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, Koji Tsuda

研究成果: ジャーナルへの寄稿学術誌査読

16 被引用数 (Scopus)

抄録

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.

本文言語英語
ページ(範囲)2962-2968
ページ数7
ジャーナルBioinformatics
25
22
DOI
出版ステータス出版済み - 11月 15 2009
外部発表はい

!!!All Science Journal Classification (ASJC) codes

  • 統計学および確率
  • 生化学
  • 分子生物学
  • コンピュータ サイエンスの応用
  • 計算理論と計算数学
  • 計算数学

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