Review





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