Structured Expression Dispersion as a Context-Dependent Survival Signal: A Falsification and Mechanism Study in TCGA Glioblastoma (v3.0)
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2026
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| _version_ | 1866901285699059712 |
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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 |