A Bayesian machine learning approach for drug target identification using diverse data types

Drug target identification is a crucial step in drug development. Here, the authors introduce a Bayesian machine learning framework that integrates multiple data types to predict the targets of small molecules, enabling identification of a new set of microtubule inhibitors and the target of the anti...

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Autores principales:	Neel S. Madhukar, Prashant K. Khade, Linda Huang, Kaitlyn Gayvert, Giuseppe Galletti, Martin Stogniew, Joshua E. Allen, Paraskevi Giannakakou, Olivier Elemento
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/fd2f68d27a0449ed8826ad49e3a65d1c
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spelling	oai:doaj.org-article:fd2f68d27a0449ed8826ad49e3a65d1c2021-12-02T17:32:08ZA Bayesian machine learning approach for drug target identification using diverse data types10.1038/s41467-019-12928-62041-1723https://doaj.org/article/fd2f68d27a0449ed8826ad49e3a65d1c2019-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-12928-6https://doaj.org/toc/2041-1723Drug target identification is a crucial step in drug development. Here, the authors introduce a Bayesian machine learning framework that integrates multiple data types to predict the targets of small molecules, enabling identification of a new set of microtubule inhibitors and the target of the anti-cancer molecule ONC201.Neel S. MadhukarPrashant K. KhadeLinda HuangKaitlyn GayvertGiuseppe GallettiMartin StogniewJoshua E. AllenParaskevi GiannakakouOlivier ElementoNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-14 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Neel S. Madhukar Prashant K. Khade Linda Huang Kaitlyn Gayvert Giuseppe Galletti Martin Stogniew Joshua E. Allen Paraskevi Giannakakou Olivier Elemento A Bayesian machine learning approach for drug target identification using diverse data types
description	Drug target identification is a crucial step in drug development. Here, the authors introduce a Bayesian machine learning framework that integrates multiple data types to predict the targets of small molecules, enabling identification of a new set of microtubule inhibitors and the target of the anti-cancer molecule ONC201.
format	article
author	Neel S. Madhukar Prashant K. Khade Linda Huang Kaitlyn Gayvert Giuseppe Galletti Martin Stogniew Joshua E. Allen Paraskevi Giannakakou Olivier Elemento
author_facet	Neel S. Madhukar Prashant K. Khade Linda Huang Kaitlyn Gayvert Giuseppe Galletti Martin Stogniew Joshua E. Allen Paraskevi Giannakakou Olivier Elemento
author_sort	Neel S. Madhukar
title	A Bayesian machine learning approach for drug target identification using diverse data types
title_short	A Bayesian machine learning approach for drug target identification using diverse data types
title_full	A Bayesian machine learning approach for drug target identification using diverse data types
title_fullStr	A Bayesian machine learning approach for drug target identification using diverse data types
title_full_unstemmed	A Bayesian machine learning approach for drug target identification using diverse data types
title_sort	bayesian machine learning approach for drug target identification using diverse data types
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/fd2f68d27a0449ed8826ad49e3a65d1c
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A Bayesian machine learning approach for drug target identification using diverse data types

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