bulk-tumour-api: a programmatically accessible dataset of pre-processed bulk tumour sequencing data
Bibliographic record
Abstract
This repository, including the API, are currently under development. bulk-tumour-api: A programmatically accessible dataset of pre-processed bulk tumour sequencing data. The python API can be found at https://github.com/tomouellette/bulk-tumour-api. All data stored in this repository have been collected from open access online sources. Original references and sources are provided in database.tsv (for empirical patient data) and synthetic.tsv (for simulated data). A note on datasets: All empirical patient sequencing samples have been processed into pseudo-VCF files which at minimum contain the following columns: sample identifier (sample), patient identifier (patient), chromosome (chr), position (pos), variant allele frequency (VAF), alternate read counts (t_alt_count), depth (DP), and total copy number (total_cn). However, if more data is required, unprocessed data including copy number segments or gene-level calls, clinical, and/or biopsy level information can be found in the /raw/. All synthetic datasets have also been processed in pseudo-VCF files. In some cases, all ground truth information (e.g. subclone frequency) is contained within the pseudo-VCF. In other cases, additional meta/ground-truth information are in separate files; any simulated sample with a column marked has_meta = True will have multiple files that will be downloaded together.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.110 | 0.104 |
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".