Advanced deep learning techniques have been widely applied in disease diagnosis and prognosis with clinical omics, especially gene expression data.
Apr 12, 2022 · We propose a novel multi-level attention graph neural network (MLA-GNN) for disease diagnosis and prognosis.
Feb 14, 2022 · Results: To explore the gene modules and inter-gene relational information contained in the omics data, we propose a novel multi-level attention ...
Motivation Advanced deep learning techniques have been widely applied in disease diagnosis and prognosis with clinical omics, especially gene expression data.
Multi-level attention graph neural network based on co-expression ...
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Model performance on the diagnosis of the COVID-19 dataset, evaluated by accuracy, precision, recall and F1-score metrics.
Multi-level attention graph neural network based on co-expression ...
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Table 8. Open in new tab. GO enrichment analysis for the TOP40 important genes selected by the FGS mechanism in the MLA-GNN and SNN model (COVID-19 dataset) ...
Multi-Level Attention Graph Neural Network Based on Co-expression Gene Modules for Disease Diagnosis and Prognosis. Article. Full-text available. Feb 2022.
Jun 30, 2023 · Multi-level attention graph neural network based on co-expression gene modules for disease diagnosis and prognosis. Bioinformatics (Oxford ...
CGMega: explainable graph neural network framework with attention ...
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Jul 17, 2024 · We proposed a new framework, CGMega, for studying cancer gene modules based on graph attention and graph interpretation technologies (Fig. 1a).
Missing: diagnosis | Show results with:diagnosis
Multi-Level Attention Graph Neural Network for Clinically Interpretable ...
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Dec 18, 2020 · Overview of the proposed Multi-Level Attention Graph Neural Network (MLA-GNN). (a) Gene Co-expression Computation module performs weighted ...