MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
1 Online-Ressource.
- Sprache
-
Englisch
- Erschienen in
-
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
- Urheber
-
Zeng, Xin
Zhong, Kai-Yang
Meng, Pei-Yan
Li, Shu-Juan
Lv, Shuang-Qing
Wen, Meng-Liang
Li, Yi
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1186/s12915-024-01981-3
- URN
-
urn:nbn:de:101:1-2411112102254.808399052281
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:22 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Zeng, Xin
- Zhong, Kai-Yang
- Meng, Pei-Yan
- Li, Shu-Juan
- Lv, Shuang-Qing
- Wen, Meng-Liang
- Li, Yi
- SpringerLink (Online service)
Ähnliche Objekte (12)
Drug–target affinity prediction with extended graph learning-convolutional networks
Introducing hat graphs
Sequence-based drug-target affinity prediction using weighted graph neural networks
DGDTA: dynamic graph attention network for predicting drug–target binding affinity
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
Sequence-based drug-target affinity prediction using weighted graph neural networks
DGDTA: dynamic graph attention network for predicting drug–target binding affinity
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
Sequence-based drug-target affinity prediction using weighted graph neural networks
DGDTA: dynamic graph attention network for predicting drug–target binding affinity
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