Machine learning identifies novel signatures of antifungal drug resistance in Saccharomycotina yeasts.

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Bibliographic Details
Title: Machine learning identifies novel signatures of antifungal drug resistance in Saccharomycotina yeasts.
Authors: Harrison MC; Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA., Rinker DC; Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA., LaBella AL; Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA.; Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Kannapolis, NC 28081, USA & Center for Computational Intelligence to Predict Health and Environmental Risks (CIPHER), University of North Carolina at Charlotte, Charlotte, North Carolina, USA., 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, WI 53726, USA.; Department of Biology, Villanova University, Villanova, PA 19085, USA., 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, WI 53726, USA., Zhou X; Guangdong Province Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Center, South China Agricultural University, Guangzhou 510642, China., Shen XX; Zhejiang Key Laboratory of Biology and Ecological Regulation of Crop Pathogens and Insects, Institute of Insect Sciences, Zhejiang University, Hangzhou 310058, China., Groenewald M; Westerdijk Fungal Biodiversity Institute, Utrecht 3584, 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, WI 53726, USA., Rokas A; Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA.
Source: BioRxiv : the preprint server for biology [bioRxiv] 2025 May 10. Date of Electronic Publication: 2025 May 10.
Publication Type: Journal Article; Preprint
Journal Info: Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet ISSN: 2692-8205 (Electronic) Linking ISSN: 26928205 NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2692-8205
DOI:10.1101/2025.05.09.653161