Cargando…
Detecting the tipping points in a three-state model of complex diseases by temporal differential networks
BACKGROUND: The progression of complex diseases, such as diabetes and cancer, is generally a nonlinear process with three stages, i.e., normal state, pre-disease state, and disease state, where the pre-disease state is a critical state or tipping point immediately preceding the disease state. Tradit...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5658963/ https://www.ncbi.nlm.nih.gov/pubmed/29073904 http://dx.doi.org/10.1186/s12967-017-1320-7 |
_version_ | 1783274085985288192 |
---|---|
author | Chen, Pei Li, Yongjun Liu, Xiaoping Liu, Rui Chen, Luonan |
author_facet | Chen, Pei Li, Yongjun Liu, Xiaoping Liu, Rui Chen, Luonan |
author_sort | Chen, Pei |
collection | PubMed |
description | BACKGROUND: The progression of complex diseases, such as diabetes and cancer, is generally a nonlinear process with three stages, i.e., normal state, pre-disease state, and disease state, where the pre-disease state is a critical state or tipping point immediately preceding the disease state. Traditional biomarkers aim to identify a disease state by exploiting the information of differential expressions for the observed molecules, but may fail to detect a pre-disease state because there are generally little significant differences between the normal and pre-disease states. Thus, it is challenging to signal the pre-disease state, which actually implies the disease prediction. METHODS: In this work, by exploiting the information of differential associations among the observed molecules between the normal and pre-disease states, we propose a temporal differential network based computational method to accurately signal the pre-disease state or predict the occurrence of severe disease. The theoretical foundation of this work is the quantification of the critical state using dynamical network biomarkers. RESULTS: Considering that there is one stationary Markov process before reaching the tipping point, a novel index, inconsistency score (I-score), is proposed to quantitatively measure the change of the stationary processes from the normal state so as to detect the onset of pre-disease state. In other words, a drastic increase of I-score implies the high inconsistency with the preceding stable state and thus signals the upcoming critical transition. This approach is applied to the simulated and real datasets of three diseases, which demonstrates the effectiveness of our method for predicting the deterioration into disease states. Both functional analysis and pathway enrichment also validate the computational results from the perspectives of both molecules and networks. CONCLUSIONS: At the molecular network level, this method provides a computational way of unravelling the underlying mechanism of the dynamical progression when a biological system is near the tipping point, and thus detecting the early-warning signal of the imminent critical transition, which may help to achieve timely intervention. Moreover, the rewiring of differential networks effectively extracts discriminatively interpretable features, and systematically demonstrates the dynamical change of a biological system. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12967-017-1320-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5658963 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-56589632017-11-01 Detecting the tipping points in a three-state model of complex diseases by temporal differential networks Chen, Pei Li, Yongjun Liu, Xiaoping Liu, Rui Chen, Luonan J Transl Med Research BACKGROUND: The progression of complex diseases, such as diabetes and cancer, is generally a nonlinear process with three stages, i.e., normal state, pre-disease state, and disease state, where the pre-disease state is a critical state or tipping point immediately preceding the disease state. Traditional biomarkers aim to identify a disease state by exploiting the information of differential expressions for the observed molecules, but may fail to detect a pre-disease state because there are generally little significant differences between the normal and pre-disease states. Thus, it is challenging to signal the pre-disease state, which actually implies the disease prediction. METHODS: In this work, by exploiting the information of differential associations among the observed molecules between the normal and pre-disease states, we propose a temporal differential network based computational method to accurately signal the pre-disease state or predict the occurrence of severe disease. The theoretical foundation of this work is the quantification of the critical state using dynamical network biomarkers. RESULTS: Considering that there is one stationary Markov process before reaching the tipping point, a novel index, inconsistency score (I-score), is proposed to quantitatively measure the change of the stationary processes from the normal state so as to detect the onset of pre-disease state. In other words, a drastic increase of I-score implies the high inconsistency with the preceding stable state and thus signals the upcoming critical transition. This approach is applied to the simulated and real datasets of three diseases, which demonstrates the effectiveness of our method for predicting the deterioration into disease states. Both functional analysis and pathway enrichment also validate the computational results from the perspectives of both molecules and networks. CONCLUSIONS: At the molecular network level, this method provides a computational way of unravelling the underlying mechanism of the dynamical progression when a biological system is near the tipping point, and thus detecting the early-warning signal of the imminent critical transition, which may help to achieve timely intervention. Moreover, the rewiring of differential networks effectively extracts discriminatively interpretable features, and systematically demonstrates the dynamical change of a biological system. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12967-017-1320-7) contains supplementary material, which is available to authorized users. BioMed Central 2017-10-26 /pmc/articles/PMC5658963/ /pubmed/29073904 http://dx.doi.org/10.1186/s12967-017-1320-7 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Chen, Pei Li, Yongjun Liu, Xiaoping Liu, Rui Chen, Luonan Detecting the tipping points in a three-state model of complex diseases by temporal differential networks |
title | Detecting the tipping points in a three-state model of complex diseases by temporal differential networks |
title_full | Detecting the tipping points in a three-state model of complex diseases by temporal differential networks |
title_fullStr | Detecting the tipping points in a three-state model of complex diseases by temporal differential networks |
title_full_unstemmed | Detecting the tipping points in a three-state model of complex diseases by temporal differential networks |
title_short | Detecting the tipping points in a three-state model of complex diseases by temporal differential networks |
title_sort | detecting the tipping points in a three-state model of complex diseases by temporal differential networks |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5658963/ https://www.ncbi.nlm.nih.gov/pubmed/29073904 http://dx.doi.org/10.1186/s12967-017-1320-7 |
work_keys_str_mv | AT chenpei detectingthetippingpointsinathreestatemodelofcomplexdiseasesbytemporaldifferentialnetworks AT liyongjun detectingthetippingpointsinathreestatemodelofcomplexdiseasesbytemporaldifferentialnetworks AT liuxiaoping detectingthetippingpointsinathreestatemodelofcomplexdiseasesbytemporaldifferentialnetworks AT liurui detectingthetippingpointsinathreestatemodelofcomplexdiseasesbytemporaldifferentialnetworks AT chenluonan detectingthetippingpointsinathreestatemodelofcomplexdiseasesbytemporaldifferentialnetworks |