2005-10

6.  Quantitative assessment of dictionary-based protein named entity tagging.

Authors:  Hongfang Liu; Zhang-Zhi Hu; Manabu Torii; Cathy Wu; Carol Friedman
Journal:  J Am Med Inform Assoc       Date:  2006-06-23       Impact factor: 4.497

7.  NERBio: using selected word conjunctions, term normalization, and global patterns to improve biomedical named entity recognition.

Authors:  Richard Tzong-Han Tsai; Cheng-Lung Sung; Hong-Jie Dai; Hsieh-Chuan Hung; Ting-Yi Sung; Wen-Lian Hsu
Journal:  BMC Bioinformatics       Date:  2006-12-18       Impact factor: 3.169

Review 8.  A cheminformatic toolkit for mining biomedical knowledge.

Authors:  Gus R Rosania; Gordon Crippen; Peter Woolf; David States; Kerby Shedden
Journal:  Pharm Res       Date:  2007-03-24       Impact factor: 4.200

9.  Literature mining on pharmacokinetics numerical data: a feasibility study.

Authors:  Zhiping Wang; Seongho Kim; Sara K Quinney; Yingying Guo; Stephen D Hall; Luis M Rocha; Lang Li
Journal:  J Biomed Inform       Date:  2009-04-02       Impact factor: 6.317

10.  PPI finder: a mining tool for human protein-protein interactions.

Authors:  Min He; Yi Wang; Wei Li
Journal:  PLoS One       Date:  2009-02-23       Impact factor: 3.240

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