| Autor: |
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 |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Nature Communications, Vol 12, Iss 1, Pp 1-18 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2041-1723 |
| DOI: |
10.1038/s41467-021-26850-3 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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Full text from SpringerLink