Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks.
We propose a statistical method to estimate gene networks from DNA microarray data and protein-protein interactions. Because physical interactions between proteins or multiprotein complexes are likely to regulate biological processes, using only mRNA expression data is not sufficient for estimating a gene network accurately. Our method adds knowledge about protein-protein interactions to the estimation method of gene networks under a Bayesian statistical framework. In the estimated gene network, a protein complex is modeled as a virtual node based on principal component analysis. We show the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle data. The proposed method improves the accuracy of the estimated gene networks, and successfully identifies some biological facts.
Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.
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N
Nariai
S
S
Kim
S
S
Imoto
S
S
Miyano
D000465:Algorithms; D001499:Bayes Theorem; D002453:Cell Cycle / Q000235:genetics; D019295:Computational Biology; D005800:Genes, Fungal; D023281:Genomics / Q000706:statistics & numerical data; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D011485:Protein Binding; D011506:Proteins / Q000235:genetics* / Q000378:metabolism*; D040901:Proteomics / Q000706:statistics & numerical data; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism
D011506:Proteins; D029701:Saccharomyces cerevisiae Proteins