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		<title>Flawed Benchmarks Hid AI Success in Gene Perturbation Prediction</title>
		<link>https://bioengineer.org/flawed-benchmarks-hid-ai-success-in-gene-perturbation-prediction/</link>
		
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		<pubDate>Thu, 08 Oct 2026 21:18:28 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[benchmarking]]></category>
		<category><![CDATA[computational biology]]></category>
		<category><![CDATA[deep learning]]></category>
		<category><![CDATA[drug discovery]]></category>
		<category><![CDATA[dynamic range fraction]]></category>
		<category><![CDATA[foundation models]]></category>
		<category><![CDATA[genetic perturbation]]></category>
		<category><![CDATA[metric calibration]]></category>
		<category><![CDATA[Nature Biotechnology]]></category>
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		<category><![CDATA[single-cell RNA-seq]]></category>
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					<description><![CDATA[A new calibration framework shows that common benchmarking metrics masked real perturbation-specific signal, and that deep learning models can outperform simple baselines when evaluated properly.]]></description>
		
		
		
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