Optimized Combination of Multiple Graphs With Application to the Integration of Brain Imaging and (epi)Genomics Data.

ABSTRACT: With the rapid development of high-throughput technologies, a growing amount of multi-omics data are collected, giving rise to a great demand for combining such data for biomedical discovery. Due to the cost and time to label the data manually, the number of labelled samples is limited. This motivated the need for semi-supervised learning algorithms. In this work, we applied a graph-based semi-supervised learning (GSSL) to classify a severe chronic mental disorder, schizophrenia (SZ). An advantage of GSSL is that it can simultaneously analyse more than two types of data, while many existing models focus on pairwise data analysis. In particular, we applied GSSL to the analysis of single nucleotide polymorphism (SNP), functional magnetic resonance imaging (fMRI) and DNA methylation data, whi

SUBMITTER: Bai Y 

PROVIDER: S-EPMC7394342 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature