A Bayesian hierarchical hidden Markov model for clustering and gene selection: Application to kidney cancer gene expression data.
Biom J
; 66(4): e2300173, 2024 Jun.
Article
in En
| MEDLINE
| ID: mdl-38817110
ABSTRACT
We introduce a Bayesian approach for biclustering that accounts for the prior functional dependence between genes using hidden Markov models (HMMs). We utilize biological knowledge gathered from gene ontologies and the hidden Markov structure to capture the potential coexpression of neighboring genes. Our interpretable model-based clustering characterized each cluster of samples by three groups of features overexpressed, underexpressed, and irrelevant features. The proposed methods have been implemented in an R package and are used to analyze both the simulated data and The Cancer Genome Atlas kidney cancer data.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Markov Chains
/
Bayes Theorem
/
Kidney Neoplasms
Limits:
Humans
Language:
En
Journal:
Biom J
Year:
2024
Document type:
Article
Affiliation country:
Estados Unidos