PBGB Seminar flyer
January 25, 2024 11:00AM - 12:00PM
Rm A-271 PSMS Bldg.
Thursday, January 25, 2024
Seminar: 11:00 - 12:00 pm
Luncheon: 12:00 pm- 1:00 pm
Rm A-271 PSMS Bldg.
Abstract
Phenotype prediction is a grand challenge of 21st century biology! Predictive models and frameworks touch nearly every area of modern research and are particularly critical in agriculture for assessing crop loss risks, developing climate smart and sustainability agricultural solutions, and informing breeding decisions. Within plant agriculture, the substantial influence of gene-by-environment effects and diverse growing conditions compound the challenge of prediction. Deep Learning offers a promising approach to phenotypic prediction as it allows for incorporation of large amounts of data and diverse data types into a single model. We will present recent findings in the application of deep learning for agriculture supporting both trait measurement and prediction as well as the limitations and promises of these techniques. The effects of different data types and qualities, “ensemble” methods, training methods, and the potential for biological insights from these models will be discussed. We demonstrate that deep learning models can, but do not always, outperform more traditional genomic prediction methods. We also show that ensembles of heterogenous models, including both deep learning and traditional statistical models can reduce error by approximately 7% relative to the best single model in a large multi-environment data set containing thousands of public maize hybrids. While not a panacea, this work suggests that deep learning is a promising tool to be used in conjunction with other methods to further phenotypic prediction in research and applied agriculture.
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