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Detecting Mutual Functional Gene Clusters from Multiple Related Diseases Nan Du∗ , Xiaoyi Li∗ , Yuan Zhang† and Aidong Zhang∗ ∗ Computer Science and Engineering Department State University of New York at Buffalo, Buffalo, U.S.A nandu,xiaoyili,azhang@buffalo.edu † College of Electronic Information and Control Engineering Beijing University of Technology Beijing, China zhangyuan@emails.bjut.edu.cn Abstract—Discovering functional gene clusters based on gene expression data has been a widely-used method that offers a tremendous opportunity for understanding the functional genomics of a specific disease. Due to its strong power of comprehending and interpreting mass of genes, plenty of studies have been done on detecting and…show more content…
M ETHODOLOGY In this section, we present our deep architecture MGCD for discovering the mutual gene clusters across multiple related diseases. We begin by overviewing the problem definition in Section II-A. Section II-B discusses the method to well represent a single disease with multiple hidden factors (i.e. the first layer in the proposed architecture). The methods of extracting mutual hidden factors that are shared among the diseases (i.e. the second layer in the proposed architecture) and clustering the observed genes into clusters (i.e. the third layer in the proposed architecture) are presented in Section II-C and Section II-D, respectively. A. PROBLEM SETTING We are considering the problem of discovering the mutual gene clusters across multiple related diseases. To address this problem, we propose a deep architecture, whose goal is to group the genes into clusters. Therefore our task is summarized as follows: Suppose we have a set of gene expression data { } W = W 1 , ..., W C from C different types of diseases. Each gene expression data W c (1 ≤ c ≤ C) is represented as an N × S c expression matrix, where N denotes the gene number, S c denotes the number of samples for the c-th disease, and c each cell wij in W c is the measured expression level of i-th gene in j-th sample in the c-th disease. Note that although we assume the genes across different diseases (i.e. N) are the same, the samples from each specific disease (S c

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