scholarly journals Quantifying critical states of complex diseases using single-sample dynamic network biomarkers

2017 ◽  
Vol 13 (7) ◽  
pp. e1005633 ◽  
Author(s):  
Xiaoping Liu ◽  
Xiao Chang ◽  
Rui Liu ◽  
Xiangtian Yu ◽  
Luonan Chen ◽  
...  
Keyword(s):  
Dynamic Network ◽  
Complex Diseases ◽  
Single Sample ◽  
Critical States ◽  
Network Biomarkers

scholarly journals Identifying Critical States of Complex Diseases by Single-Sample Jensen-Shannon Divergence

2021 ◽  
Vol 11 ◽  
Author(s):  
Jinling Yan ◽  
Peiluan Li ◽  
Rong Gao ◽  
Ying Li ◽  
Luonan Chen
Keyword(s):  
Dynamic System ◽  
Critical State ◽  
Influenza Virus Infection ◽  
Complex Diseases ◽  
Disease State ◽  
Single Sample ◽  
Critical States ◽  
Inconsistency Index ◽  
Sample Data ◽  
Jensen Shannon Divergence

MotivationThe evolution of complex diseases can be modeled as a time-dependent nonlinear dynamic system, and its progression can be divided into three states, i.e., the normal state, the pre-disease state and the disease state. The sudden deterioration of the disease can be regarded as the state transition of the dynamic system at the critical state or pre-disease state. How to detect the critical state of an individual before the disease state based on single-sample data has attracted many researchers’ attention.MethodsIn this study, we proposed a novel approach, i.e., single-sample-based Jensen-Shannon Divergence (sJSD) method to detect the early-warning signals of complex diseases before critical transitions based on individual single-sample data. The method aims to construct score index based on sJSD, namely, inconsistency index (ICI).ResultsThis method is applied to five real datasets, including prostate cancer, bladder urothelial carcinoma, influenza virus infection, cervical squamous cell carcinoma and endocervical adenocarcinoma and pancreatic adenocarcinoma. The critical states of 5 datasets with their corresponding sJSD signal biomarkers are successfully identified to diagnose and predict each individual sample, and some “dark genes” that without differential expressions but are sensitive to ICI score were revealed. This method is a data-driven and model-free method, which can be applied to not only disease prediction on individuals but also targeted drug design of each disease. At the same time, the identification of sJSD signal biomarkers is also of great significance for studying the molecular mechanism of disease progression from a dynamic perspective.

Identifying critical states of hepatocellular carcinoma based on landscape dynamic network biomarkers

2020 ◽  
Vol 85 ◽  
pp. 107202 ◽  
Author(s):  
Yichen Sun ◽  
Hongqian Zhao ◽  
Min Wu ◽  
Junhua Xu ◽  
Shanshan Zhu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Dynamic Network ◽  
Critical States ◽  
Network Biomarkers ◽  
Landscape Dynamic

scholarly journals Detection for disease tipping points by landscape dynamic network biomarkers

2018 ◽  
Vol 6 (4) ◽  
pp. 775-785 ◽  
Author(s):  
Xiaoping Liu ◽  
Xiao Chang ◽  
Siyang Leng ◽  
Hui Tang ◽  
Kazuyuki Aihara ◽  
...  
Keyword(s):  
Dynamic Network ◽  
Good Prognosis ◽  
Single Sample ◽  
Physiological Data ◽  
Tipping Points ◽  
Virus Infections ◽  
Model Free ◽  
Network Biomarkers ◽  
Landscape Dynamic ◽  
The Individual

ABSTRACT A new model-free method has been developed and termed the landscape dynamic network biomarker (l-DNB) methodology. The method is based on bifurcation theory, which can identify tipping points prior to serious disease deterioration using only single-sample omics data. Here, we show that l-DNB provides early-warning signals of disease deterioration on a single-sample basis and also detects critical genes or network biomarkers (i.e. DNB members) that promote the transition from normal to disease states. As a case study, l-DNB was used to predict severe influenza symptoms prior to the actual symptomatic appearance in influenza virus infections. The l-DNB approach was then also applied to three tumor disease datasets from the TCGA and was used to detect critical stages prior to tumor deterioration using an individual DNB for each patient. The individual DNBs were further used as individual biomarkers in the analysis of physiological data, which led to the identification of two biomarker types that were surprisingly effective in predicting the prognosis of tumors. The biomarkers can be considered as common biomarkers for cancer, wherein one indicates a poor prognosis and the other indicates a good prognosis.

