Aug 2026· Nan fang yi ke da xue xue bao = Journal of Southern Medical University· Vol 46 8, pp.
1947-1955
· 0 citations
Medicine
TL;DR
The KGNN model can effectively predict drug-target interactions to facilitate drug repurposing and the design of multi-target drugs while reducing the screening space and experimental validation costs.
Paired Wilcoxon tests in 39 AURORA-US patients with matched primary and metastatic samples confirm that 4 of the 10 IG-identified TFs are significantly altered during metastatic progression after Bonferroni correction.
C. Fernandez-Lozano, David Ferreiro, Patricia V.-del-Río· bioRxiv· 0 citations
An innovative dual-branch approach based on Graph Isomorphism Network drug representations coupled with a Multilayer Perceptron (MLP) for 50-dimensional ssGSEA pathway activities calculated from CCLE gene expression is proposed, proving the importance of biological features in the two-branch model.
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, Dan-Dan Li et al.· ACS Synthetic Biology· 0 citations
Experimental results show that CMAF-DDI improves multi-class DDI prediction compared with representative graph-based and multi-source fusion baselines and ablation, hyperparameter sensitivity, controlled protein perturbation, representation, and case-level analyses to examine the contribution and behavior of protein-en...
Heng-Peng Zhao, Xiaoli Lin, Jun Pang et al.· IEEE journal of biomedical a...· 1 citation
An AI-driven multi-omics model using GDSC, CCLE, DepMap, CTRP, and TCGA data, integrating mutation, copy-number variation, RNA expression, DNA methylation, drug SMILES, and IC50/AUC responses is developed, demonstrating that multi-omics fusion and drug-structure modeling improve prediction stability and support interpr...
Xin-Yuan Kang, Ai-Lin Deng· Applied and Computational En...· 0 citations
KGDDA is proposed, a multimodal framework designed for drug-disease association prediction that synergistically integrates KGs with medical ontologies, enabling the adaptive capture of intricate drug-disease interactions.
Qi-Chang Zhao, Qiao Ling, Muhammad Habibulla Alamin et al.· IEEE Transactions on Computa...· 0 citations
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