A multi-task prediction framework, capable of simultaneously predicting drug-disease, drug-protein, and disease-protein associations, is proposed, named MTP-DDA, demonstrating its effectiveness and robustness.
Experimental results demonstrate that MVCL-IB consistently outperforms competing methods across multiple evaluation metrics, and suggest that MVCL-IB provides an effective framework for prioritizing potential metabolite-disease associations.
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
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.
IHLO-DTI, a novel prediction model based on an improved hypergraph neural network and Laplacian matrix optimization, can effectively capture high-order many-to-many interactions between drugs and targets, improving prediction accuracy and robustness.
Guolongwei Dai, Tao Luo, Dandan Li et al.· ACS Synthetic Biology· 0 citations
MRGBMDAT, a multi-relational graph encoder network with bilinear fusion for miRNA-disease association type prediction significantly outperforms five state-of-the-art methods across multiple evaluation metrics, exhibiting strong discriminative power and generalization capability.
Y. Sun, Wenjing Su, Siqi Zhu et al.· Bioinformatics· 0 citations
RGCNMDA is a leakage-controlled multi-view framework that integrates global latent structure, local profiles and similarities, and pathway context that supports the robustness of leakage-controlled multi-view learning across standard and cold-start evaluation settings.
Chao Hou, Mohamed Kone, Yang Xiang et al.· Bioinformatics· 0 citations