scholarly journals Identifying Dysregulated lncRNA-Associated ceRNA Network Biomarkers in CML Based on Dynamical Network Biomarkers

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Junhua Xu ◽  
Min Wu ◽  
Yichen Sun ◽  
Hongqian Zhao ◽  
Yujie Wang ◽  
...  
Keyword(s):  
Myeloid Leukemia ◽  
Clinical Medicine ◽  
Critical Index ◽  
Dynamic Network ◽  
Disease Outbreaks ◽  
Chronic Phase ◽  
Cerna Network ◽  
Network Biomarkers ◽  
Timely Treatment ◽  
Competitive Endogenous Rna

The incidence of chronic myeloid leukemia (CML) is increasing year by year, which is a serious threat to human health. Early diagnosis can reduce mortality and improve prognosis. LncRNAs have been shown to be effective biomarkers for a variety of diseases and can act as competitive endogenous RNA (ceRNA). In this study, the dysregulated lncRNA-associated ceRNA networks (DLCN) of the chronic phase (CP), accelerated phase (AP), and blastic crisis (BC) for CML are constructed. Then, based on dynamic network biomarkers (DNB), some dysregulated lncRNA-associated ceRNA network biomarkers of CP, AP, and BC for CML are identified according to DNB criteria. Thus, a lncRNA (SNHG5) is identified from DLCN_CP, a lncRNA (DLEU2) is identified from DLCN_AP, and two lncRNAs (SNHG3, SNHG5) are identified from DLCN_BC. In addition, the critical index (CI) used to detect disease outbreaks shows that CML occurs in CP, which is consistent with clinical medicine. By analyzing the functions of the identified ceRNA network biomarkers, it has been found that they are effective lncRNA biomarkers directly or indirectly related to CML. The result of this study will help deepen the understanding of CML pathology from the perspective of ceRNA and help discover the effective biomarkers of CP, AP, and BC for CML in the future, so as to help patients get timely treatment and reduce the mortality of CML.

scholarly journals Disease Ionomics: Understanding the Role of Ions in Complex Disease

2020 ◽  
Vol 21 (22) ◽  
pp. 8646
Author(s):  
Yan Zhang ◽  
Yinzhen Xu ◽  
Lin Zheng
Keyword(s):  
Complex Disease ◽  
Dynamic Network ◽  
Complex Diseases ◽  
Living Organism ◽  
Future Trends ◽  
Pathological Conditions ◽  
Systematic Understanding ◽  
Elemental Profiling ◽  
Made In

Ionomics is a novel multidisciplinary field that uses advanced techniques to investigate the composition and distribution of all minerals and trace elements in a living organism and their variations under diverse physiological and pathological conditions. It involves both high-throughput elemental profiling technologies and bioinformatic methods, providing opportunities to study the molecular mechanism underlying the metabolism, homeostasis, and cross-talk of these elements. While much effort has been made in exploring the ionomic traits relating to plant physiology and nutrition, the use of ionomics in the research of serious diseases is still in progress. In recent years, a number of ionomic studies have been carried out for a variety of complex diseases, which offer theoretical and practical insights into the etiology, early diagnosis, prognosis, and therapy of them. This review aims to give an overview of recent applications of ionomics in the study of complex diseases and discuss the latest advances and future trends in this area. Overall, disease ionomics may provide substantial information for systematic understanding of the properties of the elements and the dynamic network of elements involved in the onset and development of diseases.

SMAD7 and SERPINE1 as novel dynamic network biomarkers detect and regulate the tipping point of TGF-beta induced EMT

2020 ◽  
Vol 65 (10) ◽  
pp. 842-853
Author(s):  
Zhonglin Jiang ◽  
Lina Lu ◽  
Yuwei Liu ◽  
Si Zhang ◽  
Shuxian Li ◽  
...  
Keyword(s):  
Dynamic Network ◽  
Tipping Point ◽  
Tgf Beta ◽  
Network Biomarkers
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