Statistical Analysis of High-Throughput Genetic Data Jiahua Chen (University of British Columbia),
Bibliographic record
Abstract
Recent years have seen the rapid accumulation of various types of genomic information due to concerted efforts by the scientific community and advances in molecular technologies. The publication of the human genome sequences and the sequences of many other species represent a great milestone in our scientific his-tory. In addition, a large number of genetic variants responsible for the diversity seen in a given organism have also been identified. For example, more than 10 million “common ” single nucleotide polymorphisms (SNPs) are estimated to be present in humans, and a many of these have been discovered and documented in the literature and public databases. Parallel to SNP discovery, microarrays have made it possible to examine gene expression levels at the genome level, to study genomic-wide DNA copy number changes, which are ubiquitous in human DNA, but especially in cancer tissue, to identify essentially all the binding targets of a transcription factor under different conditions, to evaluate epigenetic controls of genetic regulation, and to collect other types of information across the whole genome. All of these great successes in knowledge and data acquisition have created opportunities and challenges for statisticians, mathematicians, computer scientists, engineers, physicists, and other quantitative scientists to work closely with biologists and biomed-ical researchers. Mathematical scientists can assist biologists to utilize efficiently such enormous amounts of data, to identify genetic variants underlying human diseases, to dissect biological pathways and address
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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 | 0.009 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".