BELMM: Bayesian model selection and random walk smoothing in time-series clustering.

ABSTRACT:

Motivation

Due to advances in measuring technology, many new phenotype, gene expression, and other omics time-course datasets are now commonly available. Cluster analysis may provide useful information about the structure of such data.

Results

In this work, we propose BELMM (Bayesian Estimation of Latent Mixture Models): a flexible framework for analysing, clustering, and modelling time-series data in a Bayesian setting. The framework is built on mixture modelling: first, the mean curves of the mixture components are assumed to follow random walk smoothing priors. Second, we choose the most plausible model and the number of mixture components using the Reversible-jump Markov chain Monte Carlo. Last, we assign the individual time series into clusters based on the similarity t

SUBMITTER: Sarala O 

PROVIDER: S-EPMC10686958 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature