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: ADRML: anticancer drug response prediction using manifold learning ; volume:10 ; number:1 ; day:28 ; month:8 ; year:2020 ; pages:1-18 ; date:12.2020
Scientific reports ; 10, Heft 1 (28.8.2020), 1-18, 12.2020

Creator: Ahmadi Moughari, Fatemeh
Eslahchi, Changiz

Contributor: SpringerLink (Online service)

DOI: 10.1038/s41598-020-71257-7

URN: urn:nbn:de:101:1-2021012219280533926455

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

Last update: 15.08.2025, 7:37 AM CEST

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

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

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

piscesCSM: prediction of anticancer synergistic drug combinations

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

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New anticancer drugs

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DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics

Empagliflozin: a potential anticancer drug

CDK7 inhibitors as anticancer drugs

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

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

piscesCSM: prediction of anticancer synergistic drug combinations

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

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

New anticancer drugs

Role of solute carriers in response to anticancer drugs

DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics

Empagliflozin: a potential anticancer drug

CDK7 inhibitors as anticancer drugs

Computational modeling of methionine cycle-based metabolism and DNA methylation and the implications for anti-cancer drug response prediction

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ADRML: anticancer drug response prediction using manifold learning

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Other Objects (12)

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

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

piscesCSM: prediction of anticancer synergistic drug combinations

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

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

New anticancer drugs

Role of solute carriers in response to anticancer drugs

DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics

Empagliflozin: a potential anticancer drug

CDK7 inhibitors as anticancer drugs

Computational modeling of methionine cycle-based metabolism and DNA methylation and the implications for anti-cancer drug response prediction

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

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

piscesCSM: prediction of anticancer synergistic drug combinations

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

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

New anticancer drugs

Role of solute carriers in response to anticancer drugs

DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics

Empagliflozin: a potential anticancer drug

CDK7 inhibitors as anticancer drugs

Computational modeling of methionine cycle-based metabolism and DNA methylation and the implications for anti-cancer drug response prediction

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

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

piscesCSM: prediction of anticancer synergistic drug combinations

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

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

New anticancer drugs

Role of solute carriers in response to anticancer drugs

DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics

Empagliflozin: a potential anticancer drug

CDK7 inhibitors as anticancer drugs

Computational modeling of methionine cycle-based metabolism and DNA methylation and the implications for anti-cancer drug response prediction

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