Biocomputing 2002 by Russ B. Altman, A. Keith Dunker, Lawrence Hunter, Kevin

By Russ B. Altman, A. Keith Dunker, Lawrence Hunter, Kevin Lauderdale, T. E. D. Klein, Russ Altman, Teri E. Klein

Graduate scholars, teachers and industrialists in bioinformatics. The Pacific Symposium on Biocomputing brings jointly key researchers from the foreign biocomputing neighborhood. it really is designed to be maximally conscious of the necessity for serious mass in subdisciplines inside biocomputing. This ebook comprises peer-reviewed articles in computational biology.

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More structure can be imposed by placing constraints on f5ro (r = 1 , . . , J — 1). For example, we could set (3r0 = /3o for all r. This corresponds to a one-unit change in expression level for any gene having the same effect for discriminating any two tumor classes. In a typical microarray experiment, it is not possible to estimate the parameters in (1) using standard statistical methods because p is much larger than n. We propose using the singular value decomposition to reduce the dimension of /3r0.

Ben-Hur, D. T. Siegelmann, and V. Vapnik, "A support vector method for hierarchical clustering" In Advances in Neural Information Processing Systems 13 367-373 (MIT Press, 2000). 14. Y. R. L. Ruzzo, "Validating clustering for gene expression data" Bioinformatics 17(4), 309-318 (2001). 15. K. C. Dubes, Algorithms for clustering data (Prentice Hall, Englewood Cliffs, NJ, 1988). 16. S. N. Grundy, D. Lin, N. Cristianini, C. Sugnet, M. Ares, and D. Haussler, "Knowledge-based analysis of microarray gene expression data by using support vector machines" Proc.

Nat Med 4, 844-847. 6. P. J. Park, M. Pagano and M. Bonetti. (2000). A nonparametric scoring algorithm for identifying informative genes from microarray data. In Proc Pac Symp Biocomputing. 29 7. V. G. Tusher, R. Tibshirani and G. Chu. (2001) Significance analysis of microarrays applied to ionizing response. Proc Nat Acad Sciences 98, 5116-5121. 8. S. Raychaudhuri, J. M. Stuart and R. Altman. (2000). Principal components analysis to summarize microarray experiments: application to sporulation time series.

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