Gene Expression Classification

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Presentation transcript:

Gene Expression Classification Y.A. Qi, T.P. Minka, R.W. Picard, and Z. Ghahramani Genes Test error rate Test error rate Subjects Features Features Task Classify gene expression datasets into different categories, f.g., normal v.s. cancer. Challenge Thousands of genes are measured in the micro-array data, while only a small subset of genes are believed to be relevant for the classification tasks. Approach Predictive Automatic Relevance Determination. This method brings Bayesian tools to bear on the problem of selecting which genes are relevant. Classifying Leukemia Data Classifying Colon Cancer Task Distinguish acute myeloid leukemia (AML) from acute lymphoblastic leukemia (ALL). Data 47 and 25 samples of type ALL and AML respectively with 7129 features per sample. The dataset was randomly split 100 times into 36 training and 36 testing samples, with the new method run on each. Task Discriminate tumor from normal tissues using microarray data. Data 22 normal and 40 cancer samples with 2000 features per sample. We randomly split the dataset into 50 training and 12 testing samples 100 times and run the new method on each partition.