Bioinformatics Advance Access originally published online on August 17, 2009
Bioinformatics 2009 25(22):2962-2968; doi:10.1093/bioinformatics/btp494
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach
1IBM Research, Tokyo Research Laboratory, 1623-14 Shimo-tsuruma, Yamato, Kanagawa, 242-8502 Japan, 2Mines ParisTech, Centre for Computational Biology, 35 rue Saint-Honore, F-77305 Fontainebleau Cedex, France, 3Institut Curie, 4INSERM, U900, F-75248, Paris, France, 5Ochanomizu University, Center for Informational Biology, 2-1-1 Ohtsuka, Bunkyo-ku, Tokyo 112-8610, 6Tokyo Institute of Technology, Department of Computer Science, 2-12-1, O-okayama, Meguro-ku, Tokyo 152-8552 and 7National Institute of Advanced Industrial Science and Technology, Computational Biology Research Center (AIST), 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan
*To whom correspondence should be addressed.
 |
Abstract |
|---|
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{at}mist.i.u-tokyo.ac.jp
Supplementary information: Supplementary data are available at Bioinformatics online.
Present address: Department of Mathematical Informatics, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan.
Associate Editor: Jonathan Wren
Received on May 6, 2009; revised on July 11, 2009; accepted on August 1, 2009
CiteULike 
Connotea 
Del.icio.us What's this?