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

@article{citeulike:5485496, 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@mist.i.u-tokyo.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btp494}, author = {Kashima, Hisashi and Yamanishi, Yoshihiro and Kato, Tsuyoshi and Sugiyama, Masashi and Tsuda, Koji}, citeulike-article-id = {5485496}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btp494}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/content/btp494v1/.abstract}, citeulike-linkout-2 = {http://bioinformatics.oxfordjournals.org/content/btp494v1/.full.pdf}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/19689962}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=19689962}, day = {15}, doi = {10.1093/bioinformatics/btp494}, journal = {Bioinformatics}, keywords = {evolution, species\_specific\_nw}, month = {November}, number = {22}, pages = {2962--2968}, posted-at = {2009-10-16 06:46:51}, priority = {2}, title = {Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach}, url = {http://dx.doi.org/10.1093/bioinformatics/btp494}, volume = {25}, year = {2009} }

TY - JOUR ID - citeulike:5485496 L3 - citeulike-article-id:5485496 N2 - 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. 10.1093/bioinformatics/btp494 IS - 22 JF - Bioinformatics EP - 2968 TI - Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach VL - 25 SP - 2962 KW - evolution KW - species_specific_nw AU - Kashima, Hisashi AU - Yamanishi, Yoshihiro AU - Kato, Tsuyoshi AU - Sugiyama, Masashi AU - Tsuda, Koji PY - 2009/11/15/ UR - http://dx.doi.org/10.1093/bioinformatics/btp494 ER -