Banding Together: A Systematic Comparison of The Cancer Genome Atlas and the Mitelman Databases
Bibliographic record
Abstract
Abstract Cytogenetic aberrations at the single-cell level represent an important characteristic of cancer cells relevant to tumor evolution and prognosis. However, with the advent of The Cancer Genome Atlas (TCGA), there has been a major shift in cancer research to the use of data from aggregate cell populations. Given that tumor cells harbor hundreds to thousands of biologically relevant genetic alterations that manifest as intratumor heterogeneity, these aggregate analyses may miss alterations readily observable at single-cell resolution. Using the Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer, we developed an algorithm to parse International System for Cytogenetic Nomenclature notation for quantitative abnormalities. Comparison of the Mitelman database and TCGA demonstrated that the Mitelman database is a powerful resource, and that cytogenetic aberrations captured by traditional approaches used in Mitelman database are on par with population-based genomic analyses used in TCGA. This algorithm will help nonspecialists to overcome the challenges associated with the format and syntax of the Mitelman database. Significance: A novel in silico approach compares cytogenetic data between the Mitelman database and TCGA, highlighting the advantages and limitations of both datasets.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".