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Related Experiment Video

Updated: Jun 16, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

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Published on: December 10, 2012

BAGE: a Bayesian framework for age prediction based on PBMC gene expression data.

Veronica Suaste1,2,3, Maria L Daza-Torres4,5, J Cricelio Montesinos-López4

  • 1Department of Biosciences, University of Oslo, Oslo, Norway. veronsua@uio.no.

BMC Bioinformatics
|June 13, 2026
PubMed
Summary
This summary is machine-generated.

We developed BAGE, a novel Bayesian framework for predicting chronological age using gene expression data from peripheral blood mononuclear cells (PBMCs). This method accurately estimates age and identifies immune cell-related biomarkers.

Keywords:
AgeAgingBayesianPBMCPredictionTranscriptome

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Published on: January 26, 2024

Area of Science:

  • Genomics
  • Bioinformatics
  • Aging Research

Background:

  • Chronological age estimation from biological data is crucial for aging studies.
  • Transcriptomic data-based age prediction is sample-type dependent.
  • A gap exists for bulk RNA-seq age predictors specifically trained on PBMCs.

Purpose of the Study:

  • To develop and validate a novel age prediction model using bulk RNA-seq data from PBMCs.
  • To address batch effects and technical heterogeneity in transcriptomic datasets.
  • To identify a robust gene signature for age prediction in PBMCs.

Main Methods:

  • Aggregated 16 PBMC bulk transcriptomic datasets (174 healthy individuals).
  • Implemented BAGE (Bayesian framework for age prediction from gene expression data) using a Bayesian linear mixed model.
  • Incorporated dataset as a random intercept to account for batch effects.

Main Results:

  • The best BAGE model achieved R-squared of 0.86 and MAE of 5.5, outperforming existing methods.
  • A consensus signature of 70 genes, stable across datasets, was identified.
  • Pathway analysis indicated the signature is related to natural killer cells.

Conclusions:

  • Explicitly modeling heterogeneity and using a PBMC-specific signature improved predictive accuracy.
  • The BAGE framework is adaptable and extensible for future research.
  • The identified gene set may serve as interpretable biomarkers for immune aging.