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	<title>Machine Learning in Drug Discovery &#8211; BIOENGINEER.ORG</title>
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		<title>Transforming Drug Response Predictions with Dual-Branch Model</title>
		<link>https://bioengineer.org/transforming-drug-response-predictions-with-dual-branch-model/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Mon, 26 Jan 2026 20:59:44 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[AI in biology]]></category>
		<category><![CDATA[Based on the research content]]></category>
		<category><![CDATA[Cellular Perturbation Modeling]]></category>
		<category><![CDATA[Drug Response Prediction]]></category>
		<category><![CDATA[Dual-Branch Transformer]]></category>
		<category><![CDATA[here are 5 appropriate tags: **dual-branch transformer]]></category>
		<category><![CDATA[Machine Learning in Drug Discovery]]></category>
		<category><![CDATA[Precision Medicine]]></category>
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					<description><![CDATA[In an age where the intersection of artificial intelligence and biology is becoming increasingly pivotal, a groundbreaking study published in Nature Machine Intelligence has illuminated a novel approach to understanding drug-induced cellular responses. The research, conducted by an accomplished team including Guo, Zhang, and Hu, proposes a dual-branch transformer model that could revolutionize the way [&#8230;]]]></description>
		
		
		
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