Heterogeneous dual-channel and interpretable graph representation learning with global virtual nodes for microRNA-mediated drug sensitivity prediction.
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.
Experimental results show that MVGSCA effectively integrates heterogeneous biological information and achieves superior prediction performance, offering valuable insights into cancer resistance mechanisms and supporting drug discovery efforts.
Ru Nie, Ying Fu, Zheng-Wei Li et al.· IEEE transactions on computa...· 0 citations
STDCAMDA provides an effective computational framework for prioritizing candidate disease-related microbes and supporting downstream biomedical validation and introduces two strategies: a dynamically weighted fusion classifier that integrates dual-channel features and uses a multi-layer perceptron for prediction, and a...
Jiahao Li, Xiangmin Ji, Xiao-Wen Gao et al.· Journal of King Saud Univers...· 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
DC-MetaMG, a deep learning framework based on a causal disentanglement strategy that models association responses as the synergistic interplay between two complementary mechanisms: static binding and dynamic regulation, is proposed, demonstrating its suitability for training scenarios involving complex biological infor...
Runzhou Tang, Xi Zhou, Yujie Qi et al.· IEEE journal of biomedical a...· 0 citations
Experiments demonstrate that HSAF-DDI achieves superior overall performance compared to state-of-the-art methods, indicating the critical role of fine-grained biological features in improving the DDI prediction performance.
Xiaoli Lin, Si-Yuan Zhang, Bo Li et al.· Journal of Chemical Informat...· 0 citations
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.
Yong-Tian Wang, Wen-Kai Shen, Jiahao Li et al.· Interdisciplinary Sciences C...· 0 citations
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