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Bioinformatics Advance Access published online on February 2, 2009
Bioinformatics, doi:10.1093/bioinformatics/btp049
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© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

SciMiner: Web-based literature mining tool for target identification and functional enrichment analysis

Junguk Hur 1, Adam D. Schuyler 2, David J. States 3 and Eva L. Feldman 1,2

1Bioinformatics Program, 2Department of Neurology, University of Michigan, Ann Arbor, MI 48109, USA 3School of Health Information Science, University of Texas at Houston, Houston, TX 77030, USA

*To whom correspondence should be addressed. Mr. Junguk Hur, E-mail: juhur{at}umich.edu


  Abstract

Summary: SciMiner is a web-based literature mining and functional analysis tool that identifies genes and proteins using a context specific analysis of MEDLINE abstracts and full texts. SciMiner accepts a free text query (PubMed Entrez search) or a list of PubMed identifiers (PMIDs) as input. SciMiner uses both regular expression patterns and dictionaries of gene symbols and names compiled from multiple sources. Ambiguous acronyms are resolved by a scoring scheme based on the co-occurrence of acronyms and corresponding description terms, which incorporates optional user-defined filters. Functional enrichment analyses are used to identify highly relevant targets (genes and proteins), GO (Gene Ontology) terms, MeSH (Medical Subject Headings) terms, pathways, and protein-protein interaction networks by comparing identified targets from one search result to those from other searches or to the full HUGO human gene set. The performance of gene/protein name identification was evaluated using the BioCreAtIvE (Critical Assessment of Information Extraction systems in Biology) version 2 (Year 2006) Gene Normalization Task as a gold standard. SciMiner achieved 87.1% recall, 71.3% precision, and 75.8% F-measure. SciMiner's literature mining performance coupled with functional enrichment analyses provides an efficient platform for retrieval and summary of rich biological information from corpora of users' interests.

Availability: http://jdrf.neurology.med.umich.edu/SciMiner/. A server version of the SciMiner is also available for download and enables users to utilize their institution's journal subscriptions.

Contact: juhur{at}umich.edu

Supplementary information: Supplementary materials are available at Bioinformatics online.

Associate Editor: Dr. Jonathan Wren

Received on September 6, 2008; revised on January 9, 2009; accepted on January 21, 2009

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