Classification of Schizophrenia by Iterative Random Forest Feature Selection Based on DNA Methylation Array Data Changes in DNA methylation are widely thought to be involved in the evolution of the disease, and most studies suggest that whole genome hypomethylation levels are widespread in patients with schizophrenia. Since the exploration of DNA methylation changes in the etiology and pathogenesis of schizophrenia will be important for the prevention and early intervention, it is crucial to identify differentially methylated sites with high specificity and sensitivity for disease classification. In this study, we present a comprehensive approach MethIRF for the DNA methylationbased classification of schizophrenia by iterative random forest feature selection. The results show that MethIRF has a powerful discrimination ability compared to other four criteria methods for detecting differentially methylation sites. Moreover, the proposed method can discover significant CpG sites associated with schizophrenia and explore changes in the biological mechanisms of diseases.

Using Convolutional Neural Network to Study the Regulatory Relationship Between DNA Methylation and Gene Expression In this work, we propose a novel framework based on convolutional neural networks to integrate information of DNA methylation and DNA sequences for studying their relationship with gene expression. Using the data from 8 cell types, we show that the trained models provide higher explained variation of gene expression from DNA methylation (up to 30%) compared to methods that only consider one genomic data type or from one region (up to 12%). In addition, the extracted sequence motifs from the filters of the first convolutional layers reveal the involvement of regulatory modulators including DNA Methyltransferase 1 (DNMT1) and other transcription factors, shedding light on the complex regulatory mechanisms of DNA methylation. We found that more than half of the 20 most enriched motifs correspond to regulators that are well supported by previous publications and ChIPSeq transcription factor binding datasets. Our work has demonstrated the usefulness of convolutional neural networks in revealing complex relationships from omics data.