Saliency-Aware Deep Residual Networks for Plant Phenotype Prediction
Bibliographic record
Abstract
Predicting complex phenotypes from genomic data is challenging in biology and machine learning. We propose a deep learning approach using a saliency-aware residual neural network (ResNet) for such prediction and applied to plant single-nucleotide polymorphism (SNP) data. Our model encodes genome-wide SNPs in a one-hot tensor and employs a 1D ResNet architecture with a custom inverse square root unit (ISRU) activation to stabilize regression outputs. We train the network with 10-fold cross-validation on both imputed and non-imputed (QA) genotype datasets. Experimental results on a soybean dataset show that our model achieves a mean Pearson correlation coefficient (PCC) between 0.39 and 0.64 for different soybean phenotypes. To interpret the model, we compute gradient-based saliency maps that highlight influential SNPs. The saliency analysis reveals a sparse set of top-ranking SNPs with outsized impact on the phenotype prediction, aligning with known genomic markers. We provide a fully containerized (Docker) pipeline for reproducibility. This work demonstrates that deep residual networks can yield accurate phenotype predictions while also identifying candidate genetic variants, bridging the gap between predictive accuracy and interpretability in genome-wide association studies. The model is available at https://github.com/ProductiveOwl/Soybean-CNN.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".