OmicsGAT: Graph Attention Network for Cancer Subtype Analyses.

ABSTRACT: The use of high-throughput omics technologies is becoming increasingly popular in all facets of biomedical science. The mRNA sequencing (RNA-seq) method reports quantitative measures of more than tens of thousands of biological features. It provides a more comprehensive molecular perspective of studied cancer mechanisms compared to traditional approaches. Graph-based learning models have been proposed to learn important hidden representations from gene expression data and network structure to improve cancer outcome prediction, patient stratification, and cell clustering. However, these graph-based methods cannot rank the importance of the different neighbors for a particular sample in the downstream cancer subtype analyses. In this study, we introduce omicsGAT, a graph attention network (G

SUBMITTER: Baul S 

PROVIDER: S-EPMC9499656 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature