Abstract Data from high‐throughput phenotyping (HTP) could be used for phenotype imputation to enhance genomic selection (GS) or gene discovery, but this has not been explored in crop species. Three machine learning models: multiple linear regression (MLR), missForest, and k‐nearest neighbors, were evaluated for grain yield (GY) phenotype imputation in
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An effective BiLSTM-CNN model for predicting large-scale temporal-spatial dynamics of normalized difference vegetation index. [PDF]
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Neighborhood greenspace and brain imaging outcomes in older adults without dementia from three US Alzheimer's Disease Research Centers. [PDF]
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