Supervised Inference of Metabolic Networks from the Integration of Genomic Data and Chemical Information

Elements of Computational Systems Biology

Volumn , Issue , 2010, Pages 189-211

  Yamanishi, Yoshihiro ^a  

^a PSL RESEARCH UNIVERSITY   (France)

Author keywords

Supervised inference of metabolic networks from genomic data and chemical information integration; Supervised network inference method with metric learning; Tackling supervised network inference problem from kernel matrix completion view

Indexed keywords

EID: 84864436418     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9780470556757.ch8     Document Type: Chapter

References (43)

1. H. Toh and K. Horimoto. Inference of a genetic network by a combined approach of cluster analysis and graphical Gaussian modeling. Bioinformatics, 18:287-297, 2002.
2. M.W. Covert, E. M. Knight, J. L. Reed, M. J. Herrgard, and B. O. Palsson. Integrating highthroughput and computational data elucidates bacterial networks. Nature, 429:92-96, 2004.
3. Z. Hu, J. Mellor, J. Wu, T. Yamada, D. Holloway, and C. Delisi. Visant: data-integrating visual framework for biological networks and modules. Nucleic Acids Res., 33:W352-W357, 2005.
4. C. von Mering, M. Huynen, D. Jaeggi, S. Schmidt, P. Bork, and B. Snel. String: a database of predicted functional associations between proteins. Nucleic Acids Res., 31:258-261, 2003.
5. M. Kanehisa, M. Araki, S. Goto, M. Hattori, M. Hirakawa, M. Itoh, T. Katayama, S. Kawashima, S. Okuda, T. Tokimatsu, and Y. Yamanishi. Kegg for linking genomes to life and the environment. Nucleic Acids Res., 36(Database issue):D480-484, 2008.
6. I. M. Keseler, J. Collado-Vides, S. Gama-Castro, J. Ingraham, S. Paley, I. T. Paulsen, M. Peralta-Gil, and P. D. Karp. Ecocyc: a comprehensive database resource for Escherichia coli. Nucleic Acids Res., 33(Database issue):D334-337, 2005.
7. P. D. Karp. Call for an enzyme genomics initiative. Genome Biol., 5:401, 2004.
8. A. Osterman and R. Overbeek. Missing genes in metabolic pathways: a comparative genomics approach. Curr. Opin. Chem. Biol., 7:238-251, 2003.
9. T. F. Smith and M. S. Waterman. Identification of common molecular subsequences. J. Mol. Biol., 147:195-197, 1981.
10. S. F. Altschul, W. Gish, W. Miller, E. Myers, and D. J. Lipman. Basic local alignment search tool. J. Mol. Biol., 215:403-410, 1990.
11. S. E. Brenner, C. Chothia, and T. J. P. Hubbard. Assessing sequence comparison methods with reliable structurally identified distant evolutionary relationships. Proc. Natl. Acad. Sci. USA, 95:6073-6078, 1998.
12. H. Bono, H. Ogata, S. Goto, and M. Kanehisa. Reconstruction of amino acid biosynthesis pathways from the complete genome sequence. Genome Res., 8:203-210, 1998.
13. A. J. Barrett, C. R. Canter, C. Liebecq, G. P. Moss, W. Saenger, N. Sharon, K. F. Tipton, P. Vnetianer, and V. F. G. Vliegenthart. Enzyme Nomenclature. Academic Press, San Diego, CA, 1992.
14. R. Overbeek, M. Fonstein, M. D'Souza, G. D. Pusch, and N. Maltsev. The use of gene clusters to infer functional coupling. Proc. Natl. Acad. Sci. USA, 96:2896-2901, 1999.
15. A. J. Enright, I. Iliopoulos, N. C. Kyrpides, and C. A. Ouzounis. Protein interaction maps for complete genomes based on gene fusion events. Nature, 402:25-26, 1999.
16. M. Huynen, B. Snel,W. Lathe, and P. Bork. Predicting protein function by genomic context: quantitative evaluation and qualitative inferences. Genome Res., 10:1204-1210, 2000.
17. P. Kharchenko, D. Vitkup, and G. M. Church. Filling gaps in a metabolic network using expression information. Bioinformatics, 20:449-453, 2004.
18. M. L. Green and P. D. Karp. A bayesian method for identifying missing enzymes in predicted metabolic pathway databases. BMC Bioinform., 5:76, 2004.
19. Y. Yamanishi, J. P. Vert, and M. Kanehisa. Protein network inference from multiple genomic data: a supervised approach. Bioinformatics, 20(Suppl 1):i363-i370, 2004.
