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		<title>New dataset and protein language models sharpen T cell receptor binding prediction</title>
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				<category><![CDATA[Technology]]></category>
		<category><![CDATA[benchmark dataset]]></category>
		<category><![CDATA[binding prediction]]></category>
		<category><![CDATA[Hi-TPH]]></category>
		<category><![CDATA[immunoinformatics]]></category>
		<category><![CDATA[learning]]></category>
		<category><![CDATA[machine]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Nature Computational Science]]></category>
		<category><![CDATA[Personalized Immunotherapy]]></category>
		<category><![CDATA[pHLA binding]]></category>
		<category><![CDATA[protein language models]]></category>
		<category><![CDATA[T cell receptor]]></category>
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					<description><![CDATA[A new hierarchical dataset and a family of fine-tuned protein language models improve T cell receptor–pHLA binding prediction, achieving a 19.2% performance gain over state-of-the-art tools on an external wet-lab test set.]]></description>
		
		
		
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