Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs

Artificial intelligence and machine learning promise to transform cancer therapies by accurately predicting the most appropriate drugs to treat individual patients. Here, the authors present an approach which uses omics data to produce ordered lists of drugs based on their effectiveness in decreasin...

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Autores principales:	Henry Gerdes, Pedro Casado, Arran Dokal, Maruan Hijazi, Nosheen Akhtar, Ruth Osuntola, Vinothini Rajeeve, Jude Fitzgibbon, Jon Travers, David Britton, Shirin Khorsandi, Pedro R. Cutillas
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/dd9e23dc4fd245f6935c304010512356
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spelling	oai:doaj.org-article:dd9e23dc4fd245f6935c3040105123562021-12-02T14:02:51ZDrug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs10.1038/s41467-021-22170-82041-1723https://doaj.org/article/dd9e23dc4fd245f6935c3040105123562021-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22170-8https://doaj.org/toc/2041-1723Artificial intelligence and machine learning promise to transform cancer therapies by accurately predicting the most appropriate drugs to treat individual patients. Here, the authors present an approach which uses omics data to produce ordered lists of drugs based on their effectiveness in decreasing cancer cell proliferation.Henry GerdesPedro CasadoArran DokalMaruan HijaziNosheen AkhtarRuth OsuntolaVinothini RajeeveJude FitzgibbonJon TraversDavid BrittonShirin KhorsandiPedro R. CutillasNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Henry Gerdes Pedro Casado Arran Dokal Maruan Hijazi Nosheen Akhtar Ruth Osuntola Vinothini Rajeeve Jude Fitzgibbon Jon Travers David Britton Shirin Khorsandi Pedro R. Cutillas Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs
description	Artificial intelligence and machine learning promise to transform cancer therapies by accurately predicting the most appropriate drugs to treat individual patients. Here, the authors present an approach which uses omics data to produce ordered lists of drugs based on their effectiveness in decreasing cancer cell proliferation.
format	article
author	Henry Gerdes Pedro Casado Arran Dokal Maruan Hijazi Nosheen Akhtar Ruth Osuntola Vinothini Rajeeve Jude Fitzgibbon Jon Travers David Britton Shirin Khorsandi Pedro R. Cutillas
author_facet	Henry Gerdes Pedro Casado Arran Dokal Maruan Hijazi Nosheen Akhtar Ruth Osuntola Vinothini Rajeeve Jude Fitzgibbon Jon Travers David Britton Shirin Khorsandi Pedro R. Cutillas
author_sort	Henry Gerdes
title	Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs
title_short	Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs
title_full	Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs
title_fullStr	Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs
title_full_unstemmed	Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs
title_sort	drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/dd9e23dc4fd245f6935c304010512356
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Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs

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