HOGMMNC: a higher order graph matching with multiple network constraints model for gene-drug regulatory modules identification

Motivation: The emergence of large amounts of genomic, chemical, and pharmacological data provides new opportunities and challenges. Identifying gene-drug associations is not only crucial in providing a comprehensive understanding of the molecular mechanisms of drug action, but is also important in the development of effective treatments for patients. However, accurately determining the complex associations among pharmacogenomic data remains challenging. We propose a higher order graph matching with multiple network constraints (HOGMMNC) model to accurately identify gene-drug modules. The HOGMMNC model aims to capture the inherent structural relations within data drawn from multiple sources by hypergraph matching. The proposed technique seamlessly integrates prior constraints to enhance the accuracy and reliability of the identified relations. An effective numerical solution is combined with a novel sampling strategy to solve the problem efficiently. Results: The superiority and effectiveness of our proposed method are demonstrated through a comparison with four state-of-the-art techniques using synthetic and empirical data. The experiments on synthetic data show that the proposed method clearly outperforms other methods, especially in the presence of noise and irrelevant samples. The HOGMMNC model identifies eighteen gene-drug modules in the empirical data. The modules are validated to have significant associations via pathway analysis. Significance: The modules identified by HOGMMNC provide new insights into the molecular mechanisms of drug action and provide patients with more effective treatments. Our proposed method can be applied to the study of other biological correlated module identification problems (e.g. miRNA-gene, gene-methylation, and gene-disease).