An alternative computing strategy for genomic prediction using a Bayesian mixture model
Bibliographic record
Abstract
Chen, L., Li, C. and Schenkel, F. 2015. An alternative computing strategy for genomic prediction using a Bayesian mixture model. Can. J. Anim. Sci. 95: 1-11. Bayesian methods for genomic prediction are commonly implemented via Markov chain Monte Carlo (MCMC) sampling schemes, which are computationally demanding in large-scale applications. An alternative computing algorithm, called right-hand side updating strategy (RHSU), was proposed by exploiting the sparsity feature of the marker effects in a Bayesian mixture model. The new algorithm was compared with the conventional Gauss-Seidel residual update (GSRU) algorithm by the number of floating point operations (FLOP) required in one round of MCMC sampling. The two algorithms were also compared in a Holstein data example with the training data size varying from 1000 to 10 000 and a marker density of 35 790 single nucleotide polymorphisms (SNP). Results showed that the proposed RHSU algorithm would outperform the traditional GSRU algorithm when the sample size exceeded a fraction of the number of the SNPs, which typically varied from 0.05 to 0.18 when the proportion of SNPs with no effect on the trait varied from 0.90 to 0.95. Results from the Holstein data example agreed very well with theoretical expectations. With adoption of a 50 k SNP panel and an increasing training data size, RHSU would be very useful if Bayesian methods are preferable for genomic prediction.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".