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High-throughput data such as microarrays make it possible to investigate the molecular-level mechanism of cancer more efficiently. Computational methods boost the microarray analysis by managing large and complex data systematically. However, combinatorial interactions among genes have not been considered as a unit of the analysis since previous methods mainly focus on a whole gene or a single isolated...
High-throughput microarrays inform us on different outlooks of the molecular mechanisms underlying the function of cells and organisms. While computational analysis for the microarrays show good performance, it is still difficult to infer modules of multiple co-regulated genes. Here, we present a novel classification method to identify the gene modules associated with cancers from microarray data...
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