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


BACKGROUND
The software and data are available at http://cbio.ensmp.fr/~yyamanishi/LinkPropagation/.
Kashima
Hisashi
H
IBM Research, Tokyo Research Laboratory, 1623-14 Shimo-tsuruma, Yamato, Kanagawa 242-8502, Japan. kashima@mist.i.u-tokyo.ac.jp
Yamanishi
Yoshihiro
Y
Kato
Tsuyoshi
T
Sugiyama
Masashi
M
Tsuda
Koji
K
D000818:Animals; D005075:Biological Evolution; D017173:Caenorhabditis elegans / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D053263:Gene Regulatory Networks / Q000235:genetics*; D016678:Genome; D016480:Helicobacter pylori / Q000235:genetics; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D012441:Saccharomyces cerevisiae / Q000235:genetics