- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
2045-2322
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
DESTINI: A deep-learning approach to contact-driven protein structure prediction ; volume:9 ; number:1 ; day:5 ; month:3 ; year:2019 ; pages:1-13 ; date:12.2019
Scientific reports ; 9, Heft 1 (5.3.2019), 1-13, 12.2019
- Creator
-
Gao, Mu
Zhou, Hongyi
Skolnick, Jeffrey
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1038/s41598-019-40314-1
- URN
-
urn:nbn:de:101:1-2019032417192637136458
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 10:57 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Gao, Mu
- Zhou, Hongyi
- Skolnick, Jeffrey
- SpringerLink (Online service)
Other Objects (12)
DeepSA: a deep-learning driven predictor of compound synthesis accessibility
Livestream sales prediction based on an interpretable deep-learning model
Deep-learning continuous gravitational waves
Deep‐Learning Assisted Polarization Holograms
Structure-based, deep-learning models for protein-ligand binding affinity prediction
Sequence-based prediction of protein protein interaction using a deep-learning algorithm
Template-Driven Knowledge Distillation for Compact and Accurate Periocular Biometrics Deep-Learning Models
DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics
Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks
Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach
MULocDeep: A deep-learning framework for protein subcellular and suborganellar localization prediction with residue-level interpretation
Comparative analysis of radiomics and deep-learning algorithms for survival prediction in hepatocellular carcinoma
DeepSA: a deep-learning driven predictor of compound synthesis accessibility
Livestream sales prediction based on an interpretable deep-learning model
Deep-learning continuous gravitational waves
Deep‐Learning Assisted Polarization Holograms
Structure-based, deep-learning models for protein-ligand binding affinity prediction
Sequence-based prediction of protein protein interaction using a deep-learning algorithm
Template-Driven Knowledge Distillation for Compact and Accurate Periocular Biometrics Deep-Learning Models
DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics
Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks
Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach
MULocDeep: A deep-learning framework for protein subcellular and suborganellar localization prediction with residue-level interpretation
Comparative analysis of radiomics and deep-learning algorithms for survival prediction in hepatocellular carcinoma
DeepSA: a deep-learning driven predictor of compound synthesis accessibility
Livestream sales prediction based on an interpretable deep-learning model
Deep-learning continuous gravitational waves
Deep‐Learning Assisted Polarization Holograms
Structure-based, deep-learning models for protein-ligand binding affinity prediction
Sequence-based prediction of protein protein interaction using a deep-learning algorithm
Template-Driven Knowledge Distillation for Compact and Accurate Periocular Biometrics Deep-Learning Models
DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics
Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks
Dual-frequency spectral radar retrieval of snowfall microphysics: a physics-driven deep-learning approach
MULocDeep: A deep-learning framework for protein subcellular and suborganellar localization prediction with residue-level interpretation