20. S. Goto, H. Bono, H. Ogata, W. Fujibuchi, T. Nishioka, K. Sato, and M. Kanehisa. Organizing and computing metabolic pathway data in terms of binary relations. Pacific Symp. Biocomput., 2:175-186, 1996.
21. J.-P. Vert and Y. Yamanishi. Supervised graph inference. Advances in Neural Information and Processing System, 2005, pp. 1433-1440.
22. T. Kato, K. Tsuda, and K. Asai. Selective integration of multiple biological data for supervised network inference. Bioinformatics, 21:2488-2495, 2005.
23. Y. Yamanishi and J. P. Vert. Kernel matrix regression. In Proceedings of the 12th International Conference on Applied Stochastic Models and Data Analysis, 2007.
24. A. Ben-Hur and W. S. Noble. Kernel methods for predicting protein-protein interactions. Bioinformatics, 21(Suppl 1):i38-i46, 2005.
25. Y. Yamanishi, J. P. Vert, and M. Kanehisa. Supervised enzyme network inference from the integration of genomic data and chemical information. Bioinformatics, 21(Suppl 1): i468-i477, 2005.
26. P. T. Spellman, G. Sherlock, M. Q. Zhang, V. R. Iyer, K. Anders, M. B. Eisen, P. O. Brown, D. Botstein, and B. Futcher. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell., 9:3273-3297, 1998.
27. M. B. Eisen, P. T. Spellman, O. B. Patrick, and D. Botstein. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA, 95:14863-14868, 1998.
28. W. K. Huh, J. V. Falvo, C. Gerke, A. S. Carroll, R. W. Howson, J. S. Weissman, and E. K. O'Shea. Global analysis of protein localization in budding yeast. Nature, 425:686-691, 2003.
29. M. Pellegrini, E. M. Marcotte, M. J. Thompson, D. Eisenberg, and T. O. Yeates. Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc. Natl Acad. Sci. USA, 96:4285-4288, 1999.
30. B. Scḧolkopf, K. Tsuda, and J. P. Vert. Kernel Methods in Computational Biology. MIT Press, 2004.
31. R. I. Kondor and J. Lafferty. Diffusion kernels on graphs and other discrete input. In Proceedings of International Conference on Machine Learning (ICML 2002), 2002, pp. 315-322.
32. J.-P. Vert and M. Kanehisa. Graph-driven features extraction from microarray data using diffusion kernels and kernel cca. Advances in Neural Information and Processing System, 2003, 1425-1432.
33. B. Scholkopf, A. J. Smola, and K.-R. Muller. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput., 10:1299-1319, 1998.
34. J. Shawe-Taylor and N. Cristianini. Kernel Methods for Pattern Analysis. Cambridge University Press, 2004.
35. S. Akaho. A kernel method for canonical correlation analysis. In International Meeting of Psychometric Society (IMPS), 2001.
36. F. R. Bach and M. I. Jordan. Kernel independent component analysis. J. Mach. Learning Res., 3:1-48, 2002.
37. G.Wahba. Splines Models for Observational Data: Series in Applied Mathematics. SIAM, Philadelphia, PA 1990.
38. F. Girosi, M. Jones, and T. Poggio. Regularization theory and neural networks architectures. Neural Comput., 7:219-269, 1995.
39. Y. Yamanishi, J. P. Vert, A. Nakaya, and M. Kanehisa. Extraction of correlated gene clusters from multiple genomic data by generalized kernel canonical correlation analysis. Bioinformatics, 19 (Suppl 1):i323-i330, 2003.
40. M. Kotera, Y. Okuno, M. Hattori, S. Goto, and M. Kanehisa. Computational assignment of the ec numbers for genomic-scale analysis of enzymatic reactions. J. Am. Chem. Soc., 126:16487-16498, 2004.
41. M. Gribskov and N. L. Robinson. Use of receiver operating characteristic (roc) analysis to evaluate sequence matching. Comput. Chem., 20:25-33, 1996.
42. Y. Yamanishi, H. Mihara, M. Osaki, H. Muramatsu, N. Esaki, T. Sato, Y. Hizukuri, S. Goto, and M. Kanehisa. Prediction of missing enzyme genes in a bacterial metabolic network: reconstruction of the lysine-degradation pathway of pseudomonas aeruginosa. FEBS J., 274:2262-2273, 2007.
43. S. Okamoto, Y. Yamanishi, S. Ehira, S. Kawashima, K. Tonomura, and M. Kanehisa. Prediction of nitrogen metabolism-related genes in anabaena by kernel-based network analysis. Proteomics, 7:900-909, 2006.