Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets

The analysis of essential genes in pathogens can be used to discover potential antimicrobial targets. Here, the authors use a machine learning model and chemogenomic analyses to generate genome-wide gene essentiality predictions for the fungal pathogen Candida albicans, define the function of three...

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Autores principales:	Ci Fu, Xiang Zhang, Amanda O. Veri, Kali R. Iyer, Emma Lash, Alice Xue, Huijuan Yan, Nicole M. Revie, Cassandra Wong, Zhen-Yuan Lin, Elizabeth J. Polvi, Sean D. Liston, Benjamin VanderSluis, Jing Hou, Yoko Yashiroda, Anne-Claude Gingras, Charles Boone, Teresa R. O’Meara, Matthew J. O’Meara, Suzanne Noble, Nicole Robbins, Chad L. Myers, Leah E. Cowen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/30b53e35a0bc440e930c93cb62d5cbf8
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

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spelling	oai:doaj.org-article:30b53e35a0bc440e930c93cb62d5cbf82021-11-14T12:35:00ZLeveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets10.1038/s41467-021-26850-32041-1723https://doaj.org/article/30b53e35a0bc440e930c93cb62d5cbf82021-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-26850-3https://doaj.org/toc/2041-1723The analysis of essential genes in pathogens can be used to discover potential antimicrobial targets. Here, the authors use a machine learning model and chemogenomic analyses to generate genome-wide gene essentiality predictions for the fungal pathogen Candida albicans, define the function of three uncharacterized essential genes, and identify the target of a new antifungal compound.Ci FuXiang ZhangAmanda O. VeriKali R. IyerEmma LashAlice XueHuijuan YanNicole M. RevieCassandra WongZhen-Yuan LinElizabeth J. PolviSean D. ListonBenjamin VanderSluisJing HouYoko YashirodaAnne-Claude GingrasCharles BooneTeresa R. O’MearaMatthew J. O’MearaSuzanne NobleNicole RobbinsChad L. MyersLeah E. CowenNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-18 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Ci Fu Xiang Zhang Amanda O. Veri Kali R. Iyer Emma Lash Alice Xue Huijuan Yan Nicole M. Revie Cassandra Wong Zhen-Yuan Lin Elizabeth J. Polvi Sean D. Liston Benjamin VanderSluis Jing Hou Yoko Yashiroda Anne-Claude Gingras Charles Boone Teresa R. O’Meara Matthew J. O’Meara Suzanne Noble Nicole Robbins Chad L. Myers Leah E. Cowen Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets
description	The analysis of essential genes in pathogens can be used to discover potential antimicrobial targets. Here, the authors use a machine learning model and chemogenomic analyses to generate genome-wide gene essentiality predictions for the fungal pathogen Candida albicans, define the function of three uncharacterized essential genes, and identify the target of a new antifungal compound.
format	article
author	Ci Fu Xiang Zhang Amanda O. Veri Kali R. Iyer Emma Lash Alice Xue Huijuan Yan Nicole M. Revie Cassandra Wong Zhen-Yuan Lin Elizabeth J. Polvi Sean D. Liston Benjamin VanderSluis Jing Hou Yoko Yashiroda Anne-Claude Gingras Charles Boone Teresa R. O’Meara Matthew J. O’Meara Suzanne Noble Nicole Robbins Chad L. Myers Leah E. Cowen
author_facet	Ci Fu Xiang Zhang Amanda O. Veri Kali R. Iyer Emma Lash Alice Xue Huijuan Yan Nicole M. Revie Cassandra Wong Zhen-Yuan Lin Elizabeth J. Polvi Sean D. Liston Benjamin VanderSluis Jing Hou Yoko Yashiroda Anne-Claude Gingras Charles Boone Teresa R. O’Meara Matthew J. O’Meara Suzanne Noble Nicole Robbins Chad L. Myers Leah E. Cowen
author_sort	Ci Fu
title	Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets
title_short	Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets
title_full	Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets
title_fullStr	Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets
title_full_unstemmed	Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets
title_sort	leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/30b53e35a0bc440e930c93cb62d5cbf8
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Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets

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