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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 analyzing the gene clusters for various diseases. However, more and more evidence suggest that human diseases are not isolated from each other. Therefore, it’s significant and interesting to detect the common functional gene clusters driving the core mechanisms among multiple related diseases. There are mainly two challenges for this task: first, the gene expression from each disease may contain noise; second, the common factors underlying multiple diseases are hard to detect.
To address these challenges, we propose a novel deep architecture to discover the mutual functional gene clusters across multiple types of diseases. The proposed deep architecture is represented as a multilayer network: each disease’s gene expression pattern is well represented by some hidden factors in the first layer; then, the common hidden factors across diseases are captured in the second layer; finally, the genes are grouped into multiple clusters in the third layer based on their common hidden factors from the second layer. To demonstrate that the proposed method can discover precise and meaningful gene clusters which are not directly obtainable from traditional methods, we perform extensive experimental studies on both synthetic and
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 analyzing the gene clusters for various diseases. However, more and more evidence suggest that human diseases are not isolated from each other. Therefore, it’s significant and interesting to detect the common functional gene clusters driving the core mechanisms among multiple related diseases. There are mainly two challenges for this task: first, the gene expression from each disease may contain noise; second, the common factors underlying multiple diseases are hard to detect.
To address these challenges, we propose a novel deep architecture to discover the mutual functional gene clusters across multiple types of diseases. The proposed deep architecture is represented as a multilayer network: each disease’s gene expression pattern is well represented by some hidden factors in the first layer; then, the common hidden factors across diseases are captured in the second layer; finally, the genes are grouped into multiple clusters in the third layer based on their common hidden factors from the second layer. To demonstrate that the proposed method can discover precise and meaningful gene clusters which are not directly obtainable from traditional methods, we perform extensive experimental studies on both synthetic and