IDEAL (Interpretability-Driven Evolvable Attentive Learning for Microbe-Drug Association) is proposed, a multi-view framework that integrates drug network topological attributes, BERT-encoded drug semantics, drug fingerprints, microbe genome sequence attributes, BERT-encoded microbe semantics, and microbe metabolic pathway attributes.
Abstract
Predicting associations between human microbes and drugs (MDA) is a critical step in drug development and precision medicine. Although various computational approaches have been proposed, many existing models still struggle to reveal the key features and interaction mechanisms that drive their predictions, and their decision processes remain difficult to interpret and validate biologically. To address these limitations, we propose IDEAL (Interpretability-Driven Evolvable Attentive Learning for Microbe-Drug Association), a multi-view framework that integrates drug network topological attributes, BERT-encoded drug semantics, drug fingerprints, microbe genome sequence attributes, BERT-encoded microbe semantics, and microbe metabolic pathway attributes. We further introduce GNNExplainer into the graph convolutional network (GCN) pipeline to identify influential drug-microbe relations and feed the resulting structural signal back into graph refinement. In addition, we employ Elastic Weight Consolidation (EWC) to support parameter-efficient adaptation when biological data grow over time while mitigating catastrophic forgetting. Extensive experiments on three public datasets demonstrate that the proposed method achieves strong predictive performance, offers interpretable structural explanations, and provides a practical transfer-learning route for evolving MDA benchmarks. The source code for our model is publicly available at https://github.com/Luoxidu02/IDEAL.
SGTL-DDA is proposed, a novel graph transformer framework designed to incorporate structural information and domain-specific knowledge from heterogeneous biological information networks (HBINs) that successfully identifies both known therapeutics and novel repositioning candidates, supported by molecular docking results and literature evidence.
Bowei Zhao, Hui Zhao, Yu-an Huang et al.· IEEE transactions on computa...· 0 citations
The human microbiota is essential for maintaining physiological homeostasis, and microbial dysbiosis is increasingly implicated in the pathogenesis of complex diseases. Identifying potential microbe-disease associations (MDAs) can therefore facilitate mechanistic investigation, biomarker discovery, and therapeutic development. However, wet-laboratory validation is costly and time-consuming, while existing computational methods often struggle with sparse association networks and complex nonlinear interactions. We propose a novel deep learning approach named the Dual Dynamic Hypergraph Convolution Framework for Human Microbe-Disease Association Prediction (DDHMDA). Specifically, DDHMDA first utilizes graph convolutional networks to encode local topological features. Subsequently, it dynamically constructs a dual hypergraph architecture: a differentiable K-means similarity hypergraph to capture intra-modal global clustering patterns, and an attention-based cross-modal interaction hypergraph to model inter-modal interactions synergistically. Under leakage-free pair-level five-fold cross-validation (denoted as CV3), DDHMDA achieved AUC/AUPR values of 0.9789 ± 0.0177/0.9843 ± 0.0129 on HMDAD and 0.9651 ± 0.0042/0.9740 ± 0.0031 on Disbiome. DDHMDA also obtained the best overall CV3 performance among the eight evaluated methods. Furthermore, ablation experiments and case studies validate the practical effectiveness of individual modules and the biological interpretability in discovering novel MDAs. Therefore, DDHMDA would be a reliable tool for identifying potential MDAs.
The proposed ZSCAN-DDIE framework enhances prediction accuracy for unseen DDIE categories but also provides biologically meaningful insights into molecular interaction mechanisms, offering a robust and clinically relevant solution for pharmacovigilance and drug safety assessment.
Shaoxi Gao, Zhanpeng Gan, Fangfang Han et al.· Bioinformatics Advances· 0 citations
GCN-XGB is a novel hybrid computational framework that integrates a two-layer Graph Convolutional Network with Extreme Gradient Boosting to improve the accuracy of miRNA-disease association prediction and is suggested to be a powerful and reliable tool for identifying potential disease-related miRNAs and prioritizing candidates for experimental validation.
Jie Zhou, Peishen Yan, Jia Qu et al.· Journal of Mechanics in Medi...· 0 citations
DDI-MMAF is proposed, a lightweight cross-modal framework that avoids using explicit high-dimensional omics profiles as direct model inputs and enables accurate synergy prediction from raw minimalist inputs, offering a practical and efficient computational solution for cost-effective drug combination discovery.
Hao Li, Qianhui Jiang, Jiahui Guan et al.· European journal of medicina...· 0 citations
MuSeL is proposed, a multi-scale adaptive graph representation learning framework that jointly mitigates the above issues from two complementary perspectives: global topology modeling and local structure optimization.
Yuehu Wu, Lei Wang, Zhengwei Li et al.· Proceedings of the 32nd ACM...· 0 citations
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