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Using Deep Learning to Predict Rice Traits from Genetic Variations
Jenn Hoskins
11th August, 2024
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Image Source: Natural Science News, 2024
Key Findings
- The study focused on rice, a crucial staple crop, and explored new methods to boost its yield
- Combining structural variants (SVs) with single nucleotide polymorphisms (SNPs) in genomic prediction models significantly improved prediction accuracy
- Deep Learning (DL) models generally outperformed traditional Bayesian models, especially for binary traits and when training and target sets were not closely related
References
Main Study
1) Evaluation of deep learning for predicting rice traits using structural and single-nucleotide genomic variants
Published 10th August, 2024
https://doi.org/10.1186/s13007-024-01250-y
Related Studies
2) Integrated genomic selection for rapid improvement of crops.
3) Prediction of total genetic value using genome-wide dense marker maps.
Journal: Genetics, Issue: Vol 157, Issue 4, Apr 2001
4) Boosting Genetic Gain in Allogamous Crops via Speed Breeding and Genomic Selection.
5) Strategies Using Genomic Selection to Increase Genetic Gain in Breeding Programs for Wheat.