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This article is part of the supplement: Proceedings of the Seventh Annual MCBIOS Conference. Bioinformatics: Systems, Biology, Informatics and Computation

Open Access Proceedings

Discovering gene functional relationships using FAUN (Feature Annotation Using Nonnegative matrix factorization)

Elina Tjioe1, Michael W Berry1* and Ramin Homayouni2

Author Affiliations

1 Department of Electrical Engineering and Computer Science and Graduate School of Genome Science and Techonology, University of Tennessee, Knoxville, TN 37996, USA

2 Department of Biology, Bioinformatics Program, University of Memphis, Memphis, TN 38152, USA

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BMC Bioinformatics 2010, 11(Suppl 6):S14  doi:10.1186/1471-2105-11-S6-S14

Published: 7 October 2010

Abstract

Background

Searching the enormous amount of information available in biomedical literature to extract novel functional relationships among genes remains a challenge in the field of bioinformatics. While numerous (software) tools have been developed to extract and identify gene relationships from biological databases, few effectively deal with extracting new (or implied) gene relationships, a process which is useful in interpretation of discovery-oriented genome-wide experiments.

Results

In this study, we develop a Web-based bioinformatics software environment called FAUN or Feature Annotation Using Nonnegative matrix factorization (NMF) to facilitate both the discovery and classification of functional relationships among genes. Both the computational complexity and parameterization of NMF for processing gene sets are discussed. FAUN is tested on three manually constructed gene document collections. Its utility and performance as a knowledge discovery tool is demonstrated using a set of genes associated with Autism.

Conclusions

FAUN not only assists researchers to use biomedical literature efficiently, but also provides utilities for knowledge discovery. This Web-based software environment may be useful for the validation and analysis of functional associations in gene subsets identified by high-throughput experiments.