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	<title>İlaç keşfi &#8211; BIOENGINEER.ORG</title>
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	<title>İlaç keşfi &#8211; BIOENGINEER.ORG</title>
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		<title>Boosting Link Prediction in Biomedical Knowledge Graphs</title>
		<link>https://bioengineer.org/boosting-link-prediction-in-biomedical-knowledge-graphs/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Tue, 20 Jan 2026 18:27:40 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[BioPathNet]]></category>
		<category><![CDATA[Biyomedikal bilgi grafikleri]]></category>
		<category><![CDATA[Deep Learning in Biomedicine]]></category>
		<category><![CDATA[Derin öğrenme]]></category>
		<category><![CDATA[Drug Discovery** **Açıklama:** 1. **Biomedical Knowledge Graphs:** Makalenin temel konusu ve verilerin temsil edildiği yapı. 2. **Link Prediction:** Makalenin çözmeye çalış]]></category>
		<category><![CDATA[İlaç keşfi]]></category>
		<category><![CDATA[İşte 5 uygun etiket (virgülle ayrılmış): **Biomedical Knowledge Graphs]]></category>
		<category><![CDATA[link prediction]]></category>
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					<description><![CDATA[In an exciting advance for the field of biomedical informatics, researchers have unveiled a groundbreaking method aimed at enhancing link prediction within biomedical knowledge graphs through a novel framework called BioPathNet. This innovative approach addresses a significant challenge in the field—making accurately informed predictions about potential relationships and interactions between biological entities, which can have [&#8230;]]]></description>
		
		
		
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		<title>Machine Learning Unveils PRMT5 Inhibitors’ Diversity and Stability</title>
		<link>https://bioengineer.org/machine-learning-unveils-prmt5-inhibitors-diversity-and-stability/</link>
		
		<dc:creator><![CDATA[Bioengineer]]></dc:creator>
		<pubDate>Fri, 16 Jan 2026 02:01:28 +0000</pubDate>
				<category><![CDATA[Health]]></category>
		<category><![CDATA[drug discovery]]></category>
		<category><![CDATA[Dynamic stability** **Açıklama:** 1. **PRMT5 inhibitors:** Makalenin temel araştırma konusu. 2. **Machine learning:** Çalışmada kullanılan ana metodolojik yaklaşım. 3]]></category>
		<category><![CDATA[İlaç keşfi]]></category>
		<category><![CDATA[İşte bu içerik için uygun 5 Türkçe etiket: **Makine öğrenmesi]]></category>
		<category><![CDATA[Kanser Tedavisi]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[Makalenin içeriğine ve anahtar kelimelerine göre en uygun 5 etiket: **PRMT5 inhibitors]]></category>
		<category><![CDATA[Molecular modeling]]></category>
		<category><![CDATA[Moleküler modelleme** **Açıklama:** 1. **Makine öğrenmesi:** Çalışmanın temel metodolojisini vurgular. 2. **İlaç ke]]></category>
		<category><![CDATA[PRMT5 inhibitörleri]]></category>
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					<description><![CDATA[In a groundbreaking research effort, Dr. A. Khan has delved into the intricate world of protein arginine methyltransferase 5 (PRMT5) inhibitors, utilizing advanced machine learning techniques and molecular modeling methodologies. The study, set to appear in the esteemed journal Molecular Diversity, explores not only the structural diversity of these small molecules but also their dynamic [&#8230;]]]></description>
		
		
		
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