Journal Article

Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach

Hisashi Kashima, Yoshihiro Yamanishi, Tsuyoshi Kato, Masashi Sugiyama and Koji Tsuda

in Bioinformatics

Volume 25, issue 22, pages 2962-2968
Published in print November 2009 | ISSN: 1367-4803
Published online August 2009 | e-ISSN: 1460-2059 | DOI: https://dx.doi.org/10.1093/bioinformatics/btp494
Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach

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Motivation: The existing supervised methods for biological network inference work on each of the networks individually based only on intra-species information such as gene expression data. We believe that it will be more effective to use genomic data and cross-species evolutionary information from different species simultaneously, rather than to use the genomic data alone.

Results: We created a new semi-supervised learning method called Link Propagation for inferring biological networks of multiple species based on genome-wide data and evolutionary information. The new method was applied to simultaneous reconstruction of three metabolic networks of Caenorhabditis elegans, Helicobacter pylori and Saccharomyces cerevisiae, based on gene expression similarities and amino acid sequence similarities. The experimental results proved that the new simultaneous network inference method consistently improves the predictive performance over the individual network inferences, and it also outperforms in accuracy and speed other established methods such as the pairwise support vector machine.

Availability: The software and data are available at http://cbio.ensmp.fr/∼yyamanishi/LinkPropagation/.

Contact: kashima@mist.i.u-tokyo.ac.jp

Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  5356 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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