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Method and Computer System for Dialog Optimization of Aging Biomarker Panels for Biological Age Assessment.

Vyacheslav N Krut'ko^1,2, Vitaly I Dontsov¹, Nina A Ermakova²

¹Institute for Systems Analysis Federal Research Center "Computer Science and Control" of Russian Academy of Sciences, Moscow, Russia.

Frontiers in Genetics

|March 22, 2021

Summary

This summary is machine-generated.

A new computer system optimizes biomarker panels for biological age (BA) assessment. This system enhances accuracy and reduces measurements, aiding anti-aging medicine and disease prevention.

Keywords:

aging biological age biomarkers of aging diagnostics of aging dialog optimization personalized medicine

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Area of Science:

Biomedical Engineering
Computational Biology
Gerontology

Background:

Accurate assessment of human biological age (BA) is crucial for understanding aging and preventing age-related diseases.
Current methods for BA assessment often rely on numerous biomarkers and can be affected by data noise and incompleteness.
Optimizing biomarker panels is essential for improving diagnostic accuracy and efficiency.

Purpose of the Study:

To develop a computer system for automated, step-by-step optimization of biomarker (BM) panels for BA assessment.
To enhance the accuracy of BA determination while minimizing the number of BMs required.
To provide a user-friendly platform for researchers to customize optimization criteria.

Main Methods:

Developed a novel algorithm and computer system (CS) for dialog-based optimization of BM panels.
Implemented optimization criteria including high correlation with chronological age (CA), minimal panel size, high BA assessment accuracy, and outlier rejection.
The CS processes physiological, biochemical, and other age-related BM data as input.

Main Results:

The developed CS can automatically construct optimized BM panels for BA assessment.
The system demonstrates the ability to increase BA determination accuracy and reduce the number of measured BMs.
Optimization criteria can be customized by researchers to suit specific task requirements.

Conclusions:

The developed CS offers a powerful tool for optimizing BM panels for biological age assessment.
This technology has significant potential applications in anti-aging medicine for disease prevention and treatment.
The system's user-friendly interface and customizable criteria facilitate its practical implementation in research and clinical settings.