- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1471-2105
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Learning protein binding affinity using privileged information ; volume:19 ; number:1 ; day:15 ; month:11 ; year:2018 ; pages:1-12 ; date:12.2018
BMC bioinformatics ; 19, Heft 1 (15.11.2018), 1-12, 12.2018
- Klassifikation
-
Biowissenschaften, Biologie
- Urheber
-
Abbasi, Wajid Arshad
Asif, Amina
Ben-Hur, Asa
Minhas, Fayyaz ul Amir Afsar
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1186/s12859-018-2448-z
- URN
-
urn:nbn:de:101:1-2018121519315148569150
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:50 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
- Abbasi, Wajid Arshad
- Asif, Amina
- Ben-Hur, Asa
- Minhas, Fayyaz ul Amir Afsar
- SpringerLink (Online service)
Ähnliche Objekte (12)
Learning protein-ligand binding affinity with atomic environment vectors
PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery
Structure-based, deep-learning models for protein-ligand binding affinity prediction
Learning a peptide-protein binding affinity predictor with kernel ridge regression
Learning about particles : 50 privileged years
Design of a high-affinity carbohydrate binding protein
Ensembling methods for protein-ligand binding affinity prediction
Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning
MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning
Accurate and sensitive quantification of protein-DNA binding affinity
Falling water ice affinity purification of ice-binding proteins
High-affinity RNA binding by a hyperthermophilic single-stranded DNA-binding protein
Learning protein-ligand binding affinity with atomic environment vectors
PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery
Structure-based, deep-learning models for protein-ligand binding affinity prediction
Learning a peptide-protein binding affinity predictor with kernel ridge regression
Learning about particles : 50 privileged years
Design of a high-affinity carbohydrate binding protein
Ensembling methods for protein-ligand binding affinity prediction
Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning
MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning
Accurate and sensitive quantification of protein-DNA binding affinity
Falling water ice affinity purification of ice-binding proteins
High-affinity RNA binding by a hyperthermophilic single-stranded DNA-binding protein
Learning protein-ligand binding affinity with atomic environment vectors
PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery
Structure-based, deep-learning models for protein-ligand binding affinity prediction
Learning a peptide-protein binding affinity predictor with kernel ridge regression
Learning about particles : 50 privileged years
Design of a high-affinity carbohydrate binding protein
Ensembling methods for protein-ligand binding affinity prediction
Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning
MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning
Accurate and sensitive quantification of protein-DNA binding affinity
Falling water ice affinity purification of ice-binding proteins