SMG: self-supervised masked graph learning for cancer gene identification.

ABSTRACT: Cancer genomics is dedicated to elucidating the genes and pathways that contribute to cancer progression and development. Identifying cancer genes (CGs) associated with the initiation and progression of cancer is critical for characterization of molecular-level mechanism in cancer research. In recent years, the growing availability of high-throughput molecular data and advancements in deep learning technologies has enabled the modelling of complex interactions and topological information within genomic data. Nevertheless, because of the limited labelled data, pinpointing CGs from a multitude of potential mutations remains an exceptionally challenging task. To address this, we propose a novel deep learning framework, termed self-supervised masked graph learning (SMG), which comprises SMG re

SUBMITTER: Cui Y 

PROVIDER: S-EPMC10639095 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature