Our research focuses on the development of machine learning techniques for application to problems in molecular biology. We approach these problems using Bayesian techniques such as hidden Markov models, as well as support vector machines and related, non-Bayesian methods. Much of our work addresses two core problems in machine learning: incorporating domain-specific prior knowledge and learning from heterogeneous data. We apply our techniques to problems such as automatic gene finding, microarray expression analysis, gene functional classification, and protein remote homology detection.
Asa Ben-Hur and William Stafford Noble. "Kernel methods for predicting protein-protein interactions." Bioinformatics (Proceedings of the Intelligent Systems for Molecular Biology Conference). 21(Suppl 1):i38-i46, 2005.
William Stafford Noble, Scott Kuehn, Robert Thurman, Richard Humbert, James C. Wallace, Mann Yu, Michael Hawrylycz and John Stamatoyannopoulos. "Predicting the in vivo signature of human gene regulatory sequences." Bioinformatics (Proceedings of the Intelligent Systems for Molecular Biology Conference). 21(Suppl 1):i338-i343, 2005.
Weston, Jason, Andre Eliseeff, Dengyong Zhou, Christina Leslie and William Stafford Noble. "Protein ranking: from local to global structure in the protein similarity network." Proceedings of the National Academy of Science. 101(17):6559-6563, 2004.
Lanckriet, Gert R. G., Minghua Deng, Nello Cristianini, Michael I. Jordan and William Stafford Noble. "Kernel-based data fusion and its application to protein function prediction in yeast." Proceedings of the Pacific Symposium on Biocomputing, January 3-8, 2004. pp. 300-311.
Bailey, Timothy and William Stafford Noble. "Searching for statistically significant regulatory modules" Bioinformatics (Proceedings of the European Conference on Computational Biology). 19(Suppl. 2):ii16-ii25, 2003.
Anderson, David C., Weiqun Li, Donald G. Payan and William Stafford Noble. "A new algorithm for the evaluation of shotgun peptide sequencing in proteomics: support vector machine classification of peptide MS/MS spectra and SEQUEST scores." Journal of Proteome Research. 2(2):137-146, 2003.
Liao, Li and William Stafford Noble. "Combining pairwise sequence similarity and support vector machines for remote protein homology detection". Proceedings of the Sixth Annual International Conference on Research in Computational Molecular Biology, April 18-21, 2002. pp. 225-232.
Pavlidis, Paul, Jason Weston, Jinsong Cai and William Stafford Noble. "Learning gene functional classifications from multiple data types." Journal of Computational Biology. 9(2):401-411, 2002.
Brown, Michael P. S., William Noble Grundy, David Lin, Nello Cristianini, Charles Sugnet, Terrence S. Furey, Manuel Ares, Jr., and David Haussler. "Knowledge-based analysis of microarray gene expression data using support vector machines." Proceedings of the National Academy of Science. 97(1):262-267, 2000.
Bailey, Timothy L. and William Noble Grundy. "Classifying proteins by family using the product of correlated p-values". Proceedings of the Third International Conference on Computational Molecular Biology (RECOMB99), April 11-14, 1999. pp. 10-14.
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