Wading through the noise of "multi-omics" to identify prognostic biomarkers in hepatocellular carcinoma.
Bibliographic record
Abstract
Multi-omics, the molecular analysis of genes, transcriptional RNA and proteins, allows researchers document the mechanism of action of a target gene. However multi-omics may result in an avalanche of information when used to screen a population. It is very difficult to discern a pattern or signal related to a disease or its progression. Differential multi-omics exploits our ability to see differences between subjects who are similar in all respects except for the outcome being tested. Twin studies are an example of this. Miao and colleagues compared two patients who had diverse outcomes following treatment of multi-focal hepatocellular carcinoma (HCC) to identify seven candidates as the responsible genes. In a larger cohort of patients with HCC they narrowed the field down to a single target down. By looking at progression of HCC, they isolated TTK, a protein kinase which disrupts the interaction of the tumour suppressor p53 with the oncogene MDM2. TTK-high tumours recurred 3 times faster than TTK-low tumours.
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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 | 0.027 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".