Predicting phenotypes from genetic, environment, management, and historical data using CNNs
| العنوان: | Predicting phenotypes from genetic, environment, management, and historical data using CNNs |
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| المؤلفون: | Patrick O’Briant, Emre Cimen, Timothy Reeves, Jacob D. Washburn, Graeme Hammer, Greg McLean, Edward S. Buckler, Guillaume P. Ramstein, Mark E. Cooper |
| المصدر: | Washburn, J D, Cimen, E, Ramstein, G, Reeves, T, O’Briant, P, McLean, G, Cooper, M, Hammer, G & Buckler, E S 2021, ' Predicting phenotypes from genetic, environment, management, and historical data using CNNs ', Theoretical and Applied Genetics, vol. 134, no. 12, pp. 3997-4011 . https://doi.org/10.1007/s00122-021-03943-7 |
| بيانات النشر: | Springer Science and Business Media LLC, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Feature engineering, Gene by environment, Computer science, business.industry, Yield (finance), General Medicine, Standard methods, Biology, Perceptron, Machine learning, computer.software_genre, Convolutional neural network, Prediction methods, Genetics, Deep neural networks, Survey data collection, Saliency map, Artificial intelligence, business, Agronomy and Crop Science, computer, Biotechnology, Slightly worse |
| الوصف: | Key Message: Convolutional Neural Networks (CNNs) can perform similarly or better than standard genomic prediction methods when sufficient genetic, environmental, and management data are provided. Abstract: Predicting phenotypes from genetic (G), environmental (E), and management (M) conditions is a long-standing challenge with implications to agriculture, medicine, and conservation. Most methods reduce the factors in a dataset (feature engineering) in a subjective and potentially oversimplified manner. Deep neural networks such as Multilayer Perceptrons (MPL) and Convolutional Neural Networks (CNN) can overcome this by allowing the data itself to determine which factors are most important. CNN models were developed for predicting agronomic yield from a combination of replicated trials and historical yield survey data. The results were more accurate than standard methods when tested on held-out G, E, and M data (r = 0.50 vs. r = 0.43), and performed slightly worse than standard methods when only G was held out (r = 0.74 vs. r = 0.80). Pre-training on historical data increased accuracy compared to trial data alone. Saliency map analysis indicated the CNN has “learned” to prioritize many factors of known agricultural importance. |
| تدمد: | 1432-2242 0040-5752 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::edbb83766b2d24e54b228d903b7e2be5 https://doi.org/10.1007/s00122-021-03943-7 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....edbb83766b2d24e54b228d903b7e2be5 |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 14322242 00405752 |
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