Sci—Thur PM: YIS — 09: ROC Analysis of Metabolomics Data Sets for Cancer Screening
Bibliographic record
Abstract
Disease processes are known to alter the metabolic pathways of affected cells and tissues in an organism. This perturbation of the steady state can lead to quantifiable changes in metabolites found in biofluids such as urine, blood or saliva. The objective of this work was to identify robust patterns of change in the metabolic profile of mouse urine as a group of experimental animals transitioned from healthy to tumour bearing. Samples were collected for one week before tumour cell injections and for a period of four weeks after injection. An age‐matched group of control mice was profiled to investigate the effects of age and non‐tumour related experimental procedures. Samples were analyzed using an 800 MHz NMR spectrometer and profiled using the Chenomx software platform. A total of 40 metabolites were evaluated in each sample. The paired t‐test was used to identify statistically significant changes (controlled for multiple hypothesis testing) in individual metabolites and ratios of metabolite pairs. Partial least squares discriminant analysis was used to identify more complicated relationships between metabolites. Of the 40 individual metabolites, 16 changed in a significant manner that was not also observed in the controls. Of the 780 metabolite ratios, 126 were significant. To investigate potential clinical utility, population distributions were subjected to ROC curve analysis. Individual metabolite‐based results produced areas under the curve (AUC) as high as 0.770, ratios produced AUCs as high as 0.937 and multivariate models produced AUC values as high as 0.942. AUC values larger than 0.9 are considered excellent.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.030 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".