MATHEMATICAL DECODING OF THE CORRELATION BETWEEN DIFFERENT ORGANS' ACTIVITIES: A REVIEW

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F25%3A50022589" target="_blank" >RIV/62690094:18450/25:50022589 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60076658:12310/25:43910741

Výsledek na webu

<a href="https://www.worldscientific.com/doi/10.1142/S0218348X25300120" target="_blank" >https://www.worldscientific.com/doi/10.1142/S0218348X25300120</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1142/S0218348X25300120" target="_blank" >10.1142/S0218348X25300120</a>

Jazyk výsledku

angličtina

Název v původním jazyce

MATHEMATICAL DECODING OF THE CORRELATION BETWEEN DIFFERENT ORGANS' ACTIVITIES: A REVIEW

Popis výsledku v původním jazyce

Understanding the correlation between different organ activities is essential for advancing knowledge in physiology, medicine, and bioengineering. While existing literature often focuses on individual organs, a significant gap remains in synthesizing the diverse mathematical methodologies used to decode complex multi-organ relationships. This review addresses that gap by providing a comprehensive analysis of advanced mathematical tools - including time series analysis, signal processing, entropy measures, fractal theory, network modeling, and machine learning (ML) - that have been applied to characterize dynamic inter-organ communication. We discuss how these methods reveal nonlinear, causal, and scale-invariant relationships among organ systems and how they are used to predict pathological conditions, monitor health status, and inform personalized interventions. By bridging theoretical models with clinical applications, this review offers a unified framework for understanding systemic physiology and supports future advancements in multi-organ diagnostics and therapies.

Název v anglickém jazyce

MATHEMATICAL DECODING OF THE CORRELATION BETWEEN DIFFERENT ORGANS' ACTIVITIES: A REVIEW

Popis výsledku anglicky

Understanding the correlation between different organ activities is essential for advancing knowledge in physiology, medicine, and bioengineering. While existing literature often focuses on individual organs, a significant gap remains in synthesizing the diverse mathematical methodologies used to decode complex multi-organ relationships. This review addresses that gap by providing a comprehensive analysis of advanced mathematical tools - including time series analysis, signal processing, entropy measures, fractal theory, network modeling, and machine learning (ML) - that have been applied to characterize dynamic inter-organ communication. We discuss how these methods reveal nonlinear, causal, and scale-invariant relationships among organ systems and how they are used to predict pathological conditions, monitor health status, and inform personalized interventions. By bridging theoretical models with clinical applications, this review offers a unified framework for understanding systemic physiology and supports future advancements in multi-organ diagnostics and therapies.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Fractals

ISSN

0218-348X

e-ISSN

1793-6543

Svazek periodika

33

Číslo periodika v rámci svazku

09

Stát vydavatele periodika

SG - Singapurská republika

Počet stran výsledku

14

Strana od-do

"Article Number: 2530012"

Kód UT WoS článku

001554144900001

EID výsledku v databázi Scopus

2-s2.0-105013754301