Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration.

ABSTRACT: Accurate molecular subtypes prediction of cancer patients is significant for personalized cancer diagnosis and treatments. Large amount of multi-omics data and the advancement of data-driven methods are expected to facilitate molecular subtyping of cancer. Most existing machine learning-based methods usually classify samples according to single omics data, fail to integrate multi-omics data to learn comprehensive representations of the samples, and ignore that information transfer and aggregation among samples can better represent them and ultimately help in classification. We propose a novel framework named multi-omics graph convolutional network (M-GCN) for molecular subtyping based on robust graph convolutional networks integrating multi-omics data. We first apply the Hilbert-Schmidt in

SUBMITTER: Yin C 

PROVIDER: S-EPMC9137453 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature