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Nonlinear joint latent variable models and integrative tumor subtype discovery
2016
Statistical analysis and data mining
Integrative analysis has been used to identify clusters by integrating data of disparate types, such as deoxyribonucleic acid (DNA) copy number alterations and DNA methylation changes for discovering novel subtypes of tumors. Most existing integrative analysis methods are based on joint latent variable models, which are generally divided into two classes: joint factor analysis and joint mixture modeling, with continuous and discrete parameterizations of the latent variables respectively.
doi:10.1002/sam.11306
pmid:29333206
pmcid:PMC5761081
fatcat:rx244ipogne2fmwfhbqxuwzqqm