- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1471-2105
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Machine learning approach informs biology of cancer drug response ; volume:23 ; number:1 ; day:17 ; month:5 ; year:2022 ; pages:1-13 ; date:12.2022
BMC bioinformatics ; 23, Heft 1 (17.5.2022), 1-13, 12.2022
- Urheber
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1186/s12859-022-04720-z
- URN
-
urn:nbn:de:101:1-2022072309494514798740
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:26 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
- Zhu, Eliot Y.
- Dupuy, Adam J.
- SpringerLink (Online service)
Ähnliche Objekte (12)
Interpretable Machine Learning Approaches in Computational Biology
Machine learning empowered drug discovery
Machine learning based personalized drug response prediction for lung cancer patients
Biases Model Machine Learning Predictions in Protein Biology
Machine learning in drug discovery and drug design
Machine Learning for Kinase Drug Discovery
Artificial intelligence alphafold model for molecular biology and drug discovery: a machine-learning-driven informatics investigation
Ten quick tips for machine learning in computational biology
Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy
Machine learning and feature selection for drug response prediction in precision oncology applications
Evaluating molecular representations in machine learning models for drug response prediction and interpretability
Machine Learning‐Enabled Drug‐Induced Toxicity Prediction
Interpretable Machine Learning Approaches in Computational Biology
Machine learning empowered drug discovery
Machine learning based personalized drug response prediction for lung cancer patients
Biases Model Machine Learning Predictions in Protein Biology
Machine learning in drug discovery and drug design
Machine Learning for Kinase Drug Discovery
Artificial intelligence alphafold model for molecular biology and drug discovery: a machine-learning-driven informatics investigation
Ten quick tips for machine learning in computational biology
Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy
Machine learning and feature selection for drug response prediction in precision oncology applications
Evaluating molecular representations in machine learning models for drug response prediction and interpretability
Machine Learning‐Enabled Drug‐Induced Toxicity Prediction
Interpretable Machine Learning Approaches in Computational Biology
Machine learning empowered drug discovery
Machine learning based personalized drug response prediction for lung cancer patients
Biases Model Machine Learning Predictions in Protein Biology
Machine learning in drug discovery and drug design
Machine Learning for Kinase Drug Discovery
Artificial intelligence alphafold model for molecular biology and drug discovery: a machine-learning-driven informatics investigation
Ten quick tips for machine learning in computational biology
Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy
Machine learning and feature selection for drug response prediction in precision oncology applications
Evaluating molecular representations in machine learning models for drug response prediction and interpretability