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	<id>https://transhumanist.ru/index.php?action=history&amp;feed=atom&amp;title=CDH2</id>
	<title>CDH2 - История изменений</title>
	<link rel="self" type="application/atom+xml" href="https://transhumanist.ru/index.php?action=history&amp;feed=atom&amp;title=CDH2"/>
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	<updated>2026-06-22T19:07:57Z</updated>
	<subtitle>История изменений этой страницы в вики</subtitle>
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		<id>https://transhumanist.ru/index.php?title=CDH2&amp;diff=6156&amp;oldid=prev</id>
		<title>OdysseusBot: Новая страница: «Cadherin-2 precursor (CDw325) (Neural cadherin) (N-cadherin) (CD325 antigen) [CDHN] [NCAD]  ==Publications==  {{medline-entry |title=CellBIC: bimodality-based top...»</title>
		<link rel="alternate" type="text/html" href="https://transhumanist.ru/index.php?title=CDH2&amp;diff=6156&amp;oldid=prev"/>
		<updated>2021-05-12T15:14:31Z</updated>

		<summary type="html">&lt;p&gt;Новая страница: «Cadherin-2 precursor (CDw325) (Neural cadherin) (N-cadherin) (CD325 antigen) [CDHN] [NCAD]  ==Publications==  {{medline-entry |title=CellBIC: bimodality-based top...»&lt;/p&gt;
&lt;p&gt;&lt;b&gt;Новая страница&lt;/b&gt;&lt;/p&gt;&lt;div&gt;Cadherin-2 precursor (CDw325) (Neural cadherin) (N-cadherin) (CD325 antigen) [CDHN] [NCAD]&lt;br /&gt;
&lt;br /&gt;
==Publications==&lt;br /&gt;
&lt;br /&gt;
{{medline-entry&lt;br /&gt;
|title=CellBIC: bimodality-based top-down clustering of single-cell RNA sequencing data reveals hierarchical structure of the cell type.&lt;br /&gt;
|pubmed-url=https://pubmed.ncbi.nlm.nih.gov/30102368&lt;br /&gt;
|abstract=Single-cell RNA sequencing (scRNA-seq) is a powerful tool to study heterogeneity and dynamic changes in cell populations. Clustering scRNA-seq is essential in identifying new cell types and studying their characteristics. We develop CellBIC (single Cell BImodal Clustering) to cluster scRNA-seq data based on modality in the gene expression distribution. Compared with classical bottom-up approaches that rely on a distance metric, CellBIC performs hierarchical clustering in a top-down manner. CellBIC outperformed the bottom-up hierarchical clustering approach and other recently developed clustering algorithms while maintaining the hierarchical structure of cells. Importantly, CellBIC identifies type 2 diabetes and age specific β cell signatures characterized by [[SIX3]] and [[CDH2]], respectively.&lt;br /&gt;
|mesh-terms=* Aging&lt;br /&gt;
* Algorithms&lt;br /&gt;
* Antigens, CD&lt;br /&gt;
* Cadherins&lt;br /&gt;
* Cluster Analysis&lt;br /&gt;
* Computational Biology&lt;br /&gt;
* Diabetes Mellitus, Type 2&lt;br /&gt;
* Gene Expression Profiling&lt;br /&gt;
* Genetic Markers&lt;br /&gt;
* Humans&lt;br /&gt;
* Islets of Langerhans&lt;br /&gt;
* Pancreas&lt;br /&gt;
* Sequence Analysis, RNA&lt;br /&gt;
* Single-Cell Analysis&lt;br /&gt;
&lt;br /&gt;
|full-text-url=https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6265269&lt;br /&gt;
}}&lt;/div&gt;</summary>
		<author><name>OdysseusBot</name></author>
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