Multi-Omics Data Integration for Improved Cancer Subtyping via Denoising Autoencoder-Based Multi-Kernel Learning

Objectives: Cancer, characterized by its profound complexity and heterogeneity, arises from a multitude of molecular disruptions. The pursuit of identifying distinct cancer subtypes is driven by the need to stratify patients into clinically coherent subgroups, each exhibiting unique prognostic outco...

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Vydáno v:	Genes Ročník 16; číslo 11; s. 1246
Hlavní autoři:	Yao, Xiukun, Wang, Tong, Yang, Qi, Wang, Jiawen, Qi, Yao, Xu, Tong, Wei, Zhiwen, Cui, Yuehua, Cao, Hongyan, Yun, Keming
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 22.10.2025
Témata:	Autoencoder Biological analysis Biomarkers, Tumor - genetics Cancer Clustering Computational Biology - methods Datasets Deep Learning DNA methylation Genomes Genomics - methods Glioma Glioma - classification Glioma - genetics Gliomas Humans Kidney Neoplasms - classification Kidney Neoplasms - genetics Methods Multiomics Neoplasms - classification Neoplasms - genetics Noise control Oncology, Experimental Performance evaluation Precision medicine Prognosis Renal cell carcinoma Simulation methods China deep learning hierarchical multi-kernel learning denoising autoencoder multi-omics data integration subtypes identification
ISSN:	2073-4425, 2073-4425
On-line přístup:	Získat plný text
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