Detecting mutual functional gene clusters from multiple related diseases

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. 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 real datasets - public gene-expression data of three types of cancers. Experimental results show that the proposed approach is highly effective in discovering the mutual functional gene clusters.