Replication Data for: A multivariate Poisson deep learning model for genomic prediction of count data
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Genomic selection (GS) is an important method used in plant and animal breeding. The experimental data provided in this study contain counting data. These datasets  were used to support research on efficient methodologies for multivariate count data outcomes including a multivariate Poisson deep neural network (MPDN) model, a conventional multivariate generalized Poisson regression model, and a univariate Poisson deep learning models. The results of the analyses are presented in a corresponding publication' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Agricultural research","Count data","Genomic prediction","Genomic selection","Multivariate Poisson deep neural network","Poisson regression models","Triticum aestivum","Wheat"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Genomic selection (GS) is an important method used in plant and animal breeding. The experimental data provided in this study contain counting data. These datasets  were used to support research on efficient methodologies for multivariate count data outcomes including a multivariate Poisson deep neural network (MPDN) model, a conventional multivariate generalized Poisson regression model, and a univariate Poisson deep learning models. The results of the analyses are presented in a corresponding publication' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Genomic selection (GS) is an important method used in plant and animal breeding. The experimental data provided in this study contain counting data. These datasets  were used to support research on efficient methodologies for multivariate count data outcomes including a multivariate Poisson deep neural network (MPDN) model, a conventional multivariate generalized Poisson regression model, and a univariate Poisson deep learning models. The results of the analyses are presented in a corresponding publication
Agricultural research; Count data; Genomic prediction; Genomic selection; Multivariate Poisson deep neural network; Poisson regression models; Triticum aestivum; Wheat