Author Correction: ADRML: anticancer drug response prediction using manifold learning

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 2045-2322

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Author Correction: ADRML: anticancer drug response prediction using manifold learning ; volume:10 ; number:1 ; day:15 ; month:12 ; year:2020 ; pages:1 ; date:12.2020
Scientific reports ; 10, Heft 1 (15.12.2020), 1, 12.2020

Classification: Allgemeines, Wissenschaft

Creator: Moughari, Fatemeh Ahmadi
Eslahchi, Changiz

Contributor: SpringerLink (Online service)

DOI: 10.1038/s41598-020-77486-0

URN: urn:nbn:de:101:1-2021012219275845996274

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:29 AM CEST

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Moughari, Fatemeh Ahmadi
Eslahchi, Changiz
SpringerLink (Online service)

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piscesCSM: prediction of anticancer synergistic drug combinations

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Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization

Neurological adverse reactions to anticancer drugs

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Author Correction: ADRML: anticancer drug response prediction using manifold learning

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Other Objects (12)

ADRML: anticancer drug response prediction using manifold learning

piscesCSM: prediction of anticancer synergistic drug combinations

MOKPE: drug–target interaction prediction via manifold optimization based kernel preserving embedding

New anticancer drugs

Ensemble transfer learning for the prediction of anti-cancer drug response

Comprehensive anticancer drug response prediction based on a simple cell line-drug complex network model

Empagliflozin: a potential anticancer drug

CDK7 inhibitors as anticancer drugs

Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning

Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization

Neurological adverse reactions to anticancer drugs

Anticancer Drug Discovery: Role of Pharmacogenomics

ADRML: anticancer drug response prediction using manifold learning

piscesCSM: prediction of anticancer synergistic drug combinations

MOKPE: drug–target interaction prediction via manifold optimization based kernel preserving embedding

New anticancer drugs

Ensemble transfer learning for the prediction of anti-cancer drug response

Comprehensive anticancer drug response prediction based on a simple cell line-drug complex network model

Empagliflozin: a potential anticancer drug

CDK7 inhibitors as anticancer drugs

Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning

Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization

Neurological adverse reactions to anticancer drugs

Anticancer Drug Discovery: Role of Pharmacogenomics

ADRML: anticancer drug response prediction using manifold learning

piscesCSM: prediction of anticancer synergistic drug combinations

MOKPE: drug–target interaction prediction via manifold optimization based kernel preserving embedding

New anticancer drugs

Ensemble transfer learning for the prediction of anti-cancer drug response

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Empagliflozin: a potential anticancer drug

CDK7 inhibitors as anticancer drugs

Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning

Improved anticancer drug response prediction in cell lines using matrix factorization with similarity regularization

Neurological adverse reactions to anticancer drugs

Anticancer Drug Discovery: Role of Pharmacogenomics

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