Review



graph convolutional network  (Omics Data Automation)

 
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    Omics Data Automation graph convolutional network
    Schematic of network‐based graph embedding neural network framework. (A) Data preparation and integration. Gene interaction networks from multiple sources are merged into a single molecular graph, which is subsequently supplemented with transcriptome (or other omics) data along with information on known cancer driver genes. (B) Graph embedding for training. In this graph, nodes represent genes, edges represent gene interactions, node features correspond to multidimensional gene expression vectors and edge features encode gene interaction data from different sources as n ‐dimensional binary vectors ( n = 3, three sources in this plot). (C) Graph embedding‐based neural network framework for omics data integration. This framework is designed to enhance node embedding extraction within a graph for node classification tasks. The framework processes molecular interaction graphs and is trained on these graphs. The node embeddings from all subgraphs are used to generate features separately that can be coupled with a <t>convolutional</t> neural network or graph attention network model to generate the final gene‐ranked list, representing their likelihood (equivalent to ranking score) of being associated with cancer. CDGs, cancer driver genes.
    Graph Convolutional Network, supplied by Omics Data Automation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/graph convolutional network/product/Omics Data Automation
    Average 90 stars, based on 1 article reviews
    graph convolutional network - by Bioz Stars, 2026-04
    90/100 stars

    Images

    1) Product Images from "Enhancing Molecular Network‐Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities"

    Article Title: Enhancing Molecular Network‐Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities

    Journal: Journal of Cellular and Molecular Medicine

    doi: 10.1111/jcmm.70351

    Schematic of network‐based graph embedding neural network framework. (A) Data preparation and integration. Gene interaction networks from multiple sources are merged into a single molecular graph, which is subsequently supplemented with transcriptome (or other omics) data along with information on known cancer driver genes. (B) Graph embedding for training. In this graph, nodes represent genes, edges represent gene interactions, node features correspond to multidimensional gene expression vectors and edge features encode gene interaction data from different sources as n ‐dimensional binary vectors ( n = 3, three sources in this plot). (C) Graph embedding‐based neural network framework for omics data integration. This framework is designed to enhance node embedding extraction within a graph for node classification tasks. The framework processes molecular interaction graphs and is trained on these graphs. The node embeddings from all subgraphs are used to generate features separately that can be coupled with a convolutional neural network or graph attention network model to generate the final gene‐ranked list, representing their likelihood (equivalent to ranking score) of being associated with cancer. CDGs, cancer driver genes.
    Figure Legend Snippet: Schematic of network‐based graph embedding neural network framework. (A) Data preparation and integration. Gene interaction networks from multiple sources are merged into a single molecular graph, which is subsequently supplemented with transcriptome (or other omics) data along with information on known cancer driver genes. (B) Graph embedding for training. In this graph, nodes represent genes, edges represent gene interactions, node features correspond to multidimensional gene expression vectors and edge features encode gene interaction data from different sources as n ‐dimensional binary vectors ( n = 3, three sources in this plot). (C) Graph embedding‐based neural network framework for omics data integration. This framework is designed to enhance node embedding extraction within a graph for node classification tasks. The framework processes molecular interaction graphs and is trained on these graphs. The node embeddings from all subgraphs are used to generate features separately that can be coupled with a convolutional neural network or graph attention network model to generate the final gene‐ranked list, representing their likelihood (equivalent to ranking score) of being associated with cancer. CDGs, cancer driver genes.

