Literature DB >> 18820304

Literature mining in support of drug discovery.

Pankaj Agarwal1, David B Searls.   

Abstract

The drug discovery enterprise provides strong drivers for data integration. While attention in this arena has tended to focus on integration of primary data from omics and other large platform technologies contributing to drug discovery and development, the scientific literature remains a major source of information valuable to pharmaceutical enterprises, and therefore tools for mining such data and integrating it with other sources are of vital interest and economic impact. This review provides a brief overview of approaches to literature mining as they relate to drug discovery, and offers an illustrative case study of a 'lightweight' approach we have implemented within an industrial context.

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Year:  2008        PMID: 18820304     DOI: 10.1093/bib/bbn035

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  21 in total

Review 1.  Recent progress in automatically extracting information from the pharmacogenomic literature.

Authors:  Yael Garten; Adrien Coulet; Russ B Altman
Journal:  Pharmacogenomics       Date:  2010-10       Impact factor: 2.533

2.  PubMedMiner: Mining and Visualizing MeSH-based Associations in PubMed.

Authors:  Yucan Zhang; Indra Neil Sarkar; Elizabeth S Chen
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14

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

Authors:  Udo Hahn; K Bretonnel Cohen; Yael Garten; Nigam H Shah
Journal:  Brief Bioinform       Date:  2012-07       Impact factor: 11.622

4.  Kinase inhibition-related adverse events predicted from in vitro kinome and clinical trial data.

Authors:  Xinan Yang; Yong Huang; Matthew Crowson; Jianrong Li; Michael L Maitland; Yves A Lussier
Journal:  J Biomed Inform       Date:  2010-05-01       Impact factor: 6.317

5.  Text Mining of Rheumatoid Arthritis and Diabetes Mellitus to Understand the Mechanisms of Chinese Medicine in Different Diseases with Same Treatment.

Authors:  Ning Zhao; Guang Zheng; Jian Li; Hong-Yan Zhao; Cheng Lu; Miao Jiang; Chi Zhang; Hong-Tao Guo; Ai-Ping Lu
Journal:  Chin J Integr Med       Date:  2018-01-09       Impact factor: 1.978

6.  Automatic vs. manual curation of a multi-source chemical dictionary: the impact on text mining.

Authors:  Kristina M Hettne; Antony J Williams; Erik M van Mulligen; Jos Kleinjans; Valery Tkachenko; Jan A Kors
Journal:  J Cheminform       Date:  2010-03-23       Impact factor: 5.514

Review 7.  Empowering industrial research with shared biomedical vocabularies.

Authors:  Lee Harland; Christopher Larminie; Susanna-Assunta Sansone; Sorana Popa; M Scott Marshall; Michael Braxenthaler; Michael Cantor; Wendy Filsell; Mark J Forster; Enoch Huang; Andreas Matern; Mark Musen; Jasmin Saric; Ted Slater; Jabe Wilson; Nick Lynch; John Wise; Ian Dix
Journal:  Drug Discov Today       Date:  2011-09-23       Impact factor: 7.851

8.  Human synthetic lethal inference as potential anti-cancer target gene detection.

Authors:  Nuria Conde-Pueyo; Andreea Munteanu; Ricard V Solé; Carlos Rodríguez-Caso
Journal:  BMC Syst Biol       Date:  2009-12-16

9.  Novel phytochemical-antibiotic conjugates as multitarget inhibitors of Pseudomononas aeruginosa GyrB/ParE and DHFR.

Authors:  Premkumar Jayaraman; Kishore R Sakharkar; ChuSing Lim; Mohammad Imran Siddiqi; Sarinder K Dhillon; Meena K Sakharkar
Journal:  Drug Des Devel Ther       Date:  2013-06-17       Impact factor: 4.162

10.  Benchmarking human protein complexes to investigate drug-related systems and evaluate predicted protein complexes.

Authors:  Min Wu; Qi Yu; Xiaoli Li; Jie Zheng; Jing-Fei Huang; Chee-Keong Kwoh
Journal:  PLoS One       Date:  2013-02-06       Impact factor: 3.240

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