Normal view MARC view ISBD view

Prediction of multiple-trait and multiple-environment genomic data using recommender systems [Electronic Resource]

By: Montesinos-Lopez, O.A.
Contributor(s): Montesinos-Lopez, A | Crossa, J | Montesinos-Lopez, J.C | Mota-Sanchez, D | Estrada-González, F | Gillberg, J | Singh, R.P | Mondal, S | Juliana, P.
Material type: materialTypeLabelArticlePublisher: Bethesda, MD : Genetis Society of America, 2018Subject(s): Genomics | Genotype environment interaction | Statistical methods | Precision agricultureOnline resources: Open Access through Dspace In: G3 v. 8, no. 1, p. 131-147Summary: 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.
Tags from this library: No tags from this library for this title. Log in to add tags.
    average rating: 0.0 (0 votes)
Item type Current location Collection Call number Status Date due Barcode Item holds
Article CIMMYT Knowledge Center: John Woolston Library

Lic. Jose Juan Caballero Flores

 

CIMMYT Staff Publications Collection Available
Total holds: 0

Peer review

Open Access

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.

Wheat CRP FP2 - Novel diversity and tools adapt to climate change and resource constraints

Text in English

There are no comments for this item.

Log in to your account to post a comment.

Click on an image to view it in the image viewer

baner

International Maize and Wheat Improvement Center (CIMMYT) © Copyright 2015. Carretera México-Veracruz. Km. 45, El Batán, Texcoco, México, C.P. 56237.
Monday –Friday 9:00 am. 17:00 pm. If you have any question, please contact us at CIMMYT-Knowledge-Center@cgiar.org

Centro Internacional de Mejoramiento de Maíz y Trigo (CIMMYT) © Copyright 2015. Carretera México-Veracruz. Km. 45, El Batán, Texcoco, México, C.P. 56237.
Lunes –Viernes 9:00 am. 17:00 pm. Si tiene cualquier pregunta, contáctenos a CIMMYT-Knowledge-Center@cgiar.org