Towards Identification of Genes Contributing to Similarity of Patients' Multi-Omics Profiles: A Case Study of Acute Myeloid Leukemia.

We propose a computational framework for selecting biologically plausible genes identified by clustering of multi-omics data that reveal patients' similarity, thus giving researchers a more comprehensive view on any given disease. We employ spectral clustering of a similarity network created by fusion of three similarity networks, based on mRNA expression of immune genes, miRNA expression and DNA methylation data, using SNF_v2.1 software.

Predicting novel genomic regions linked to genetic disorders using GWAS and chromosome conformation data - a case study of schizophrenia.

Genome-wide association studies identified numerous loci harbouring single nucleotide polymorphisms (SNPs) associated with various human diseases, although the causal role of many of them remains unknown. In this paper, we postulate that co-location and shared biological function of novel genes with genes known to associate with a specific phenotype make them potential candidates linked to the same phenotype ("guilt-by-proxy"). We propose a novel network-based approach for predicting candidate genes/genomic regions utilising the knowledge of the 3D architecture of the human genome and GWAS data.