AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
Case studies further validated predicted circRNA-drug associations, including cisplatin, enzalutamide, and sorafenib, against published experimental findings, demonstrating HMCDSP's capacity to reveal clinically relevant biomarkers and inform personalized therapeutic strategies.
BioHSNet, a biological function-guided hypergraph siamese network for inferring drug-target interactions from perturbation transcriptomics, utilizes hyperedge representations of functionally grouped gene expression to capture higher-order functional relationships, and integrates compound structural information into the...
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.
Results indicate that FragSyn, through the synergistic design of fragment-level representation and cellular context awareness, provides an effective and interpretable new approach to synergistic drug combination prediction.
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.
A novel GNN framework, APKAGN, designed for predicting miRNA-disease associations significantly enhances the accuracy of MDAs prediction, offering a powerful tool for investigating disease mechanisms and identifying biomarkers.
Channel-gated global heterogeneous propagation is introduced, in which global virtual nodes first enable graph-level context exchange and a channel-wise propagation gate then recalibrates propagated embeddings to emphasize discriminative feature channels and suppress noisy or redundant ones.
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