    Techniques Used: Gene Expression, Extraction



    Similar Products

    graph neural network gnn crystal pattern graph graph convolutional attention operator a b s t r a c t  (Genovis Inc)
    99
    Genovis Inc graph neural network gnn crystal pattern graph graph convolutional attention operator a b s t r a c t
    Graph Neural Network Gnn Crystal Pattern Graph Graph Convolutional Attention Operator A B S T R A C T, supplied by Genovis Inc, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/graph neural network gnn crystal pattern graph graph convolutional attention operator a b s t r a c t/product/Genovis Inc
    Average 99 stars, based on 1 article reviews
    graph neural network gnn crystal pattern graph graph convolutional attention operator a b s t r a c t - by Bioz Stars, 2026-04
    99/100 stars
    		  Buy from Supplier
    graph convolutional neural networks  (IEEE Access)
    90
    IEEE Access graph convolutional neural networks
    Graph Convolutional Neural Networks, supplied by IEEE Access, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/graph convolutional neural networks/product/IEEE Access
    Average 90 stars, based on 1 article reviews
    graph convolutional neural networks - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier
    pathway-level graph convolutional network  (Kozhikode Corporation)
    90
    Kozhikode Corporation pathway-level graph convolutional network
    Pathway Level Graph Convolutional Network, supplied by Kozhikode Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/pathway-level graph convolutional network/product/Kozhikode Corporation
    Average 90 stars, based on 1 article reviews
    pathway-level graph convolutional network - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier
    hybrid relational graph convolutional network  (MedLink Corporation)
    90
    MedLink Corporation hybrid relational graph convolutional network
    Hybrid Relational Graph Convolutional Network, supplied by MedLink Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/hybrid relational graph convolutional network/product/MedLink Corporation
    Average 90 stars, based on 1 article reviews
    hybrid relational graph convolutional network - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier
    hybrid relational graph convolutional network (rgcn)  (MedLink Corporation)
    90
    MedLink Corporation hybrid relational graph convolutional network (rgcn)
    Hybrid Relational Graph Convolutional Network (Rgcn), supplied by MedLink Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/hybrid relational graph convolutional network (rgcn)/product/MedLink Corporation
    Average 90 stars, based on 1 article reviews
    hybrid relational graph convolutional network (rgcn) - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier
    mutual infomax graph convolution network  (InfoMax Inc)
    90
    InfoMax Inc mutual infomax graph convolution network
    Mutual Infomax Graph Convolution Network, supplied by InfoMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/mutual infomax graph convolution network/product/InfoMax Inc
    Average 90 stars, based on 1 article reviews
    mutual infomax graph convolution network - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier
    graph convolutional network  (Omics Data Automation)
    90
    Omics Data Automation graph convolutional network
    Schematic of network‐based graph embedding neural network framework. (A) Data preparation and integration. Gene interaction networks from multiple sources are merged into a single molecular graph, which is subsequently supplemented with transcriptome (or other omics) data along with information on known cancer driver genes. (B) Graph embedding for training. In this graph, nodes represent genes, edges represent gene interactions, node features correspond to multidimensional gene expression vectors and edge features encode gene interaction data from different sources as n ‐dimensional binary vectors ( n = 3, three sources in this plot). (C) Graph embedding‐based neural network framework for omics data integration. This framework is designed to enhance node embedding extraction within a graph for node classification tasks. The framework processes molecular interaction graphs and is trained on these graphs. The node embeddings from all subgraphs are used to generate features separately that can be coupled with a <t>convolutional</t> neural network or graph attention network model to generate the final gene‐ranked list, representing their likelihood (equivalent to ranking score) of being associated with cancer. CDGs, cancer driver genes.
    Graph Convolutional Network, supplied by Omics Data Automation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/graph convolutional network/product/Omics Data Automation
    Average 90 stars, based on 1 article reviews
    graph convolutional network - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier
    hierarchical graph convolutional network with infomax-guided graph embedding  (InfoMax Inc)
    90
    InfoMax Inc hierarchical graph convolutional network with infomax-guided graph embedding
    Schematic of network‐based graph embedding neural network framework. (A) Data preparation and integration. Gene interaction networks from multiple sources are merged into a single molecular graph, which is subsequently supplemented with transcriptome (or other omics) data along with information on known cancer driver genes. (B) Graph embedding for training. In this graph, nodes represent genes, edges represent gene interactions, node features correspond to multidimensional gene expression vectors and edge features encode gene interaction data from different sources as n ‐dimensional binary vectors ( n = 3, three sources in this plot). (C) Graph embedding‐based neural network framework for omics data integration. This framework is designed to enhance node embedding extraction within a graph for node classification tasks. The framework processes molecular interaction graphs and is trained on these graphs. The node embeddings from all subgraphs are used to generate features separately that can be coupled with a <t>convolutional</t> neural network or graph attention network model to generate the final gene‐ranked list, representing their likelihood (equivalent to ranking score) of being associated with cancer. CDGs, cancer driver genes.
    Hierarchical Graph Convolutional Network With Infomax Guided Graph Embedding, supplied by InfoMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/hierarchical graph convolutional network with infomax-guided graph embedding/product/InfoMax Inc
    Average 90 stars, based on 1 article reviews
    hierarchical graph convolutional network with infomax-guided graph embedding - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier
    graph convolutional residual neural networks  (MultiTarget Pharmaceuticals)
    90
    MultiTarget Pharmaceuticals graph convolutional residual neural networks
    Schematic of network‐based graph embedding neural network framework. (A) Data preparation and integration. Gene interaction networks from multiple sources are merged into a single molecular graph, which is subsequently supplemented with transcriptome (or other omics) data along with information on known cancer driver genes. (B) Graph embedding for training. In this graph, nodes represent genes, edges represent gene interactions, node features correspond to multidimensional gene expression vectors and edge features encode gene interaction data from different sources as n ‐dimensional binary vectors ( n = 3, three sources in this plot). (C) Graph embedding‐based neural network framework for omics data integration. This framework is designed to enhance node embedding extraction within a graph for node classification tasks. The framework processes molecular interaction graphs and is trained on these graphs. The node embeddings from all subgraphs are used to generate features separately that can be coupled with a <t>convolutional</t> neural network or graph attention network model to generate the final gene‐ranked list, representing their likelihood (equivalent to ranking score) of being associated with cancer. CDGs, cancer driver genes.
    Graph Convolutional Residual Neural Networks, supplied by MultiTarget Pharmaceuticals, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/graph convolutional residual neural networks/product/MultiTarget Pharmaceuticals
    Average 90 stars, based on 1 article reviews
    graph convolutional residual neural networks - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier
    equivariant graph convolutional neural networks  (Sandia National Laboratories)
    90
    Sandia National Laboratories equivariant graph convolutional neural networks
    Schematic of network‐based graph embedding neural network framework. (A) Data preparation and integration. Gene interaction networks from multiple sources are merged into a single molecular graph, which is subsequently supplemented with transcriptome (or other omics) data along with information on known cancer driver genes. (B) Graph embedding for training. In this graph, nodes represent genes, edges represent gene interactions, node features correspond to multidimensional gene expression vectors and edge features encode gene interaction data from different sources as n ‐dimensional binary vectors ( n = 3, three sources in this plot). (C) Graph embedding‐based neural network framework for omics data integration. This framework is designed to enhance node embedding extraction within a graph for node classification tasks. The framework processes molecular interaction graphs and is trained on these graphs. The node embeddings from all subgraphs are used to generate features separately that can be coupled with a <t>convolutional</t> neural network or graph attention network model to generate the final gene‐ranked list, representing their likelihood (equivalent to ranking score) of being associated with cancer. CDGs, cancer driver genes.
    Equivariant Graph Convolutional Neural Networks, supplied by Sandia National Laboratories, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/equivariant graph convolutional neural networks/product/Sandia National Laboratories
    Average 90 stars, based on 1 article reviews
    equivariant graph convolutional neural networks - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Schematic of network‐based graph embedding neural network framework. (A) Data preparation and integration. Gene interaction networks from multiple sources are merged into a single molecular graph, which is subsequently supplemented with transcriptome (or other omics) data along with information on known cancer driver genes. (B) Graph embedding for training. In this graph, nodes represent genes, edges represent gene interactions, node features correspond to multidimensional gene expression vectors and edge features encode gene interaction data from different sources as n ‐dimensional binary vectors ( n = 3, three sources in this plot). (C) Graph embedding‐based neural network framework for omics data integration. This framework is designed to enhance node embedding extraction within a graph for node classification tasks. The framework processes molecular interaction graphs and is trained on these graphs. The node embeddings from all subgraphs are used to generate features separately that can be coupled with a convolutional neural network or graph attention network model to generate the final gene‐ranked list, representing their likelihood (equivalent to ranking score) of being associated with cancer. CDGs, cancer driver genes.

    Journal: Journal of Cellular and Molecular Medicine

    Article Title: Enhancing Molecular Network‐Based Cancer Driver Gene Prediction Using Machine Learning Approaches: Current Challenges and Opportunities

    doi: 10.1111/jcmm.70351

    Figure Lengend Snippet: Schematic of network‐based graph embedding neural network framework. (A) Data preparation and integration. Gene interaction networks from multiple sources are merged into a single molecular graph, which is subsequently supplemented with transcriptome (or other omics) data along with information on known cancer driver genes. (B) Graph embedding for training. In this graph, nodes represent genes, edges represent gene interactions, node features correspond to multidimensional gene expression vectors and edge features encode gene interaction data from different sources as n ‐dimensional binary vectors ( n = 3, three sources in this plot). (C) Graph embedding‐based neural network framework for omics data integration. This framework is designed to enhance node embedding extraction within a graph for node classification tasks. The framework processes molecular interaction graphs and is trained on these graphs. The node embeddings from all subgraphs are used to generate features separately that can be coupled with a convolutional neural network or graph attention network model to generate the final gene‐ranked list, representing their likelihood (equivalent to ranking score) of being associated with cancer. CDGs, cancer driver genes.

    Article Snippet: Graph convolutional network , Omics data and gene or protein interaction network , The topology of graphs, including the direct and indirect relationships between nodes (genes), can be learned. , EMOGI [ ], MTGCN [ ], DGMP [ ], MNGCL [ ], NIGCNDriver [ ], CGMega [ ] .

    Techniques: Gene Expression, Extraction