A dual-view fusion framework using residual gated graph convolution and Mamba for metabolite-disease association prediction with explorations on salivary metabolites
Aug 2026· Journal of Computer-Aided Molecular Design· Vol 40· 0 citations· 51 references
Medicine
TL;DR
DFFRGM, a dual-view fusion framework that integrates residual gated graph convolution and Mamba-based multi-hop dependency modeling for metabolite-disease association prediction for metabolite-disease association prediction is proposed.
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.
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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...
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