Benchmarking Bayesian Deep Learning (BDL) for Important SNP Identification in Plant Genomes
Bibliographic record
Abstract
Genome-wide association studies (GWAS) detect genotype-phenotype links but often miss nonlinear effects and struggle with uncertainty in high-dimensional SNP data. We introduce the use of deep learning for SNP prioritization and rigorously evaluate a previously proposed Bayesian neural network (BNN) that had not been empirically tested. Our contributions are: (i) extensive testing of the BNN across multiple datasets spanning both binary and continuous phenotypes, (ii) biological verification of discoveries using Gene Ontology, and (iii) head-tohead benchmarking against standard GWAS tools and a convolutional neural network (CNN) used as the baseline deep learning model. Results show that the BNN's uncertainty quantification yields stable, interpretable SNP rankings, while the CNN captures local interaction patterns; together, they broaden candidate variant discovery beyond frequentist pipelines. Overall, deep learning especially probabilistic BNNs robustly enhances SNP discovery and interpretability for complex plant traits. For further details, the repository containing the implementation and code is available at: https://github.com/Raeein/Feature-Selection-Bayes-Deep-LearningFeature-Selection-Using-Bayes-Deep-Learning.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".