Título
Prediction of multiple-trait and multiple-environment genomic data using recommender systems
Autor
Osval Antonio Montesinos-Lopez
Jose Crossa
David Mota-Sanchez
Ravi Gopal Singh
Suchismita Mondal
Philomin Juliana
Nivel de Acceso
Acceso Abierto
Materias
Genomics - (AGROVOC) Genotype environment interaction - (AGROVOC) Statistical methods - (AGROVOC) Precision agriculture - (AGROVOC) Genomic Information - (AGROVOC) Matrix Factorization - (AGROVOC) Prediction Accuracy - (AGROVOC) Collaborative Foltering - (AGROVOC) GenPred - (AGROVOC) Shared Data Resources - (AGROVOC) GENOMICS - (AGROVOC) GENOTYPE ENVIRONMENT INTERACTION - (AGROVOC) STATISTICAL METHODS - (AGROVOC) CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA - (CTI)
Resumen o descripción
In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: itembased collaborative filtering (IBCF) and the matrix factorization algorithm (MF) in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment–trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets.
Fecha de publicación
2018
Tipo de publicación
Artículo
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Investigadores
Repositorio Orígen
Repositorio Institucional de Publicaciones Multimedia del CIMMYT
Descargas
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