A literature search tool for intelligent extraction of disease-associated genes -- Jung et al. -- Journal of the American Medical Informatics Association

A literature search tool for intelligent extraction of disease-associated genes -- Jung et al. -- Journal of the American Medical Informatics Association

J Am Med Inform Assoc doi:10.1136/amiajnl-2012-001563

• Research and applications

A literature search tool for intelligent extraction of disease-associated genes

1. Jae-Yoon Jung1,


2. Todd F DeLuca1,


3. Tristan H Nelson2,


4. Dennis P Wall1,2

+ Author Affiliations

1. 
   ¹Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
   
   


2. 
   ²Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
   
   

1. Correspondence to Dr Dennis P Wall, Center for Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA; dpwall@hms.harvard.edu

• Received 11 December 2012


• Revised 15 July 2013


• Accepted 8 August 2013


• Published Online First 2 September 2013

Abstract

Objective To extract disorder-associated genes from the scientific literature in PubMed with greater sensitivity for literature-based support than existing methods.

Methods We developed a PubMed query to retrieve disorder-related, original research articles. Then we applied a rule-based text-mining algorithm with keyword matching to extract target disorders, genes with significant results, and the type of study described by the article.

Results We compared our resulting candidate disorder genes and supporting references with existing databases. We demonstrated that our candidate gene set covers nearly all genes in manually curated databases, and that the references supporting the disorder–gene link are more extensive and accurate than other general purpose gene-to-disorder association databases.

Conclusions We implemented a novel publication search tool to find target articles, specifically focused on links between disorders and genotypes. Through comparison against gold-standard manually updated gene–disorder databases and comparison with automated databases of similar functionality we show that our tool can search through the entirety of PubMed to extract the main gene findings for human diseases rapidly and accurately.

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