Structure-preserving deep embedded clustering algorithm for incomplete gene expression data

Missing values inevitably appear in gene expression data, making it impossible to directly apply clustering algorithms to incomplete gene expression data to identify co-expressed genes. Deep autoencoders are often used for feature learning of data in clustering incomplete data due to their powerful...

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Veröffentlicht in:	Chinese Control Conference S. 8255 - 8261
Hauptverfasser:	Wang, Zhencheng, Li, Dan
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	Technical Committee on Control Theory, Chinese Association of Automation 28.07.2024
Schlagworte:	Analytical models Biology Clustering algorithms Correlation Data models Deep embedded clustering Gene expression data Imputation Joint optimization Missing value Representation learning Structure preserving
ISSN:	1934-1768
Online-Zugang:	Volltext
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