DGNMDA: Dual Heterogeneous Graph Neural Network Encoder for miRNA-Disease Association Prediction

Abstract

In recent years, numerous studies have highlighted the pivotal importance of miRNAs in personalized healthcare, showcasing broad application prospects. miRNAs hold significant potential in disease diagnosis, prognosis assessment, and therapeutic target discovery, making them an integral part of precision medicine. They are expected to enable precise disease subtyping and risk prediction, thereby advancing the development of precision medicine. GNNs, a class of deep learning architectures tailored for graph data analysis, have greatly facilitated the advancement of miRNA-disease association prediction algorithms. However, current methods often fall short in leveraging network node information, particularly in utilizing global information while neglecting the importance of local information. Effectively harnessing both local and global information remains a pressing challenge. To tackle this challenge, we propose an innovative model named DGNMDA. Initially, we constructed various miRNA and disease similarity networks based on authoritative databases. Subsequently, we creatively design a dual heterogeneous graph neural network encoder capable of efficiently learning feature information between adjacent nodes and similarity information across the entire graph. Additionally, we develop a specialized fine-grained multi-layer feature interaction gating mechanism to integrate outputs from the neural network encoders to identify novel associations connecting miRNAs with diseases. We evaluate our model using 5-fold cross-validation and real-world disease case studies, based on the HMDD V3.2 dataset. Our method demonstrates superior performance compared to existing approaches in various tasks, confirming the effectiveness and potential of DGNMDA as a robust method for predicting miRNA-disease associations.