Deeply integrating latent consistent representations in high-noise multi-omics data for cancer subtyping

Abstract Cancer is a complex and high-mortality disease regulated by multiple factors. Accurate cancer subtyping is crucial for formulating personalized treatment plans and improving patient survival rates. The underlying mechanisms that drive cancer progression can be comprehensively understood by...

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Bibliographic Details
Published in:	Briefings in bioinformatics Vol. 25; no. 2
Main Authors:	Cai, Yueyi, Wang, Shunfang
Format:	Journal Article
Language:	English
Published:	England Oxford University Press 22.01.2024 Oxford Publishing Limited (England)
Subjects:	Algorithms Cancer Carcinoma, Renal Cell - genetics Cluster Analysis Clustering Deep learning Genomes Humans Integration Kidney Neoplasms - genetics Machine learning Multiomics Neoplasms - genetics Noise levels Problem Solving Protocol Renal cell carcinoma Representations Survival contrastive learning multi-omics data integration cancer subtyping variational autoencoder
ISSN:	1467-5463, 1477-4054, 1477-4054
Online Access:	Get full text
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