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

Authors :
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
Source :
Nature Communications, Vol 12, Iss 1, Pp 1-18 (2021)
Publication Year :
2021
Publisher :
Nature Portfolio, 2021.

Abstract

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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
12
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.30b53e35a0bc440e930c93cb62d5cbf8
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-021-26850-3