Structured Expression Dispersion as a Context-Dependent Survival Signal: A Falsification and Mechanism Study in TCGA Glioblastoma (v3.0)

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Autore principale: Mitchell , Thomas S.
Natura: Recurso digital
Lingua:inglese
Pubblicazione: Zenodo 2026
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author Mitchell , Thomas S.
author_facet Mitchell , Thomas S.
contents <p>This study investigates whether patient-level gene expression dispersion (coefficient of variation, CV) represents a biologically meaningful survival signal or an emergent statistical property of high-dimensional data. Using TCGA glioblastoma (GBM), the CV metric significantly stratifies survival. However, systematic falsification—including random gene controls, permutation-based structure destruction, cross-cancer testing, network analysis, spectral decomposition, predictive validation, and high-dimensional geometry—demonstrates that the signal is context-dependent, non-generalizable, weakly predictive, and not supported by pathway or network structure. These findings show that survival signals can arise from aggregation effects in high-dimensional systems and emphasize the need for structural validation in omics research.</p>
format Recurso digital
id zenodo_https___doi_org_10_5281_zenodo_19966788
institution Zenodo
language eng
publishDate 2026
publisher Zenodo
record_format zenodo
spellingShingle Structured Expression Dispersion as a Context-Dependent Survival Signal: A Falsification and Mechanism Study in TCGA Glioblastoma (v3.0)
Mitchell , Thomas S.
Glioblastoma, TCGA, gene expression, coefficient of variation, survival analysis, Cox regression, high-dimensional data, statistical structure, falsification, omics analysis
<p>This study investigates whether patient-level gene expression dispersion (coefficient of variation, CV) represents a biologically meaningful survival signal or an emergent statistical property of high-dimensional data. Using TCGA glioblastoma (GBM), the CV metric significantly stratifies survival. However, systematic falsification—including random gene controls, permutation-based structure destruction, cross-cancer testing, network analysis, spectral decomposition, predictive validation, and high-dimensional geometry—demonstrates that the signal is context-dependent, non-generalizable, weakly predictive, and not supported by pathway or network structure. These findings show that survival signals can arise from aggregation effects in high-dimensional systems and emphasize the need for structural validation in omics research.</p>
title Structured Expression Dispersion as a Context-Dependent Survival Signal: A Falsification and Mechanism Study in TCGA Glioblastoma (v3.0)
topic Glioblastoma, TCGA, gene expression, coefficient of variation, survival analysis, Cox regression, high-dimensional data, statistical structure, falsification, omics analysis
url https://doi.org/10.5281/zenodo.19966788