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The Effect of Aging on Tissues

The Effect of Aging on Tissues

Several body functions deteriorate with age. The external signs of aging are easily identifiable. For example, the skin becomes dry, less elastic, and thins out, forming wrinkles. The skin of the face begins to appear looser due to a decrease in the levels of elastic and collagen fibers in the connective tissue. Additionally, melanin production in the hair follicle decreases with age, resulting in gray hair. Moreover, the senses of sight and hearing decline, so glasses and hearing aids may...

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Related Experiment Video

Updated: Mar 3, 2026

Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging

Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging

Published on: January 30, 2026

ORANGE: a machine learning approach for modeling tissue-specific aging from transcriptomic data.

Wasif Jalal¹, Mubasshira Musarrat¹, Md Abul Hassan Samee²

¹Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, ECE Building, West Palashi, Dhaka 1205, Bangladesh.

Briefings in Bioinformatics

|March 2, 2026

Summary

This summary is machine-generated.

This study models tissue-specific biological ages using machine learning on transcriptomic data. Accelerated aging in specific tissues correlates with increased mortality risk, highlighting transcriptomics

Keywords:

GTEx PLS regression age-gap aging biomarkers differentially expressed genes ensemble learning linear modeling machine learning transcriptomics

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Area of Science:

Genomics
Computational Biology
Aging Research

Background:

Aging is a fundamental biological process impacting health and disease.
The relationship between tissue-specific aging and mortality is not well understood.

Purpose of the Study:

To model tissue-specific biological ages using machine learning.
To develop an "age-gap" metric to quantify deviations from chronological age.
To investigate the correlation between tissue-specific aging patterns and mortality.

Main Methods:

Utilized GTEx transcriptomic data from 12 tissue types.
Applied machine learning models (Pearson correlation, elastic net regression, neural networks).
Employed feature selection strategies including Pearson correlation, age-related differentially expressed genes, and tissue-enriched genes.

Main Results:

Developed accurate models for tissue-specific biological age prediction (avg. RMSE 6.44 years, R2 0.64).
Identified significant tissue-specific aging patterns and "extreme agers" (20% in one tissue, 1% in multiple).
Found accelerated aging in specific tissues correlates with higher mortality risk from illness.

Conclusions:

Transcriptomic data and machine learning can effectively model tissue-specific biological aging.
Tissue-specific aging patterns and "age-gap" are linked to mortality.
Findings underscore the importance of transcriptomics in understanding aging and longevity.