MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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1 Online-Ressource.
- Sprache
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Englisch
- Erschienen in
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MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction ; volume:25 ; number:1 ; day:23 ; month:8 ; year:2024 ; pages:1-18 ; date:12.2024
BMC bioinformatics ; 25, Heft 1 (23.8.2024), 1-18, 12.2024
- Urheber
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Zhang, Beiyi
Niu, Dongjiang
Zhang, Lianwei
Zhang, Qiang
Li, Zhen
- Beteiligte Personen und Organisationen
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SpringerLink (Online service)
- DOI
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10.1186/s12859-024-05904-5
- URN
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urn:nbn:de:101:1-2411120921236.543690355833
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:30 MESZ
Datenpartner
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Beteiligte
- Zhang, Beiyi
- Niu, Dongjiang
- Zhang, Lianwei
- Zhang, Qiang
- Li, Zhen
- SpringerLink (Online service)
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Graph Embedding, Content Understanding, and Self-Supervised Learning
Novel drug-target interactions via link prediction and network embedding
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GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data
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DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques
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CCL-DTI: contributing the contrastive loss in drug–target interaction prediction
Graph Embedding, Content Understanding, and Self-Supervised Learning
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A comparison of embedding aggregation strategies in drug–target interaction prediction
DTi2Vec: Drug–target interaction prediction using network embedding and ensemble learning
GSRF-DTI: a framework for drug-target interaction prediction based on a drug-target pair network and representation learning on a large graph
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