Module-level Gene-drug Interaction Identification via Hierarchical Optimal Transport

With the development of high-throughput technologies, a massive scale of pharmacological and genomic data has been accumulated, which enables the discovery of the correlation between oncogenic genes and therapeutic drugs. Generally, genes with similar functions tend to be related to similar drugs and vice versa. Previous methods detect such associations between gene modules and drug modules based on the similarity of individual genes and drugs, resulting in an inaccurate capture of module-level interactions. How to fully leverage the underlying modules within genes and drugs is a key challenge when identifying regulatory relationships between gene and drug modules. In this paper, we propose a module-level gene-drug interaction identification model via hierarchical optimal transport (H-OT). Particularly, prior knowledge of genes or drugs is integrated to uncover the underlying module of genes or drugs with similar biological functions. Moreover, the optimal associations between gene and drug modules are determined by minimizing high-level OT distance between them, the cost function specified in high-level OT is automatically learned by low-level OT, which incorporates module patterns within genes and drugs. Experiments conducted on synthetic datasets demonstrate that our model exhibits superior performance than six state-of-the-art methods. Additionally, our evaluation of real drug-gene data highlights the model’s statistical power. The gene-drug modules identified by our approach reveal closely related gene-drug interactions and significantly enrich pathways associated with cancer.