Interpretable multi-instance heterogeneous graph network learning modelling CircRNA-drug sensitivity association prediction.

Abstract

Background: Different expression levels of circular RNAs (circRNAs) affect the sensitivity of human cells to drugs, thus producing different responses to the therapeutic effects of drugs. Using traditional biomedical experiments to discover and confirm sensitivity relationships is not only time-consuming but also costly. Therefore, developing an effective method to accurately predict new associations between circRNAs and drug sensitivity is crucial and urgent. Therefore, we constructed a heterogeneous graph network MiGNN2CDS on the basis of multi-instance learning (MIL).

Results: We first extracted similar features of circRNAs and drugs and the structural features of drugs to construct a heterogeneous network. To learn the deep embedding features of the heterogeneous network, we designed a heterogeneous graph convolutional network (GCN) architecture. By introducing instance learning, we subsequently designed a pseudo-metapath instance generator and a bidirectional translation embedding projector BiTrans to learn the metapath-level representation of circRNA-drug pairs. Finally, an interpretable multiscale attention network joint predictor was designed to achieve accurate prediction and interpretable analysis of circRNA-drug sensitivity associations.

Conclusions: MiGNN2CDS achieves better prediction accuracy than many state-of-the-art models do. Case studies show that MiGNN2CDS can effectively predict unknown associations, and the model interpretability of MiGNN2CDS is verified by high-confidence meta-path analysis. The code and data are available at https://github.com/nmt315320/MiGNN2CDS.git .