Multi-block and Multi-task Learning for Integrative Genomic Study

The importance of an integrative genomic study is steadily increasing in an emerging era of various high-throughput genomic data. Mechanisms of human diseases consist of complex interactions of multiple biological processes such as genetic, epigenetic, and transcriptional regulation. The collection of the multiple genomic data that represents the multiple processes is called 'multi-block data'. The multi-block data profiled from human disease samples provide comprehensive global snapshots of the diseases. Due to the rapid development of high-throughput technologies, the integrative genomic study using the multi-block data has been more highlighted than ever. However, in spite of its importance, there are only a few methodologies that can analyze such data. In this paper, we propose a novel Multi-Block and Multi-Task Learning (MBMTL) method for the integrative genomic study. We consider Single Nucleotide Polymorphism (SNP), Copy Number Variation (CNV), DNA methylation, and gene expression data as the multi-block data from four group samples of three major psychiatric disorders as well as data from a normal control. MBMTL identifies biomarkers that play important roles in explaining mechanisms of the human diseases from the multi-block data. We also take a multi-task problem into account so that we can identify different functions of the mechanisms. The performance of the proposed MBMTL was assessed by comparing it to a number of existing multi-block methods through simulation studies. We applied MBMTL to the multi-block data of the major psychiatric disorder samples.