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	<title>Yorumlanabilir yapay zeka &#8211; BIOENGINEER.ORG</title>
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		<title>Accurate Gene and Cell Type Prediction from Spatial Data</title>
		<link>https://bioengineer.org/accurate-gene-and-cell-type-prediction-from-spatial-data/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Fri, 16 Jan 2026 18:01:45 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[Cell Type Identification]]></category>
		<category><![CDATA[computational biology]]></category>
		<category><![CDATA[Gen belirteçleri]]></category>
		<category><![CDATA[Gene marker prediction]]></category>
		<category><![CDATA[Hesaplamalı biyoloji** **Kısa açıklama:** 1. **Uzamsal transkriptomik:** Makalenin temel]]></category>
		<category><![CDATA[Hücre tipi tanımlama]]></category>
		<category><![CDATA[interpretable machine learning]]></category>
		<category><![CDATA[İşte 5 uygun etiket (virgülle ayrılmış): **Uzamsal transkriptomik]]></category>
		<category><![CDATA[Spatial transcriptomics]]></category>
		<category><![CDATA[Yorumlanabilir yapay zeka]]></category>
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					<description><![CDATA[In a groundbreaking advance that promises to reshape our understanding of cellular diversity within complex tissues, researchers have unveiled a novel computational framework designed to analyze spatial transcriptomics data with unprecedented robustness and interpretability. This pioneering method enables scientists to predict gene markers and identify cell types directly from spatially resolved gene expression profiles, thereby [&#8230;]]]></description>
		
		
		
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