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A machine learning approach to genome-wide association mapping of disease resistance and geographic origin in sorghum.

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Title: A machine learning approach to genome-wide association mapping of disease resistance and geographic origin in sorghum.
Authors: Ahn E; United States Department of Agriculture, Sustainable Perennial Crops Laboratory, Agricultural Research Service, United States, Beltsville, MD, 20705, USA. Ezekiel.ahn@usda.gov., Baek I; United States Department of Agriculture, Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States, Beltsville, MD, 20705, USA., Park S; United States Department of Agriculture, Sustainable Perennial Crops Laboratory, Agricultural Research Service, United States, Beltsville, MD, 20705, USA., Prom LK; United States Department of Agriculture, Insect Control and Cotton Disease Research, Agricultural Research Service, Southern Plains Agricultural Research Center, United States, College Station, TX, 77845, USA., Lim S; United States Department of Agriculture, Sustainable Perennial Crops Laboratory, Agricultural Research Service, United States, Beltsville, MD, 20705, USA., Jang JH; United States Department of Agriculture, Sustainable Perennial Crops Laboratory, Agricultural Research Service, United States, Beltsville, MD, 20705, USA., Hong SM; United States Department of Agriculture, Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States, Beltsville, MD, 20705, USA.; Department of Civil Urban Earth and Environmental Engineering, Ulsan National Institute of Science and Technology, UNIST-gil 50, Ulsan, 44919, Republic of Korea., Kim MS; United States Department of Agriculture, Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States, Beltsville, MD, 20705, USA., Meinhardt LW; United States Department of Agriculture, Sustainable Perennial Crops Laboratory, Agricultural Research Service, United States, Beltsville, MD, 20705, USA., Magill C; Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, 77843, USA. c-magill@tamu.edu.
Source: BMC plant biology [BMC Plant Biol] 2026 Feb 28; Vol. 26 (1). Date of Electronic Publication: 2026 Feb 28.
Publication Type: Journal Article
Journal Info: Publisher: BioMed Central Country of Publication: England NLM ID: 100967807 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2229 (Electronic) Linking ISSN: 14712229 NLM ISO Abbreviation: BMC Plant Biol Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: A machine learning approach to genome-wide association mapping of disease resistance and geographic origin in sorghum.
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  Data: <searchLink fieldCode="AU" term="%22Ahn+E%22">Ahn E</searchLink>; United States Department of Agriculture, Sustainable Perennial Crops Laboratory, Agricultural Research Service, United States, Beltsville, MD, 20705, USA. Ezekiel.ahn@usda.gov.<br /><searchLink fieldCode="AU" term="%22Baek+I%22">Baek I</searchLink>; United States Department of Agriculture, Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States, Beltsville, MD, 20705, USA.<br /><searchLink fieldCode="AU" term="%22Park+S%22">Park S</searchLink>; United States Department of Agriculture, Sustainable Perennial Crops Laboratory, Agricultural Research Service, United States, Beltsville, MD, 20705, USA.<br /><searchLink fieldCode="AU" term="%22Prom+LK%22">Prom LK</searchLink>; United States Department of Agriculture, Insect Control and Cotton Disease Research, Agricultural Research Service, Southern Plains Agricultural Research Center, United States, College Station, TX, 77845, USA.<br /><searchLink fieldCode="AU" term="%22Lim+S%22">Lim S</searchLink>; United States Department of Agriculture, Sustainable Perennial Crops Laboratory, Agricultural Research Service, United States, Beltsville, MD, 20705, USA.<br /><searchLink fieldCode="AU" term="%22Jang+JH%22">Jang JH</searchLink>; United States Department of Agriculture, Sustainable Perennial Crops Laboratory, Agricultural Research Service, United States, Beltsville, MD, 20705, USA.<br /><searchLink fieldCode="AU" term="%22Hong+SM%22">Hong SM</searchLink>; United States Department of Agriculture, Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States, Beltsville, MD, 20705, USA.; Department of Civil Urban Earth and Environmental Engineering, Ulsan National Institute of Science and Technology, UNIST-gil 50, Ulsan, 44919, Republic of Korea.<br /><searchLink fieldCode="AU" term="%22Kim+MS%22">Kim MS</searchLink>; United States Department of Agriculture, Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, United States, Beltsville, MD, 20705, USA.<br /><searchLink fieldCode="AU" term="%22Meinhardt+LW%22">Meinhardt LW</searchLink>; United States Department of Agriculture, Sustainable Perennial Crops Laboratory, Agricultural Research Service, United States, Beltsville, MD, 20705, USA.<br /><searchLink fieldCode="AU" term="%22Magill+C%22">Magill C</searchLink>; Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, 77843, USA. c-magill@tamu.edu.
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  Data: <searchLink fieldCode="JN" term="%22100967807%22">BMC plant biology</searchLink> [BMC Plant Biol] 2026 Feb 28; Vol. 26 (1). <i>Date of Electronic Publication: </i>2026 Feb 28.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22BioMed+Central%22">BioMed Central </searchLink><i>Country of Publication: </i>England <i>NLM ID: </i>100967807 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1471-2229 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2214712229%22">14712229 </searchLink><i>NLM ISO Abbreviation: </i>BMC Plant Biol <i>Subsets: </i>MEDLINE
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              Text: 2026 Feb 28
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