Literature DB >> 11072322

Identifying the Interaction between Genes and Gene Products Based on Frequently Seen Verbs in Medline Abstracts.

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Abstract

We have selected the most frequently seen verbs from raw texts made up of 1-million-words of Medline abstracts, and we were able to identify (or bracket) noun phrases contained in the corpus, with a precision rate of 90%. Then, based on the noun-phrase-bracketted corpus, we tried to find the subject and object terms for some frequently seen verbs in the domain. The precision rate of finding the right subject and object for each verb was about 73%. This task was only made possible because we were able to linguistically analyze (or parse) a large quantity of a raw corpus. Our approach will be useful for classifying genes and gene products and for identifying the interaction between them. It is the first step of our effort in building a genome-related thesaurus and hierarchies in a fully automatic way.

Year:  1998        PMID: 11072322

Source DB:  PubMed          Journal:  Genome Inform Ser Workshop Genome Inform


  19 in total

1.  Research for research: tools for knowledge discovery and visualization.

Authors:  Erik M Van Mulligen; Christiaan Van Der Eijk; Jan A Kors; Bob J A Schijvenaars; Barend Mons
Journal:  Proc AMIA Symp       Date:  2002

2.  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

3.  Extraction of protein interaction data: a comparative analysis of methods in use.

Authors:  Hena Jose; Thangavel Vadivukarasi; Jyothi Devakumar
Journal:  EURASIP J Bioinform Syst Biol       Date:  2007

4.  BioTagger-GM: a gene/protein name recognition system.

Authors:  Manabu Torii; Zhangzhi Hu; Cathy H Wu; Hongfang Liu
Journal:  J Am Med Inform Assoc       Date:  2008-12-11       Impact factor: 4.497

5.  Kinase pathway database: an integrated protein-kinase and NLP-based protein-interaction resource.

Authors:  Asako Koike; Yoshiyuki Kobayashi; Toshihisa Takagi
Journal:  Genome Res       Date:  2003-06       Impact factor: 9.043

6.  PreBIND and Textomy--mining the biomedical literature for protein-protein interactions using a support vector machine.

Authors:  Ian Donaldson; Joel Martin; Berry de Bruijn; Cheryl Wolting; Vicki Lay; Brigitte Tuekam; Shudong Zhang; Berivan Baskin; Gary D Bader; Katerina Michalickova; Tony Pawson; Christopher W V Hogue
Journal:  BMC Bioinformatics       Date:  2003-03-27       Impact factor: 3.169

7.  Textpresso: an ontology-based information retrieval and extraction system for biological literature.

Authors:  Hans-Michael Müller; Eimear E Kenny; Paul W Sternberg
Journal:  PLoS Biol       Date:  2004-09-21       Impact factor: 8.029

8.  Protein interaction sentence detection using multiple semantic kernels.

Authors:  Tamara Polajnar; Theodoros Damoulas; Mark Girolami
Journal:  J Biomed Semantics       Date:  2011-05-14

9.  Connecting the dots between PubMed abstracts.

Authors:  M Shahriar Hossain; Joseph Gresock; Yvette Edmonds; Richard Helm; Malcolm Potts; Naren Ramakrishnan
Journal:  PLoS One       Date:  2012-01-03       Impact factor: 3.240

10.  Functional gene clustering via gene annotation sentences, MeSH and GO keywords from biomedical literature.

Authors:  Jeyakumar Natarajan; Jawahar Ganapathy
Journal:  Bioinformation       Date:  2007-12-30
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