Understanding the Function of a Locus Using the Knowledge Available at Single-Nucleotide Polymorphisms
Bibliographic record
Abstract
Understanding the function of a locus is an issue in molecular biology. Although numerous molecular data have been generated in the last decades, it remains difficult to grasp how these data are related at a locus. In this study, we describe an analytical workflow that can solve this problem using the knowledge available at the single-nucleotide polymorphism (SNP) level. The underlying algorithm uses SNPs as connectors to link biological entities and identify correlations between them through a joint bioinformatics/statistics approach. We demonstrate its application in finding the mechanism whereby a mutation causes a phenotype and in revealing the path whereby a gene is regulated and impacts a phenotype. We translate our workflow into publicly available shell scripts. Our approach provides a basic framework to solve the information overload problem in biology surrounding the annotation of a locus and is a step toward repurposing GWAS data for new applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".