Multiview representation learning for identification of novel cancer genes and their causative biological mechanisms.

ABSTRACT: Tumorigenesis arises from the dysfunction of cancer genes, leading to uncontrolled cell proliferation through various mechanisms. Establishing a complete cancer gene catalogue will make precision oncology possible. Although existing methods based on graph neural networks (GNN) are effective in identifying cancer genes, they fall short in effectively integrating data from multiple views and interpreting predictive outcomes. To address these shortcomings, an interpretable representation learning framework IMVRL-GCN is proposed to capture both shared and specific representations from multiview data, offering significant insights into the identification of cancer genes. Experimental results demonstrate that IMVRL-GCN outperforms state-of-the-art cancer gene identification methods and several b

SUBMITTER: Yang J 

PROVIDER: S-EPMC11361854 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature