Campus Units

Computer Science, Biomedical Sciences, Bioinformatics and Computational Biology, Animal Science

Document Type

Article

Publication Version

Published Version

Publication Date

4-29-2010

Journal or Book Title

BMC Bioinformatics

Volume

11

Issue

Supplement 3

First Page

S7

DOI

10.1186/1471-2105-11-S3-S7

Abstract

Background
Ortholog detection methods present a powerful approach for finding genes that participate in similar biological processes across different organisms, extending our understanding of interactions between genes across different pathways, and understanding the evolution of gene families.

Results
We exploit features derived from the alignment of protein-protein interaction networks and gene-coexpression networks to reconstruct KEGG orthologs for Drosophila melanogaster, Saccharomyces cerevisiae, Mus musculus and Homo sapiens protein-protein interaction networks extracted from the DIP repository and Mus musculus and Homo sapiens and Sus scrofa gene coexpression networks extracted from NCBI's Gene Expression Omnibus using the decision tree, Naive-Bayes and Support Vector Machine classification algorithms.

Conclusions
The performance of our classifiers in reconstructing KEGG orthologs is compared against a basic reciprocal BLAST hit approach. We provide implementations of the resulting algorithms as part of BiNA, an open source biomolecular network alignment toolkit.

Comments

This article is from BMC Bioinformatics 11 (2010): S7, doi:10.1186/1471-2105-11-S3-S7. Posted with permission.

Rights

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Copyright Owner

Towfic et al.

Language

en

File Format

application/pdf

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