Bioinformatics Advance Access published online on February 22, 2005
Bioinformatics, doi:10.1093/bioinformatics/bti339
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1 Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-43 Aomi Koto-ku, Tokyo, Japan
* To whom correspondence should be addressed.
Motivation: Inferring networks of proteins from biological data is a central issue of computational biology. Most network inference methods, including Bayesian networks, take unsupervised approaches in which the network is totally unknown in the beginning, and all the edges have to be predicted. Yamanishi et al. [2004] recently proposed a more realistic supervised framework that assumes that a substantial part of the network is known. We propose a new kernel-based method for supervised graph inference based on multiple types of biological datasets such as gene expression, phylogenetic profiles, and amino acid sequences. Notably, our method assigns a weight to each type of dataset and thereby selects informative ones. Data selection is useful to reduce data collection costs. For example, when a similar network inference problem must be solved for other organisms, the dataset excluded by our algorithm need not to be collected. Results: First, we formulate supervised network inference as a kernel matrix completion problem, where the inference of edges boils down to estimation of missing entries of a kernel matrix. Then, an EM algorithm is proposed to simultaneously infer the missing entries of the kernel matrix and the weights of multiple datasets. By introducing the weights, we can integrate multiple datasets selectively and thereby exclude irrelevant and noisy datasets. Our approach is favorably tested in two biological networks: a metabolic network and a protein interaction network. Availability: A supplementary report including mathematical details is available at www.cbrc.jp/~kato/faem/faem.html. Software is available on request.
Received November 30, 2004
Revised February 15, 2005
Accepted February 17, 2005
Article
Selective integration of multiple biological data for supervised network inference
2 Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-43 Aomi Koto-ku, Tokyo, Japan; Max Planck Institute for Biological Cybernetics, Spemannstr. 38, 72076 Tübingen, Germany
3 Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-43 Aomi Koto-ku, Tokyo, Japan; Graduate School of Frontier Sciences, University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa, 277-8562, Japan
Tsuyoshi Kato, E-mail: kato-tsuyoshi{at}aist.go.jp
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