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10.1093/bioinformatics/btac088 http://scihub22266oqcxt.onion/10.1093/bioinformatics/btac088 35157021!ä!35157021 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Bioinformatics 2022 ; 38 (8): 2178-2186 Nephropedia Template TP {{tp|p=35157021|t=2022. Multi-level attention graph neural network based on co-expression gene modules for disease diagnosis and prognosis |pdf=|usr=}}{{35157021}} gab.com Text Multi-level attention graph neural network based on co-expression gene modules for disease diagnosis and prognosis JO: Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=35157021 #FOAvip MOTIVATION: Advanced deep learning techniques have been widely applied in disease diagnosis and prognosis with clinical omics, especially gene expression data. In the regulation of biological processes and disease progression, genes often work interactively rather than individually. Therefore, investigating gene association information and co-functional gene modules can facilitate disease state prediction. RESULTS: To explore the gene modules and inter-gene relational information contained in the omics data, we propose a novel multi-level attention graph neural network (MLA-GNN) for disease diagnosis and prognosis. Specifically, we format omics data into co-expression graphs via weighted correlation network analysis, and then construct multi-level graph features, finally fuse them through a well-designed multi-level graph feature fully fusion module to conduct predictions. For model interpretation, a novel full-gradient graph saliency mechanism is developed to identify the disease-relevant genes. MLA-GNN achieves state-of-the-art performance on transcriptomic data from TCGA-LGG/TCGA-GBM and proteomic data from coronavirus disease 2019 (COVID-19)/non-COVID-19 patient sera. More importantly, the relevant genes selected by our model are interpretable and are consistent with the clinical understanding. AVAILABILITYAND IMPLEMENTATION: The codes are available at https://github.com/TencentAILabHealthcare/MLA-GNN. Twit Text FOAVip Multi-level attention graph neural network based on co-expression gene modules for disease diagnosis and prognosis ????Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=35157021 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35157021 #FOAvip English Wikipedia <ref>{{Cite pmid|35157021}}</ref> Multi-level attention graph neural network based on co-expression gene modules for disease diagnosis and prognosis #MMPMID35157021 Xing X; Yang F; Li H; Zhang J; Zhao Y; Gao M; Huang J; Yao J Bioinformatics 2022[Apr]; 38 (8): 2178-2186 PMID35157021show ga MOTIVATION: Advanced deep learning techniques have been widely applied in disease diagnosis and prognosis with clinical omics, especially gene expression data. In the regulation of biological processes and disease progression, genes often work interactively rather than individually. Therefore, investigating gene association information and co-functional gene modules can facilitate disease state prediction. RESULTS: To explore the gene modules and inter-gene relational information contained in the omics data, we propose a novel multi-level attention graph neural network (MLA-GNN) for disease diagnosis and prognosis. Specifically, we format omics data into co-expression graphs via weighted correlation network analysis, and then construct multi-level graph features, finally fuse them through a well-designed multi-level graph feature fully fusion module to conduct predictions. For model interpretation, a novel full-gradient graph saliency mechanism is developed to identify the disease-relevant genes. MLA-GNN achieves state-of-the-art performance on transcriptomic data from TCGA-LGG/TCGA-GBM and proteomic data from coronavirus disease 2019 (COVID-19)/non-COVID-19 patient sera. More importantly, the relevant genes selected by our model are interpretable and are consistent with the clinical understanding. AVAILABILITYAND IMPLEMENTATION: The codes are available at https://github.com/TencentAILabHealthcare/MLA-GNN. |*COVID-19[MESH] |*Gene Regulatory Networks[MESH] |COVID-19 Testing[MESH] |Gene Expression Profiling[MESH] |Humans[MESH] |Neural Networks, Computer[MESH]Multi-level attention graph neural network based on co-expression gene(epmc).pdf DeepDyve Pubget Overpricing