MICROARRAY GENE EXPRESSION BICLUSTERING USING ASSOCIATIVE PATTERN MINING

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. One of the daunting challenges facing bioinformatics is to assign biochemical and cellular functions to the thousands of hitherto uncharacterized gene products discovered by several international gene-sequencing projects. Similarly, microarray gene expression analysis, an important component in the design of in-silico molecular medicine methods, has made possible to monitor the expression level of thousands of genes under different samples (conditions) at the same time. Extraction of biologically significant knowledge from the gene expression data is a growing computational challenge as the large number of genes, which can correspond to different time sequences or tissue types, have a dimensionality that is several orders of magnitude more than the evaluated samples. An important analysis aim is to identify sets of genes that are correlated, and share similar pattern and biological properties such as regulation and function. We selected and ranked the genes based on their predictive power to classify samples into functional categories by applying eight statistical measures on a cancer dataset. The ranked sets of genes were then studied for the associations between them. The discovered associations were clustered by their similarity ranking measures and compared for their efficacy by running several sets of experiments. A biomedical literature search was conducted to study the functional annotation of the discovered genes. The future work involves the accomplishment of the biclustering algorithm with validation results using published cluster statistics, information gain schemas, and expert evaluation of the discovered genes and relevant conditions for their biological significance and characterization.