Outcome-guided Bayesian clustering for disease subtype discovery using high-dimensional transcriptomic data.

ABSTRACT: Due to the tremendous heterogeneity of disease manifestations, many complex diseases that were once thought to be single diseases are now considered to have disease subtypes. Disease subtyping analysis, that is the identification of subgroups of patients with similar characteristics, is the first step to accomplish precision medicine. With the advancement of high-throughput technologies, omics data offers unprecedented opportunity to reveal disease subtypes. As a result, unsupervised clustering analysis has been widely used for this purpose. Though promising, the subtypes obtained from traditional quantitative approaches may not always be clinically meaningful (i.e. correlate with clinical outcomes). On the other hand, the collection of rich clinical data in modern epidemiology studies has

SUBMITTER: Meng L 

PROVIDER: S-EPMC11727188 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature