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Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03
Mining microarray gene expression data is an important research topic in bioinformatics with broad applications. While most of the previous studies focus on clustering either genes or samples, it is interesting to ask whether we can partition the complete set of samples into exclusive groups (called phenotypes) and find a set of informative genes that can manifest the phenotype structure. In this paper, we propose a new problem of simultaneously mining phenotypes and informative genes from genedoi:10.1145/956804.956835 fatcat:5gm3t7lwtzaknb6bws7xxhr6ja