Genome-wide copy number variant data for inflammatory bowel disease in a caucasian population
Bibliographic record
Abstract
Genome-wide copy-number association studies offer new opportunities to identify the mechanisms underlying complex diseases, including chronic inflammatory, psychiatric disorders and others. We have used genotyping microarrays to analyse the copy-number variants (CNVs) from 243 Caucasian individuals with Inflammatory Bowel Disease (IBD). The CNV data was obtained by using multiple quality control measures and merging the results of three different CNV detection algorithms: PennCNV, iPattern, and QuantiSNP. The final dataset contains 4,402 CNVs detected by two or three algorithms independently with high confidence. This paper provides a detailed description of the data generation and quality control steps. For further interpretation of the data presented in this article, please see the research article entitled ‘Copy number variation-based gene set analysis reveals cytokine signalling pathways associated with psychiatric comorbidity in patients with inflammatory bowel disease’.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".