NESM: a network embedding method for tumor stratification by integrating multi-omics data.

ABSTRACT: Tumor stratification plays an important role in cancer diagnosis and individualized treatment. Recent developments in high-throughput sequencing technologies have produced huge amounts of multi-omics data, making it possible to stratify cancer types using multiple molecular datasets. We introduce a Network Embedding method for tumor Stratification by integrating Multi-omics data. Network Embedding method for tumor Stratification by integrating Multi-omics pregroup the samples, integrate the gene features and somatic mutation corresponding to cancer types within each group to construct patient features, and then integrate all groups to obtain comprehensive patient information. The gene features contain network topology information, because it is extracted by integrating deoxyribonucleic aci

SUBMITTER: Li F 

PROVIDER: S-EPMC9635646 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature