Simple but effective methods for combining kernels in computational biology

RIVF 2008 - 2008 IEEE International Conference on Research, Innovation and Vision for the Future in Computing and Communication Technologies

Volumn , Issue , 2008, Pages 71-78

  Tanabe, Hiroaki ^a   Ho, Tu Bao ^a   Nguyen, Canh Hao ^a   Kawasaki, Saori ^a  

^a JAPAN ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; CHLORINE COMPOUNDS; COMMUNICATION; INTEGRATION; MATRIX ALGEBRA; OPTIMIZATION;

BIOLOGICAL DATA; COMMUNICATION TECHNOLOGIES; COMPARATIVE EVALUATIONS; COMPUTATIONAL BIOLOGY; COMPUTATIONAL COSTS; CROSS VALIDATIONS; DATA INTEGRATIONS; DATA SETS; DIVERSE DATA; FEATURE SPACES; HIGH-ACCURACY; INTERNATIONAL CONFERENCES; KERNEL MATRICES; KERNEL-BASED APPROACH; LARGE DATA SETS; LINEAR COMBINATION; MULTIPLE DATASETS; MULTIPLE KERNEL LEARNING; MULTIPLE KERNELS; OPTIMAL WEIGHTING; PROTEIN-PROTEIN INTERACTIONS; SEMI-DEFINITE PROGRAMMING; SEQUENTIAL MINIMAL OPTIMIZATION;

TECHNOLOGY;

EID: 51949093010     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/RIVF.2008.4586335     Document Type: Conference Paper

References (28)

1. S. Fields and O-K. Song. (1989), A novel genetic system to detect protein-protein interaction, Nature, Vol. 340, pp. 245-246.
2. Y. Ho, A Gruhler, A. Heilbut, G. D. Bader, L. Moore, S. L. Adams, A. Millar, P. Taylor, K. Bennet, and K. Boutilier. (2002), Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry, Nature, Vol. 415, pp. 180-183.
3. H. Zhu, M. Bilgin, R. Bangham, D. Hall, A. Casamayor, P. Bertone, N. Lan, R. Jansen, S. Bidlingmaier, and T. Houfek. (2001), Global analysis of protein actives using proteome chips, Science, Vol. 293, pp. 2101-2108.
4. T. Ito, T. Chiba, R. Ozawa, M. Yoshida, M. Hattori, and Y. Sakai. (2001), Comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc Natl Acad Sci, USA, Vol. 98, pp. 4569-4574.
5. P. Uetz, L. Giot, G. Cagney, T. A. Mansfield, R. S. Judson, J. R. Knight, D. Lockshon, V. Narayan, M. Srinivasan, P. Pochart, A. Qureshi-Emili, Y. Li, B. Godwin, D. Conover, T. Kalbfleisch, G. Vijayadamodar, M. Yang, M. Johnston, S. Fields, and J. M. Rothberg. (2000), A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature, Vol. 403, pp. 623-627.
6. L. R. Matthews, P. Vaglio, and J. Reboul. (2001), Identification of potential interaction networks using sequence-based searches for conserved protein-protein interactions or 'interologs', Genome Res, Vol. 30(1), pp. 31-34.
7. M. Deng, S. Mehta, F. Sun and T. Chen. (2002), Inferring domain-domain interactions from protein-protein interactions, Genome Research, Vol. 12(10), pp. 1540-1548.
8. D. Page and M. Craven. (2003), Biological Applications of Multi-Relational Data Mining, Appears in SIGKDD Explorations, special issue on Multi-Relational Data Mining
9. T. P. Nguyen and T. B. Ho. (2007), Combining domain fusions and domain-domain interactions to predict protein-protein interactions, The 7th International Workshop on Data Mining in Bioinformatics (BIOKDD '07), ACM SIGKDD '07, San Jose, CA, USA, pp. 27-34, ACM Press.
10. N. Cristianini and J. Shawe-Taylor. (2000), An Introduction to Support Vector Machines. Cambridge University Press, Cambridge, MA. MIT press.
11. B. Schölkoph and A. Smola. (2002), Learning with Kernels, MIT Press, Cambridge, MA.
12. V. N. Vapnik. (1998), Statistical Learning Theory, John Wiley & Sons.
13. Y. Yamanishi, J. P. Vert, and M. Kanehisa. (2004), Protein network inference from multiple genomic data: a supervised approach, Bioinformatics, Vol. 2, pp. 363-370.
14. P. Pavlidis, J. Weston, J. Cai, and W. S. Noble. (2002), Learning gene functional classifications from multiple data types, Journal of computational biology, Vol. 9, pp. 401-411.
15. A. Ben-Hur and W. S. Noble. (2005), Kernel methods for predicting protein-protein interactions, Bioinformatics (Proceedings of the Intelligent Systems for Molecular Biology Conference). Vol. 21, Suppl 1, pp. 38-46.
16. J. P. Vert, J. Qiu, and W. S. Noble. (2007), A new pairwise kernel for biological network inference with support vector machines, BMC Bioinformatics, Vol. 8 Sppl 10, pp. 8-18.
17. G. R. G. Lanckriet, N. Cristianini, L. EL. Ghaoui, P. Bartlett, and M. I. Jordan. (2004), Learning the kernel matrix with semi-definite programming, Journal of Machine Learning Research, Vol. 5, pp. 27-72.
18. F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan. (2004), Multiple kernel learning, conic duality, and the SMO algorithm, In C E Brodley, editor, Twenty-first international conference on Machine learning. ACM.
19. S. Sonnenbrug, G. Rötsch, C. Schäfer, and B. Schölkopf. (2006), Large scale multiple kernel learning, Journal of Machine Learning Research, Vol. 1, pp. 1-18.
20. C. H. Nguyen and T. B. Ho. (2007) Kernel Matrix Evaluation, Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 987-992.
21. D. P. Lewis, T. Jebra, and W. S. Noble. (2006), Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure, Bioinformatics Vol. 22(22), pp. 2753-2760.
22. C. Leslie, E. Eskin, and W. S. Noble. (2002), The spectrum kernel: A string kernel for SVM protein classification. Pacific Symposium on Biocomputing, Vol. 7, pp. 566-575.
23. Z. H. Zhou, J. X. Wu, Y. Jiang, S. F. Chen. (2001), Genetic Algorithm based Selective Neural Network Ensemble, Proceedings of the 17th International Joint Conference on Artificial Intelligence, Vol. 2, pp. 797-802.
24. G. R. G. Lanckriet, M. Deng, N. Cristianini, M. I. Jordan, and W. S. Noble. (2004), Kernel-based data fusion and its application to protein function prediction in yeast. In R B Altman, A K Dunker, L Hunter, T A Jung, T E Klein(Eds.), Proceedings of the Pacific Symposium on Biocomputing, pp. 300-311. World Scientific.
25. A. P. Gasch, P. T. Spellman, C. M. Kao, O. Carmel-Harel, M. B. Eisen, G. Storz, D. Botstein, and P. O. Brown. (2000). Genomic expression programs in the response of yeast cells to environmental changes. Molecular Biology of the Cell, Vol. 11, pp. 4241-4257.
26. W. K. Huh, F. V. Falve, L. C. Gerke, A. S. Carroll, R. W. Howson, J. S. Weissman, and E. K. O'Shea. (2003), Global analysis of protein localization in budding yeast, Nature, Vol. 425, pp. 686-691.
27. Y. Arava, Y. Wang, J. D. Storey, C. L. Liu, P. O. Brown, D. Herschlag. (2003). Genome-wide analysis of mRNA translation profiles in Saccharomyces cerevisiae, Proc Natl Acad Sci, USA, Vol. 100, pp. 3889-3894.
28. M. Deng, F. Sun, and T. Chen. (2003), Assessment of the reliability of protein-protein interactions and protein function prediction, Pac Symp Biocomput, pp. 140-151.