document: Mining phenotypes and informative genes from gene expression data 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 gene expression data . Some statistics-based metrics are proposed to measure the quality of the mining results . Two interesting algorithms are developed : the heuristic search and the mutual reinforcing adjustment method . We present an extensive performance study on both real-world data sets and synthetic data sets . The mining results from the two proposed methods are clearly better than those from the previous methods . They are ready for the real-world applications . Between the two methods , the mutual reinforcing adjustment method is in general more scalable , more effective and with better quality of the mining results .
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