A multimodal graph neural network framework for cancer molecular subtype classification.

ABSTRACT:

Background

The recent development of high-throughput sequencing has created a large collection of multi-omics data, which enables researchers to better investigate cancer molecular profiles and cancer taxonomy based on molecular subtypes. Integrating multi-omics data has been proven to be effective for building more precise classification models. Most current multi-omics integrative models use either an early fusion in the form of concatenation or late fusion with a separate feature extractor for each omic, which are mainly based on deep neural networks. Due to the nature of biological systems, graphs are a better structural representation of bio-medical data. Although few graph neural network (GNN) based multi-omics integrative methods have been proposed, they suffer from three co

SUBMITTER: Li B 

PROVIDER: S-EPMC10789042 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature