DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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1 Online-Ressource.
- Language
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Englisch
- Bibliographic citation
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DCGAN-DTA: Predicting drug-target binding affinity with deep convolutional generative adversarial networks ; volume:25 ; number:1 ; day:9 ; month:5 ; year:2024 ; pages:1-16 ; date:12.2024
BMC genomics ; 25, Heft 1 (9.5.2024), 1-16, 12.2024
- Creator
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Kalemati, Mahmood
Zamani Emani, Mojtaba
Koohi, Somayyeh
- Contributor
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SpringerLink (Online service)
- DOI
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10.1186/s12864-024-10326-x
- URN
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urn:nbn:de:101:1-2407190828374.165182640940
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:44 AM CEST
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Associated
- Kalemati, Mahmood
- Zamani Emani, Mojtaba
- Koohi, Somayyeh
- SpringerLink (Online service)
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MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction
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GeneralizedDTA: combining pre-training and multi-task learning to predict drug-target binding affinity for unknown drug discovery
MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs
Explainable deep drug–target representations for binding affinity prediction
Invisible steganography via generative adversarial networks
Generative adversarial nets for social scientists
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Drug–target affinity prediction with extended graph learning-convolutional networks
DGDTA: dynamic graph attention network for predicting drug–target binding affinity
Sequence-based drug-target affinity prediction using weighted graph neural networks
Generative adversarial networks based skin lesion segmentation
MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction
Generative Adversarial Privacy
GeneralizedDTA: combining pre-training and multi-task learning to predict drug-target binding affinity for unknown drug discovery
MvGraphDTA: multi-view-based graph deep model for drug-target affinity prediction by introducing the graphs and line graphs
Explainable deep drug–target representations for binding affinity prediction
Invisible steganography via generative adversarial networks
Generative adversarial nets for social scientists
Generative adversarial networks : Project relevant overview
Drug–target affinity prediction with extended graph learning-convolutional networks
DGDTA: dynamic graph attention network for predicting drug–target binding affinity
Sequence-based drug-target affinity prediction using weighted graph neural networks