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

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
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Science
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Acceso en línea:https://doaj.org/article/30b53e35a0bc440e930c93cb62d5cbf8
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Sumario: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.

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