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...

Full description

Saved in:

Bibliographic Details
Published in:	Genes Vol. 16; no. 11; p. 1246
Main Authors:	Yao, Xiukun, Wang, Tong, Yang, Qi, Wang, Jiawen, Qi, Yao, Xu, Tong, Wei, Zhiwen, Cui, Yuehua, Cao, Hongyan, Yun, Keming
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 22.10.2025
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!