nips nips2008 nips2008-225 nips2008-225-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Yoshihiro Yamanishi
Abstract: We formulate the problem of bipartite graph inference as a supervised learning problem, and propose a new method to solve it from the viewpoint of distance metric learning. The method involves the learning of two mappings of the heterogeneous objects to a unified Euclidean space representing the network topology of the bipartite graph, where the graph is easy to infer. The algorithm can be formulated as an optimization problem in a reproducing kernel Hilbert space. We report encouraging results on the problem of compound-protein interaction network reconstruction from chemical structure data and genomic sequence data. 1
[1] C.M. Dobson. Chemical space and biology. Nature, 432:824–828, 2004.
[2] M. Rarey, B. Kramer, T. Lengauer, and G. Klebe. A fast flexible docking method using an incremental construction algorithm. J Mol Biol, 261:470–489, 1996.
[3] Y. Yamanishi, J.P. Vert, and M. Kanehisa. Protein network inference from multiple genomic data: a supervised approach. Bioinformatics, 20 Suppl 1:i363–370, 2004.
[4] J.-P. Vert and Y. Yamanishi. Supervised graph inference. Advances in Neural Information and Processing System, pages 1433–1440, 2005.
[5] T. Kato, K. Tsuda, and K. Asai. Selective integration of multiple biological data for supervised network inference. Bioinformatics, 21:2488–2495, 2005.
[6] B. Sch¨ lkopf, K. Tsuda, and J.P. Vert. Kernel Methods in Computational Biology. MIT Press, 2004. o
[7] G. Wahba. Splines Models for Observational Data: Series in Applied Mathematics. SIAM, Philadelphia, 1990.
[8] F. Girosi, M. Jones, and T. Poggio. Regularization theory and neural networks architectures. Neural Computation, 7:219–269, 1995.
[9] J. Shawe-Taylor and N. Cristianini. Kernel Methods for Pattern Analysis. Camb. Univ. Press, 2004.
[10] M.J. Greenacre. Theory and applications of correspondence analysis. Academic Press, 1984.
[11] A. Globerson, G. Chechik, F. Pereira, and N. Tishby. Euclidean embedding of co-occurrence data. Advances in Neural Information and Processing System, pages 497–504, 2005.
[12] S. Akaho. A kernel method for canonical correlation analysis. International. Meeting on Psychometric Society (IMPS2001), 2001.
[13] F.R. Bach and M.I. Jordan. Kernel independent component analysis. Journal of Machine Learning Research, 3:1–48, 2002.
[14] M. Kanehisa, S. Goto, M. Hattori, K.F. Aoki-Kinoshita, M. Itoh, S. Kawashima, T. Katayama, M. Araki, and M. Hirakawa. From genomics to chemical genomics: new developments in kegg. Nucleic Acids Res., 34(Database issue):D354–357, Jan 2006.
[15] S. Gunther, S. Guenther, M. Kuhn, M. Dunkel, and et al. Supertarget and matador: resources for exploring drug-target relationships. Nucleic Acids Res, 2007.
[16] D.S. Wishart, C. Knox, A.C. Guo, D. Cheng, S. Shrivastava, D. Tzur, B. Gautam, and M. Hassanali. Drugbank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res, 2007.
[17] M. Hattori, Y. Okuno, S. Goto, and M. Kanehisa. Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. J. Am. Chem. Soc., 125:11853–11865, 2003.
[18] T.F. Smith and M.S. Waterman. Identification of common molecular subsequences. J Mol Biol, 147:195– 197, 1981.
[19] H. Saigo, J.P. Vert, N. Ueda, and T. Akutsu. Protein homology detection using string alignment kernels. Bioinformatics, 20:1682–1689, 2004. 8