Identifying Critical States of Complex Diseases by Single-Sample 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 tra...
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Frontiers Media S.A.
2021-06-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2021.684781/full |
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| author | Jinling Yan Peiluan Li Rong Gao Ying Li Luonan Chen Luonan Chen Luonan Chen Luonan Chen |
| author_facet | Jinling Yan Peiluan Li Rong Gao Ying Li Luonan Chen Luonan Chen Luonan Chen Luonan Chen |
| author_sort | Jinling Yan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-19T21:46:15Z |
| format | Article |
| id | doaj.art-dbd66ce1579946ffaa81b44415f00473 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-19T21:46:15Z |
| publishDate | 2021-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-dbd66ce1579946ffaa81b44415f004732022-12-21T20:04:32ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-06-011110.3389/fonc.2021.684781684781Identifying Critical States of Complex Diseases by Single-Sample Jensen-Shannon DivergenceJinling Yan0Peiluan Li1Rong Gao2Ying Li3Luonan Chen4Luonan Chen5Luonan Chen6Luonan Chen7School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, ChinaSchool of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, ChinaSchool of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, ChinaSchool of Mathematics and Statistics, Henan University of Science and Technology, Luoyang, ChinaState Key Laboratory of Cell Biology, Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, ChinaCenter for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, ChinaKey Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, ChinaSchool of Life Science and Technology, ShanghaiTech University, Shanghai, ChinaMotivationThe 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.https://www.frontiersin.org/articles/10.3389/fonc.2021.684781/fullcomplex diseasecritical statesJSD signal biomarkersingle-sample Jensen-Shannon divergence (sJSD)dynamic network biomarker (DNB)dark genes |
| spellingShingle | Jinling Yan Peiluan Li Rong Gao Ying Li Luonan Chen Luonan Chen Luonan Chen Luonan Chen Identifying Critical States of Complex Diseases by Single-Sample Jensen-Shannon Divergence Frontiers in Oncology complex disease critical state sJSD signal biomarker single-sample Jensen-Shannon divergence (sJSD) dynamic network biomarker (DNB) dark genes |
| title | Identifying Critical States of Complex Diseases by Single-Sample Jensen-Shannon Divergence |
| title_full | Identifying Critical States of Complex Diseases by Single-Sample Jensen-Shannon Divergence |
| title_fullStr | Identifying Critical States of Complex Diseases by Single-Sample Jensen-Shannon Divergence |
| title_full_unstemmed | Identifying Critical States of Complex Diseases by Single-Sample Jensen-Shannon Divergence |
| title_short | Identifying Critical States of Complex Diseases by Single-Sample Jensen-Shannon Divergence |
| title_sort | identifying critical states of complex diseases by single sample jensen shannon divergence |
| topic | complex disease critical state sJSD signal biomarker single-sample Jensen-Shannon divergence (sJSD) dynamic network biomarker (DNB) dark genes |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2021.684781/full |
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