Machine learning identifies candidates for drug repurposing in Alzheimer’s disease

Clinical trials of novel therapeutics for Alzheimer’s Disease (AD) have provided largely negative results, so far. Here, the authors present a machine learning framework that quantifies potential associations between the pathology of AD severity and gene-based molecular mechanisms to enable drug rep...

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Autores principales:	Steve Rodriguez, Clemens Hug, Petar Todorov, Nienke Moret, Sarah A. Boswell, Kyle Evans, George Zhou, Nathan T. Johnson, Bradley T. Hyman, Peter K. Sorger, Mark W. Albers, Artem Sokolov
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/93e763bd3055417ea9c559a7b6fa24d5
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spelling	oai:doaj.org-article:93e763bd3055417ea9c559a7b6fa24d52021-12-02T14:03:50ZMachine learning identifies candidates for drug repurposing in Alzheimer’s disease10.1038/s41467-021-21330-02041-1723https://doaj.org/article/93e763bd3055417ea9c559a7b6fa24d52021-02-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-21330-0https://doaj.org/toc/2041-1723Clinical trials of novel therapeutics for Alzheimer’s Disease (AD) have provided largely negative results, so far. Here, the authors present a machine learning framework that quantifies potential associations between the pathology of AD severity and gene-based molecular mechanisms to enable drug repurposing.Steve RodriguezClemens HugPetar TodorovNienke MoretSarah A. BoswellKyle EvansGeorge ZhouNathan T. JohnsonBradley T. HymanPeter K. SorgerMark W. AlbersArtem SokolovNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Steve Rodriguez Clemens Hug Petar Todorov Nienke Moret Sarah A. Boswell Kyle Evans George Zhou Nathan T. Johnson Bradley T. Hyman Peter K. Sorger Mark W. Albers Artem Sokolov Machine learning identifies candidates for drug repurposing in Alzheimer’s disease
description	Clinical trials of novel therapeutics for Alzheimer’s Disease (AD) have provided largely negative results, so far. Here, the authors present a machine learning framework that quantifies potential associations between the pathology of AD severity and gene-based molecular mechanisms to enable drug repurposing.
format	article
author	Steve Rodriguez Clemens Hug Petar Todorov Nienke Moret Sarah A. Boswell Kyle Evans George Zhou Nathan T. Johnson Bradley T. Hyman Peter K. Sorger Mark W. Albers Artem Sokolov
author_facet	Steve Rodriguez Clemens Hug Petar Todorov Nienke Moret Sarah A. Boswell Kyle Evans George Zhou Nathan T. Johnson Bradley T. Hyman Peter K. Sorger Mark W. Albers Artem Sokolov
author_sort	Steve Rodriguez
title	Machine learning identifies candidates for drug repurposing in Alzheimer’s disease
title_short	Machine learning identifies candidates for drug repurposing in Alzheimer’s disease
title_full	Machine learning identifies candidates for drug repurposing in Alzheimer’s disease
title_fullStr	Machine learning identifies candidates for drug repurposing in Alzheimer’s disease
title_full_unstemmed	Machine learning identifies candidates for drug repurposing in Alzheimer’s disease
title_sort	machine learning identifies candidates for drug repurposing in alzheimer’s disease
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/93e763bd3055417ea9c559a7b6fa24d5
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Machine learning identifies candidates for drug repurposing in Alzheimer’s disease

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