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	<title>machine learning in crop genetics &#8211; BIOENGINEER.ORG</title>
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		<title>ML Uncovers Transposable Elements Shaping Sorghum Traits</title>
		<link>https://bioengineer.org/ml-uncovers-transposable-elements-shaping-sorghum-traits/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Wed, 10 Sep 2025 15:56:10 +0000</pubDate>
				<category><![CDATA[Agriculture]]></category>
		<category><![CDATA[agronomic trait analysis]]></category>
		<category><![CDATA[machine learning in crop genetics]]></category>
		<category><![CDATA[phenolic compound regulation]]></category>
		<category><![CDATA[Sustainable agriculture biotechnology]]></category>
		<category><![CDATA[transposable elements in sorghum]]></category>
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					<description><![CDATA[Groundbreaking advancements in the realm of agronomy are on the horizon, heralded by recent research that utilizes cutting-edge machine learning techniques to uncover the intricate relationship between transposable elements and the phenotypic traits of mutagenized sorghum. This study, led by researchers including Ahn, Oh, and Botkin, delves deep into the genetic underpinnings that dictate the [&#8230;]]]></description>
		
		
		
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