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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.
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
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  Data: Machine learning identifies novel signatures of antifungal drug resistance in Saccharomycotina yeasts.
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  Data: <searchLink fieldCode="AU" term="%22Harrison+MC%22">Harrison MC</searchLink>; Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA.<br /><searchLink fieldCode="AU" term="%22Rinker+DC%22">Rinker DC</searchLink>; Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA.<br /><searchLink fieldCode="AU" term="%22LaBella+AL%22">LaBella AL</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Opulente+DA%22">Opulente DA</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Wolters+JF%22">Wolters JF</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Zhou+X%22">Zhou X</searchLink>; Guangdong Province Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Center, South China Agricultural University, Guangzhou 510642, China.<br /><searchLink fieldCode="AU" term="%22Shen+XX%22">Shen XX</searchLink>; Zhejiang Key Laboratory of Biology and Ecological Regulation of Crop Pathogens and Insects, Institute of Insect Sciences, Zhejiang University, Hangzhou 310058, China.<br /><searchLink fieldCode="AU" term="%22Groenewald+M%22">Groenewald M</searchLink>; Westerdijk Fungal Biodiversity Institute, Utrecht 3584, The Netherlands.<br /><searchLink fieldCode="AU" term="%22Hittinger+CT%22">Hittinger CT</searchLink>; 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.<br /><searchLink fieldCode="AU" term="%22Rokas+A%22">Rokas A</searchLink>; Department of Biological Sciences and Evolutionary Studies Initiative, Vanderbilt University, Nashville, TN 37235, USA.
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  Data: <searchLink fieldCode="JN" term="%22101680187%22">BioRxiv : the preprint server for biology</searchLink> [bioRxiv] 2025 May 10. <i>Date of Electronic Publication: </i>2025 May 10.
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              Text: 2025 May 10
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