MVCL: A Contrastive Learning Model with Multi-view Networks for Driver Gene Prediction

The identification of cancer driver genes is crucial for in elucidating the molecular pathogenesis of carcinogenesis and advancing precision oncology interventions. Although progress has been made in integrating multi-omics data, which has enhanced the predictive ability regarding cancer driver genes, current methods still have their limitations. They merely concentrate on local sample pairs within a single view or employ a fixed number of graph convolutional network layers, which are hard to adapt to the constraints of diverse biological networks. In response to these challenges, this paper introduces a multi-view contrastive learning method (MVCL) to distinguish cancer driver gene. The MVCL first constructs four distinct gene relationship networks from distinct dimensions: a Protein-Protein Interaction network, a Gene Ontology network, a pathway co-occurrence network, and a protein sequence similarity network. To accommodate the varying connection densities across different network views, a topology-adaptive encoder is designed. It dynamically adjusts GCN layer numbers based on the radius of the largest connected subgraph in each view. Feature-level and cluster-level contrastive loss functions are also introduced. They ensure consistent gene feature representation from both local and overall view. Experimental results demonstrate that MVCL significantly enhances the area under the ROC curve and the area under the precision-recall curve for identifying driver genes for pan - cancer and specific cancer types compared to existing methods. In general, MVCL shows great potential in the realm of precision tumor therapy and is applicable to predicting biomarkers of diverse complicated diseases.