Multi-omics data integration by generative adversarial network.

ABSTRACT:

Motivation

Accurate disease phenotype prediction plays an important role in the treatment of heterogeneous diseases like cancer in the era of precision medicine. With the advent of high throughput technologies, more comprehensive multi-omics data is now available that can effectively link the genotype to phenotype. However, the interactive relation of multi-omics datasets makes it particularly challenging to incorporate different biological layers to discover the coherent biological signatures and predict phenotypic outcomes. In this study, we introduce omicsGAN, a generative adversarial network model to integrate two omics data and their interaction network. The model captures information from the interaction network as well as the two omics datasets and fuse them to generate synt

SUBMITTER: Ahmed KT 

PROVIDER: S-EPMC10060730 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature