MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs ; volume:22 ; number:1 ; day:26 ; month:8 ; year:2024 ; pages:1-13 ; date:12.2024
BMC biology ; 22, Heft 1 (26.8.2024), 1-13, 12.2024

Creator: Zeng, Xin
Zhong, Kai-Yang
Meng, Pei-Yan
Li, Shu-Juan
Lv, Shuang-Qing
Wen, Meng-Liang
Li, Yi

Contributor: SpringerLink (Online service)

DOI: 10.1186/s12915-024-01981-3

URN: urn:nbn:de:101:1-2411112102254.808399052281

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:22 AM CEST

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Zeng, Xin
Zhong, Kai-Yang
Meng, Pei-Yan
Li, Shu-Juan
Lv, Shuang-Qing
Wen, Meng-Liang
Li, Yi
SpringerLink (Online service)

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Drug–target affinity prediction with extended graph learning-convolutional networks

Introducing hat graphs

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

Sequence-based drug-target affinity prediction using weighted graph neural networks

GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Graph Markup Language (GraphML)

Graph clustering-based discretization of splitting and merging methods (GraphS and GraphM)

Advancing drug–target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining

zweidimensionales bewegtes Bild

Multiplex graph analysis with GraphBLAS

zweidimensionales bewegtes Bild

Symmetric Cubic Graphs as Cayley Graphs

Drug–target affinity prediction with extended graph learning-convolutional networks

Introducing hat graphs

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

Sequence-based drug-target affinity prediction using weighted graph neural networks

GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Graph Markup Language (GraphML)

Graph clustering-based discretization of splitting and merging methods (GraphS and GraphM)

Advancing drug–target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining

zweidimensionales bewegtes Bild

Multiplex graph analysis with GraphBLAS

zweidimensionales bewegtes Bild

Symmetric Cubic Graphs as Cayley Graphs

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MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs

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Other Objects (12)

Drug–target affinity prediction with extended graph learning-convolutional networks

Introducing hat graphs

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

Sequence-based drug-target affinity prediction using weighted graph neural networks

GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Graph Markup Language (GraphML)

Graph Markup Language (GraphML)

Graph clustering-based discretization of splitting and merging methods (GraphS and GraphM)

Advancing drug–target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining

Multiplex graph analysis with GraphBLAS

Symmetric Cubic Graphs as Cayley Graphs

Drug–target affinity prediction with extended graph learning-convolutional networks

Introducing hat graphs

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

Sequence-based drug-target affinity prediction using weighted graph neural networks

GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Graph Markup Language (GraphML)

Graph Markup Language (GraphML)

Graph clustering-based discretization of splitting and merging methods (GraphS and GraphM)

Advancing drug–target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining

Multiplex graph analysis with GraphBLAS

Symmetric Cubic Graphs as Cayley Graphs

Drug–target affinity prediction with extended graph learning-convolutional networks

Introducing hat graphs

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

Sequence-based drug-target affinity prediction using weighted graph neural networks

GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Graph Markup Language (GraphML)

Graph Markup Language (GraphML)

Graph clustering-based discretization of splitting and merging methods (GraphS and GraphM)

Advancing drug–target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining

Multiplex graph analysis with GraphBLAS

Symmetric Cubic Graphs as Cayley Graphs

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