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	<title>Machine learning in biology &#8211; BIOENGINEER.ORG</title>
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		<title>Transformers Revolutionize Peptide Turnover Prediction in Proteomics</title>
		<link>https://bioengineer.org/transformers-revolutionize-peptide-turnover-prediction-in-proteomics/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Sat, 24 Jan 2026 21:27:47 +0000</pubDate>
				<category><![CDATA[Biology]]></category>
		<category><![CDATA[Computational proteomics]]></category>
		<category><![CDATA[Large-scale proteomics]]></category>
		<category><![CDATA[large-scale time-series proteomics]]></category>
		<category><![CDATA[Machine learning in biology]]></category>
		<category><![CDATA[machine learning in proteomics]]></category>
		<category><![CDATA[peptide turnover prediction]]></category>
		<category><![CDATA[personalized medicine applications]]></category>
		<category><![CDATA[Transformer architectures]]></category>
		<category><![CDATA[Transformer models]]></category>
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					<description><![CDATA[In the revolutionary field of proteomics, understanding the dynamics of peptide turnover has emerged as a frontline challenge for researchers. A recent study by prominent scientists K. Ishino, A.C. Yoshizawa, and Y. Liu, et al., titled “Peptide turnover prediction using transformer architectures on large-scale time-series proteomic data,” has taken significant strides towards addressing this issue. [&#8230;]]]></description>
		
		
		
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