GPS: Harnessing data fusion strategies to improve the accuracy of machine learning-based genomic and phenotypic selection
Bibliographic record
Abstract
Genomic selection (GS) and phenotypic selection (PS) are widely used for accelerating plant breeding. However, the accuracy, robustness, and transferability of these two selection methods are underexplored, especially when addressing complex traits. In this study, we introduce a novel data fusion framework, GPS (genomic and phenotypic selection), designed to enhance predictive performance by integrating genomic and phenotypic data through three distinct fusion strategies: data fusion, feature fusion, and result fusion. The GPS framework was rigorously tested using an extensive suite of models, including statistical approaches (GBLUP and BayesB), machine learning models (Lasso, RF, SVM, XGBoost, and LightGBM), a deep learning method (DNNGP), and a recent phenotype-assisted prediction model (MAK). These models were applied to large datasets from four crop species, maize, soybean, rice, and wheat, demonstrating the versatility and robustness of the framework. Our results indicated that: (1) data fusion achieved the highest accuracy compared with the feature fusion and result fusion strategies. The top-performing data fusion model (Lasso_D) improved the selection accuracy by 53.4% compared to the best GS model (LightGBM) and by 18.7% compared to the best PS model (Lasso). (2) Lasso_D exhibited exceptional robustness, achieving high predictive accuracy even with a sample size as small as 200 and demonstrating resilience to single-nucleotide polymorphism (SNP) density variations, underscoring its adaptability to diverse data conditions. Moreover, the model's accuracy improved with the number of auxiliary traits and their correlation strength with target traits, further highlighting its adaptability to complex trait prediction. (3) Lasso_D demonstrated broad transferability, with substantial improvements in predictive accuracy when incorporating multi-environmental data. This enhancement resulted in only a 0.3% reduction in accuracy compared to predictions generated using data from the same environment, affirming the model's reliability in cross-environmental scenarios. This study provides groundbreaking insights, pushing the boundaries of predictive accuracy, robustness, and transferability in trait prediction. These findings represent a significant contribution to plant science, plant breeding, and the broader interdisciplinary fields of statistics and artificial intelligence.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".