Uncovering the Pre-Deterioration State during Disease Progression Based on Sample-Specific Causality Network Entropy(SCNE)  

作　　者：Jiayuan Zhong Hui Tang Ziyi Huang Hua Chai Fei Ling Pei Chen Rui Liu 

机构地区：[1]School of Mathematics and Big Data,Foshan University,Foshan 528000,China [2]School of Biology and Biological Engineering,South China University of Technology,Guangzhou 510640,China [3]School of Mathematics,South China University of Technology,Guangzhou 510640,China

出　　处：《Research》2025年第1期55-67,共13页研究(英文)

基　　金：supported by National Natural Science Foundation of China(nos.T2341022,12322119,62172164,and 12271180);Guangdong Provincial Key Laboratory of Human Digital Twin(2022B1212010004);Educational Commission of Guangdong Province of China(2023KQNCX073);the Natural Science Foundation of Guangdong Province of China(2022A-1515110759,and 2023A1515110558);Fundamental Research Funds for the Central Universities(2023ZYGXZR077).

摘　　要：Complex diseases do not always follow gradual progressions.Instead,they may experience sudden shifts known as critical states or tipping points,where a marked qualitative change occurs.Detecting such a pivotal transition or pre-deterioration state holds paramount importance due to its association with severe disease deterioration.Nevertheless,the task of pinpointing the pre-deterioration state for complex diseases remains an obstacle,especially in scenarios involving high-dimensional data with limited samples,where conventional statistical methods frequently prove inadequate.In this study,we introduce an innovative quantitative approach termed sample-specific causality network entropy(SCNE),which infers a sample-specific causality network for each individual and effectively quantifies the dynamic alterations in causal relations among molecules,thereby capturing critical points or pre-deterioration states of complex diseases.We substantiated the accuracy and efficacy of our approach via numerical simulations and by examining various real-world datasets,including single-cell data of epithelial cell deterioration(EPCD)in colorectal cancer,influenza infection data,and three different tumor cases from The Cancer Genome Atlas(TCGA)repositories.Compared to other existing six single-sample methods,our proposed approach exhibits superior performance in identifying critical signals or pre-deterioration states.Additionally,the efficacy of computational findings is underscored by analyzing the functionality of signaling biomarkers.

关 键 词：critical states sample specific causality network entropy pre deterioration state complex diseases tipping points high dimensional data numerical simulations 

分 类 号：R73[医药卫生—肿瘤]