DSRIG: Incorporating graphical structure in the regularized modeling of SNP data
Bibliographic record
Abstract
Genetic selection of farm animals plays an important role in genetic improvement programs. Regularized regression methods on single nucleotide polymorphism (SNP) data from a set of candidate genes can help to identify genes that are associated with the trait of interest. This complex task must also consider the relative effect sizes on the desired trait and account for the relationships among the candidate SNPs so that selection of a SNP does not promote other undesirable traits through breeding. We present the Doubly Sparse Regression Incorporating Graphical structure (DSRIG), a novel regularized method for genetic selection that exploits the relationships among candidate SNPs to improve prediction. DSRIG was applied in the prediction of skatole and androstenone levels, two compounds known to be associated with boar taint. DSRIG was shown to provide a predictive benefit when compared to ordinary least squares (OLS) and the least absolute shrinkage and selection operator (LASSO) in a cross-validation procedure. The relative sizes of the coefficient estimates over the cross-validation procedure were compared to determine which SNPs may have the greatest impact on expression of the boar taint compounds and a consensus graph was used to infer the relationships among SNPs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".