ADRML: anticancer drug response prediction using manifold learning

Abstract One of the prominent challenges in precision medicine is to select the most appropriate treatment strategy for each patient based on the personalized information. The availability of massive data about drugs and cell lines facilitates the possibility of proposing efficient computational mod...

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Autores principales:	Fatemeh Ahmadi Moughari, Changiz Eslahchi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a0d5555dffc8446b9e2b7f8c9c55b7fd
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spelling	oai:doaj.org-article:a0d5555dffc8446b9e2b7f8c9c55b7fd2021-12-02T19:02:37ZADRML: anticancer drug response prediction using manifold learning10.1038/s41598-020-71257-72045-2322https://doaj.org/article/a0d5555dffc8446b9e2b7f8c9c55b7fd2020-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-71257-7https://doaj.org/toc/2045-2322Abstract One of the prominent challenges in precision medicine is to select the most appropriate treatment strategy for each patient based on the personalized information. The availability of massive data about drugs and cell lines facilitates the possibility of proposing efficient computational models for predicting anticancer drug response. In this study, we propose ADRML, a model for Anticancer Drug Response Prediction using Manifold Learning to systematically integrate the cell line information with the drug information to make accurate predictions about drug therapeutic. The proposed model maps the drug response matrix into the lower-rank spaces that lead to obtaining new perspectives about cell lines and drugs. The drug response for a new cell line-drug pair is computed using the low-rank features. The evaluation of ADRML performance on various types of cell lines and drug information, in addition to the comparisons with previously proposed methods, shows that ADRML provides accurate and robust predictions. Further investigations about the association between drug response and pathway activity scores reveal that the predicted drug responses can shed light on the underlying drug mechanism. Also, the case studies suggest that the predictions of ADRML about novel cell line-drug pairs are validated by reliable pieces of evidence from the literature. Consequently, the evaluations verify that ADRML can be used in accurately predicting and imputing the anticancer drug response.Fatemeh Ahmadi MoughariChangiz EslahchiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-18 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Fatemeh Ahmadi Moughari Changiz Eslahchi ADRML: anticancer drug response prediction using manifold learning
description	Abstract One of the prominent challenges in precision medicine is to select the most appropriate treatment strategy for each patient based on the personalized information. The availability of massive data about drugs and cell lines facilitates the possibility of proposing efficient computational models for predicting anticancer drug response. In this study, we propose ADRML, a model for Anticancer Drug Response Prediction using Manifold Learning to systematically integrate the cell line information with the drug information to make accurate predictions about drug therapeutic. The proposed model maps the drug response matrix into the lower-rank spaces that lead to obtaining new perspectives about cell lines and drugs. The drug response for a new cell line-drug pair is computed using the low-rank features. The evaluation of ADRML performance on various types of cell lines and drug information, in addition to the comparisons with previously proposed methods, shows that ADRML provides accurate and robust predictions. Further investigations about the association between drug response and pathway activity scores reveal that the predicted drug responses can shed light on the underlying drug mechanism. Also, the case studies suggest that the predictions of ADRML about novel cell line-drug pairs are validated by reliable pieces of evidence from the literature. Consequently, the evaluations verify that ADRML can be used in accurately predicting and imputing the anticancer drug response.
format	article
author	Fatemeh Ahmadi Moughari Changiz Eslahchi
author_facet	Fatemeh Ahmadi Moughari Changiz Eslahchi
author_sort	Fatemeh Ahmadi Moughari
title	ADRML: anticancer drug response prediction using manifold learning
title_short	ADRML: anticancer drug response prediction using manifold learning
title_full	ADRML: anticancer drug response prediction using manifold learning
title_fullStr	ADRML: anticancer drug response prediction using manifold learning
title_full_unstemmed	ADRML: anticancer drug response prediction using manifold learning
title_sort	adrml: anticancer drug response prediction using manifold learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/a0d5555dffc8446b9e2b7f8c9c55b7fd
work_keys_str_mv	AT fatemehahmadimoughari adrmlanticancerdrugresponsepredictionusingmanifoldlearning AT changizeslahchi adrmlanticancerdrugresponsepredictionusingmanifoldlearning
_version_	1718377226161881088

ADRML: anticancer drug response prediction using manifold learning

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