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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