M2BA-MDA: A Multi-Modal Multi-View Bidirectional Attention Network for Microbe-Disease Association Prediction

Numerous studies have shown that microbes play vital roles in human health and disease. Identifying microbe–disease associations can aid in disease diagnosis, treatment, and prevention. However, traditional biological experiments are time-consuming and costly. Although various computational methods have been developed, accurate and efficient approaches remain limited due to single-source data, insufficient prior knowledge, and suboptimal model performance. This paper proposed M²BA-MDA, a deep learning framework based on multi-modal, multi-view, and bidirectional attention mechanism for predicting potential microbe–disease associations. Firstly, microbe and disease features are extracted using multiple similarity measures and fused for consistency. Secondly, to mitigate gradient vanishing and over-smoothing issues in deep graph attention networks, we propose a stable enhanced graph attention network (EGAT) module incorporating cross-layer connections to extract microbial and disease features from each perspective. Moreover, to more effectively capture the complex interactions between microbes and diseases, we introduce an interaction module based on a bidirectional attention mechanism. This module enhances the mutual dependencies between the two entities and generates their final embeddings. Finally, a deep neural network (DNN) classifier is employed to predict potential associations. Extensive experiments conducted on the HMDAD and DisBiome datasets demonstrate that M²BA-MDA consistently outperforms five state-of-the-art methods. Parameter analysis and ablation studies further confirm the robustness and sensitivity of the model. In addition, case studies validate its effectiveness in identifying disease-associated microbes.