Identifying critical States of complex diseases by local network Wasserstein distance.

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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.", "claims": [{"id": "claim-method", "statement": "In this paper, we propose a model-free method for detecting critical states of complex diseases based on a single sample, namely the Local Network Wasserstein Distance (LNWD) method, which is grounded in nonlinear dynamical systems theory.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40113925", "locator": "sections[1] \"Introduction\" paragraph 6", "quote": "In this paper, we propose a model-free method for detecting critical states of complex diseases based on a single sample, namely the Local Network Wasserstein Distance (LNWD) method, which is grounded in nonlinear dynamical systems theory."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analysed during the current study are accessible in the TCGA repository and the GEO repository. 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