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	<title>long-tail learning &#8211; BIOENGINEER.ORG</title>
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		<title>AI Model Tackles Rare Cancer Subtypes by Learning From Imbalanced Molecular Data</title>
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		<pubDate>Mon, 05 Oct 2026 08:55:00 +0000</pubDate>
				<category><![CDATA[Biology]]></category>
		<category><![CDATA[Bioinformatics]]></category>
		<category><![CDATA[cancer subtype classification]]></category>
		<category><![CDATA[class imbalance]]></category>
		<category><![CDATA[copy number alteration]]></category>
		<category><![CDATA[cross-attention]]></category>
		<category><![CDATA[DNA Methylation]]></category>
		<category><![CDATA[graph neural network]]></category>
		<category><![CDATA[long-tail learning]]></category>
		<category><![CDATA[mRNA expression]]></category>
		<category><![CDATA[Multi-omics integration]]></category>
		<category><![CDATA[precision oncology]]></category>
		<category><![CDATA[TCGA]]></category>
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					<description><![CDATA[Researchers have developed CALT-GNN, a graph neural network that combines cross-attention-based multi-omics integration with long-tail expert routing to improve cancer subtype classification on imbalanced TCGA datasets.]]></description>
		
		
		
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