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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

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eGIFT: mining gene information from the literature.

Catalina O Tudor¹, Carl J Schmidt, K Vijay-Shanker

¹Department of Computer and Information Sciences, University of Delaware, Newark, Delaware, USA. tudor@cis.udel.edu

BMC Bioinformatics

|August 11, 2010

Summary

eGIFT is a web tool that helps scientists quickly understand gene information by identifying and ranking relevant terms. It overcomes gene name ambiguity and improves literature search efficiency for gene discovery.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Biomedical literature is rapidly expanding, making gene-specific literature searches challenging.
Gene name ambiguity and a high volume of results lead to irrelevant hits.
Life scientists and curators struggle to quickly grasp a gene's overall picture from available documents.

Purpose of the Study:

To present eGIFT, a web-based tool for associating informative terms (iTerms) and sentences with specific genes.
To improve the efficiency and accuracy of retrieving and understanding gene-specific information from biomedical literature.

Main Methods:

eGIFT ranks iTerms based on their occurrence frequency in gene-specific literature versus general gene literature.
It retrieves gene documents using all known gene names, aliases, and synonyms.
A disambiguation step removes irrelevant matches, and a filtering process retains focus-specific abstracts.

Main Results:

eGIFT successfully associates informative terms (iTerms) and relevant sentences with genes.
Evaluations showed 88%-94% of iTerms were salient, and 94% of UniProtKB keywords were identified.
The tool effectively handles gene name ambiguity and filters for relevant abstracts.

Conclusions:

iTerms effectively capture highly relevant gene aspects and functions.
eGIFT provides concise gene descriptions by displaying sentences containing key terms.
The tool aids life scientists in surveying high-throughput experiment results and annotators in finding gene-related articles.