Machine learning identifies novel signatures of antifungal drug resistance in Saccharomycotina yeasts.
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| Title: | Machine learning identifies novel signatures of antifungal drug resistance in Saccharomycotina yeasts. |
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| Authors: | Harrison MC; Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, Tennessee, United States of America., Rinker DC; Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, Tennessee, United States of America., LaBella AL; Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, Tennessee, United States of America.; Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Kannapolis, North Carolina, United States of America.; Center for Computational Intelligence to Predict Health and Environmental Risks (CIPHER), University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America., Opulente DA; Laboratory of Genetics, DOE Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.; Department of Biology, Villanova University, Villanova, Pennsylvania, United States of America., Wolters JF; Laboratory of Genetics, DOE Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, Wisconsin, United States of America., Zhou X; Guangdong Province Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Center, South China Agricultural University, Guangzhou, China., Shen XX; Zhejiang Key Laboratory of Biology and Ecological Regulation of Crop Pathogens and Insects, Institute of Insect Sciences, Zhejiang University, Hangzhou, China., Groenewald M; Westerdijk Fungal Biodiversity Institute, Utrecht, The Netherlands., Hittinger CT; Laboratory of Genetics, DOE Great Lakes Bioenergy Research Center, Center for Genomic Science Innovation, J. F. Crow Institute for the Study of Evolution, Wisconsin Energy Institute, University of Wisconsin-Madison, Madison, Wisconsin, United States of America., Rokas A; Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, Tennessee, United States of America. |
| Source: | PLoS genetics [PLoS Genet] 2026 Mar 17; Vol. 22 (3), pp. e1012091. Date of Electronic Publication: 2026 Mar 17 (Print Publication: 2026). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101239074 Publication Model: eCollection Cited Medium: Internet ISSN: 1553-7404 (Electronic) Linking ISSN: 15537390 NLM ISO Abbreviation: PLoS Genet Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1553-7404 |
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| DOI: | 10.1371/journal.pgen.1012091 |
