GxENet: Novel fully connected neural network based approaches to incorporate GxE for predicting wheat yield
Bibliographic record
Abstract
The expression of quantitative traits of a line of a crop depends on its genetics, the environment where it is sown and the interaction between the genetic information and the environment known as GxE. Thus to maximize food production, new varieties are developed by selecting superior lines of seeds suitable for a specific environment. Genomic selection is a computational technique for developing a new variety that uses whole genome molecular markers to identify top lines of a crop. A large number of statistical and machine learning models are employed for single environment trials, where it is assumed that the environment does not have any effect on the quantitative traits. However, it is essential to consider both genomic and environmental data to develop a new variety, as these strong assumptions may lead to failing to select top lines for an environment. Here we devised three novel deep learning frameworks incorporating GxE within the deep learning model and predicted line-specific yield for an environment. In the process, we also developed a new technique for identifying environment-specific markers that can be useful in many applications of environment-specific genomic selection. The result demonstrates that our best framework obtains 1.75 to 1.95 times better correlation coefficients than other deep learning models that incorporate environmental data depending on the test scenario. Furthermore, the feature importance analysis shows that environmental information, followed by genomic information, is the driving factor in predicting environment-specific yield for a line. We also demonstrate a way to extend our framework for new data types, such as text or soil data. The extended model also shows the potential to be useful in genomic selection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".