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Machine learning-based identification of general transcriptional predictors for plant disease.

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Title: Machine learning-based identification of general transcriptional predictors for plant disease.
Authors: Sia J; Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA., Zhang W; Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA.; Institute for Integrative Genome Biology, University of California, Riverside, CA, 92521, USA., Cheng M; Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA., Bogdan P; Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA.; Center for Complex Particle Systems (COMPASS), University of Southern California, Los Angeles, USA., Cook DE; Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA.
Source: The New phytologist [New Phytol] 2025 Jan; Vol. 245 (2), pp. 785-806. Date of Electronic Publication: 2024 Nov 21.
Publication Type: Journal Article; Research Support, N.I.H., Extramural
Journal Info: Publisher: Wiley on behalf of New Phytologist Trust Country of Publication: England NLM ID: 9882884 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1469-8137 (Electronic) Linking ISSN: 0028646X NLM ISO Abbreviation: New Phytol Subsets: MEDLINE
Database: MEDLINE Ultimate
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PubType: Academic Journal
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  Data: <searchLink fieldCode="AU" term="%22Sia+J%22">Sia J</searchLink>; Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA.<br /><searchLink fieldCode="AU" term="%22Zhang+W%22">Zhang W</searchLink>; Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA.; Institute for Integrative Genome Biology, University of California, Riverside, CA, 92521, USA.<br /><searchLink fieldCode="AU" term="%22Cheng+M%22">Cheng M</searchLink>; Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA.<br /><searchLink fieldCode="AU" term="%22Bogdan+P%22">Bogdan P</searchLink>; Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, 90089, USA.; Center for Complex Particle Systems (COMPASS), University of Southern California, Los Angeles, USA.<br /><searchLink fieldCode="AU" term="%22Cook+DE%22">Cook DE</searchLink>; Department of Plant Pathology, Kansas State University, Manhattan, KS, 66506, USA.
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  Data: <searchLink fieldCode="JN" term="%229882884%22">The New phytologist</searchLink> [New Phytol] 2025 Jan; Vol. 245 (2), pp. 785-806. <i>Date of Electronic Publication: </i>2024 Nov 21.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22Wiley+on+behalf+of+New+Phytologist+Trust%22">Wiley on behalf of New Phytologist Trust </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>9882884 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1469-8137 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%220028646X%22">0028646X </searchLink><i>NLM ISO Abbreviation: </i>New Phytol <i>Subsets: </i>MEDLINE
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      – Type: doi
        Value: 10.1111/nph.20264
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      – Code: eng
        Text: English
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              Text: 2025 Jan
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