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Leveraging machine learning essentiality predictions and chemogenomic interactions to identify antifungal targets
- 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 :
- Science
Subjects
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