ゲノム情報科学研究教育機構  アブストラクト
Date March 12, 2007
Speaker Prof. Shin Ishii, Graduate School of Information Sciece, Nara Institute of Science and Technology
Title Machine learning approaches to gene expression analyses
Abstract   Analyses of gene expression profiling data are challenging, because of the relatively small amount of data in comparion to the underlying high-dimensionality. In this talk, I first present a missing value estimation method based on Bayesian principal component analysis (PCA), which assumes that the gene expression data are generated by adding high-dimensional noise to low-dimensional factors. Next, I present a correlation-based clustering method whose component model is a constrained version of probabilistic PCA. Bayesian estimation provides a statistically consistent estimation of the model and parameters from actual data. Finally, I present a multi-class classification method by integrating binary classifiers based on the analogy from information transmission theory. The results when applied to cancer classification problems from gene expression profiling are shown.
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