Retrieval with gene queries.

BMC Bioinformatics
Aditya K Sehgal, Padmini Srinivasan

Abstract

Accuracy of document retrieval from MEDLINE for gene queries is crucially important for many applications in bioinformatics. We explore five information retrieval-based methods to rank documents retrieved by PubMed gene queries for the human genome. The aim is to rank relevant documents higher in the retrieved list. We address the special challenges faced due to ambiguity in gene nomenclature: gene terms that refer to multiple genes, gene terms that are also English words, and gene terms that have other biological meanings. Our two baseline ranking strategies are quite similar in performance. Two of our three LocusLink-based strategies offer significant improvements. These methods work very well even when there is ambiguity in the gene terms. Our best ranking strategy offers significant improvements on three different kinds of ambiguities over our two baseline strategies (improvements range from 15.9% to 17.7% and 11.7% to 13.3% depending on the baseline). For most genes the best ranking query is one that is built from the LocusLink (now Entrez Gene) summary and product information along with the gene names and aliases. For others, the gene names and aliases suffice. We also present an approach that successfully predicts, for a...Continue Reading

References

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Citations

Apr 3, 2010·Bioinformatics·Naoaki OkazakiJun'ichi Tsujii
Jun 21, 2011·Bioinformatics·Sanmitra BhattacharyaPadmini Srinivasan
Dec 6, 2007·BMC Bioinformatics·Padmini Srinivasan, Xin Ying Qiu
Oct 26, 2010·BMC Medical Informatics and Decision Making·Vagelis HristidisMichael Weiner
Jun 14, 2008·Genome Biology·Rob JelierJan A Kors
Oct 18, 2008·Genome Biology·Alexander A MorganLynette Hirschman
Feb 14, 2014·PloS One·Ashutosh K PandeyRobert W Williams
Jun 21, 2015·BMC Bioinformatics·Padmini SrinivasanCaren Chang

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Software Mentioned

LocusLink
NTop5P
Lemur
Bio Genes
Redhat Linux
ESearch
MEDLINE
PubMed
Top5P

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