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This article is part of the supplement: The 2007 International Conference on Bioinformatics & Computational Biology (BIOCOMP'07)

Open Access Research

Semantically linking and browsing PubMed abstracts with gene ontology

Bhanu C Vanteru1, Jahangheer S Shaik12 and Mohammed Yeasin123*

Author Affiliations

1 Electrical and Computer Engineering Department, University of Memphis, Memphis, Tennessee, USA

2 Bioinformatics Program, University of Memphis, Memphis, Tennessee, USA

3 Software Testing and Excellence Program, University Of Memphis University of Memphis, Memphis, Tennessee, USA

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BMC Genomics 2008, 9(Suppl 1):S10  doi:10.1186/1471-2164-9-S1-S10

Published: 20 March 2008

Abstract

Background

The technological advances in the past decade have lead to massive progress in the field of biotechnology. The documentation of the progress made exists in the form of research articles. The PubMed is the current most used repository for bio-literature. PubMed consists of about 17 million abstracts as of 2007 that require methods to efficiently retrieve and browse large volume of relevant information. The State-of-the-art technologies such as GOPubmed use simple keyword-based techniques for retrieving abstracts from the PubMed and linking them to the Gene Ontology (GO). This paper changes the paradigm by introducing semantics enabled technique to link the PubMed to the Gene Ontology, called, SEGOPubmed for ontology-based browsing. Latent Semantic Analysis (LSA) framework is used to semantically interface PubMed abstracts to the Gene Ontology.

Results

The Empirical analysis is performed to compare the performance of the SEGOPubmed with the GOPubmed. The analysis is initially performed using a few well-referenced query words. Further, statistical analysis is performed using GO curated dataset as ground truth. The analysis suggests that the SEGOPubmed performs better than the classic GOPubmed as it incorporates semantics.

Conclusions

The LSA technique is applied on the PubMed abstracts obtained based on the user query and the semantic similarity between the query and the abstracts. The analyses using well-referenced keywords show that the proposed semantic-sensitive technique outperformed the string comparison based techniques in associating the relevant abstracts to the GO terms. The SEGOPubmed also extracted the abstracts in which the keywords do not appear in isolation (i.e. they appear in combination with other terms) that could not be retrieved by simple term matching techniques.