BayesAge 2.0: A Maximum Likelihood Algorithm to Predict Transcriptomic Age.

Area of Science:

• Genomics
• Computational Biology
• Aging Research

Background:

• Aging is a complex biological process influenced by genetic and environmental factors.
• Transcriptomic age (tAge) prediction from RNA-seq data is crucial for aging research.
• Existing methods may exhibit age bias and computational inefficiencies.

Purpose of the Study:

• To introduce BayesAge 2.0, an enhanced maximum likelihood algorithm for predicting transcriptomic age.
• To improve upon the original BayesAge framework for epigenetic age prediction.
• To provide a more accurate and computationally efficient tool for tAge prediction.

Main Methods:

• BayesAge 2.0 integrates a Poisson distribution for count-based gene expression data.
• LOWESS smoothing is employed to model non-linear gene-age relationships.
• The algorithm was compared against traditional linear models like Elastic Net regression.

Main Results:

• BayesAge 2.0 demonstrates significant improvements over traditional linear models.
• Minimal age-associated bias was observed in prediction residuals.
• Reference construction and cross-validation are computationally more efficient than Elastic Net regression.

Conclusions:

• BayesAge 2.0 is a robust, accurate, and efficient tool for transcriptomic age prediction.
• The algorithm addresses key limitations of previous methods, including age bias.
• It represents a notable advance for aging research and the development of aging biomarkers.