Useful genetic variation databases for oncologists investigating the genetic basis of variable treatment response and survival in cancer
Bibliographic record
Abstract
Identification of the genetic basis of variable treatment response, prognosis and survival in cancer patients (i.e. personalized medicine) is an important aim in current medicine. Millions of genetic variations exist in the human genome, some of which are already found to be directly involved in variable treatment response and survival among cancer patients. GENETIC VARIATION DATABASES: Special databases curate, compile, organize and post information related to these genetic variations for the scientific community in a user friendly and free-to-access manner via the World Wide Web. FUTURE DIRECTIONS AND CONCLUSION: Clinicians have a critical role in genetic predictive and prognostic studies. In this review, main public-domain databases on genetic variations, including the two comprehensive genetic variation databases (dbSNP and HapMap), a pharmacogenomics database (PharmGKB), two resequencing-based genetic variation databases (SeattleSNPs and EGP), a population-based genetic variation database (JSNPs), and a copy-number variant database (DGV), and their utility in cancer research are discussed. Utilization of these databases can assist clinicians in their studies related to treatment response and prognosis in cancer patients.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".