Machine Learning-Driven SNP Identification: Enhancing Genomic Selection in Beef Cattle
Bibliographic record
Abstract
Genomic selection has revolutionized livestock breeding by enabling precise genetic predictions based on Single Nucleotide Polymorphisms (SNPs). However, high-dimensional genomic datasets pose challenges such as multicollinearity and overfitting. This study explores the application of penalized regression techniques—LASSO, Ridge, and Elastic Net—to SNP data from chromosome 1 of beef cattle, aiming to identify key genetic markers associated with growth traits. Each method’s strengths and limitations were evaluated using cross-validation and performance metrics like Mean Squared Error (MSE) and Area Under the Curve (AUC). Results demonstrate that Elastic Net outperforms LASSO and Ridge by balancing variable selection and model stability, effectively managing correlated predictors, and achieving superior prediction accuracy. These findings underscore the potential of machine learning-driven genomic analysis to enhance breeding strategies, paving the way for efficient genetic improvements in livestock. Future work will expand the methodology to additional traits, chromosomes, and species to further refine genomic prediction models.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".