Identifying critical States of complex diseases by local network Wasserstein distance
Abstract Complex diseases often undergo abrupt transitions from pre-disease to disease states, with the pre-disease state is typically unstable but potentially reversible through timely intervention. Detecting these critical transitions is crucial. We propose a model-free method, Local Network Wasse...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-94521-0 |
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| author | Changchun Liu Pingjun Hou Lin Feng |
| author_facet | Changchun Liu Pingjun Hou Lin Feng |
| author_sort | Changchun Liu |
| collection | DOAJ |
| description | Abstract Complex diseases often undergo abrupt transitions from pre-disease to disease states, with the pre-disease state is typically unstable but potentially reversible through timely intervention. Detecting these critical transitions is crucial. We propose a model-free method, Local Network Wasserstein Distance (LNWD), for identifying critical transitions/pre-disease states in complex diseases using single sample analysis. LNWD measures statistical perturbations in normal samples caused by diseased samples using the Wasserstein distance, and identifies critical states by observing LNWD score changes. Applied to KIRP, KIRC, LUAD, ESCA (TCGA datasets) and GSE2565, GSE13268 (GEO datasets), the method successfully identified critical states in six disease datasets. This single-sample, local network-based approach provides early warning signals for medical diagnosis and holds great potential for personalized disease diagnosis. |
| format | Article |
| id | doaj-art-dd4bd126b1e34de6bd33b1fe01c38de4 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-dd4bd126b1e34de6bd33b1fe01c38de42025-08-20T02:41:34ZengNature PortfolioScientific Reports2045-23222025-03-0115111410.1038/s41598-025-94521-0Identifying critical States of complex diseases by local network Wasserstein distanceChangchun Liu0Pingjun Hou1Lin Feng2School of Mathematics and Statistics, Henan University of Science and TechnologySchool of Mathematics and Statistics, Henan University of Science and TechnologySchool of Mathematics and Statistics, Henan University of Science and TechnologyAbstract Complex diseases often undergo abrupt transitions from pre-disease to disease states, with the pre-disease state is typically unstable but potentially reversible through timely intervention. Detecting these critical transitions is crucial. We propose a model-free method, Local Network Wasserstein Distance (LNWD), for identifying critical transitions/pre-disease states in complex diseases using single sample analysis. LNWD measures statistical perturbations in normal samples caused by diseased samples using the Wasserstein distance, and identifies critical states by observing LNWD score changes. Applied to KIRP, KIRC, LUAD, ESCA (TCGA datasets) and GSE2565, GSE13268 (GEO datasets), the method successfully identified critical states in six disease datasets. This single-sample, local network-based approach provides early warning signals for medical diagnosis and holds great potential for personalized disease diagnosis.https://doi.org/10.1038/s41598-025-94521-0Complex diseaseCritical stateLocal networkWasserstein distanceSingle samplePremorbid state |
| spellingShingle | Changchun Liu Pingjun Hou Lin Feng Identifying critical States of complex diseases by local network Wasserstein distance Scientific Reports Complex disease Critical state Local network Wasserstein distance Single sample Premorbid state |
| title | Identifying critical States of complex diseases by local network Wasserstein distance |
| title_full | Identifying critical States of complex diseases by local network Wasserstein distance |
| title_fullStr | Identifying critical States of complex diseases by local network Wasserstein distance |
| title_full_unstemmed | Identifying critical States of complex diseases by local network Wasserstein distance |
| title_short | Identifying critical States of complex diseases by local network Wasserstein distance |
| title_sort | identifying critical states of complex diseases by local network wasserstein distance |
| topic | Complex disease Critical state Local network Wasserstein distance Single sample Premorbid state |
| url | https://doi.org/10.1038/s41598-025-94521-0 |
| work_keys_str_mv | AT changchunliu identifyingcriticalstatesofcomplexdiseasesbylocalnetworkwassersteindistance AT pingjunhou identifyingcriticalstatesofcomplexdiseasesbylocalnetworkwassersteindistance AT linfeng identifyingcriticalstatesofcomplexdiseasesbylocalnetworkwassersteindistance |