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Bioinformatics Advance Access published online on January 18, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti268
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Bioinformatics © Oxford University Press 2005; all rights reserved.
Received July 15, 2004
Revised December 22, 2004
Accepted January 6, 2005

Article

Co-occurrence based meta-analysis of scientific texts: retrieving biological relationships between genes

R. Jelier 1*, G. Jenster 2, L. C. J. Dorssers 3, C. C. van der Eijk 1, E. M. van Mulligen 1, B. Mons 1, and J. A. Kors 1

1 Department of Medical Informatics, Erasmus MC - University Medical Center, Rotterdam, The Netherlands
2 Department of Urology, Erasmus MC - University Medical Center, Rotterdam, The Netherlands
3 Department of Pathology, Erasmus MC - University Medical Center, Rotterdam, The Netherlands

* To whom correspondence should be addressed.
R. Jelier, E-mail: r.jelier{at}erasmusmc.nl


  Abstract

Motivation: The advent of high-throughput experiments in molecular biology creates a need for methods to efficiently extract and use information for large numbers of genes. Recently, the associative concept space (ACS) has been developed for the representation of information extracted from biomedical literature. The ACS is a Euclidean space in which thesaurus concepts are positioned and the distances between concepts indicates their relatedness. The ACS uses co-occurrence of concepts as a source of information. In this paper we evaluate how well the system can retrieve functionally related genes and we compare its performance with a simple gene co-occurrence method.

Results: To assess the performance of the ACS we composed a test set of five groups of functionally related genes. With the ACS good scores were obtained for four of the five groups. When compared to the gene co-occurrence method, the ACS is capable of revealing more functional biological relations and can achieve results with less literature available per gene. Hierarchical clustering was performed on the ACS output, as a potential aid to users, and was found to provide useful clusters. Our results suggest that the algorithm can be of value for researchers studying large numbers of genes.

Availability: The ACS program is available upon request from the authors.


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