Genome-Enabled Prediction Methods Based on Machine Learning

Genome-Enabled Prediction Methods Based on Machine Learning

189–218 pp

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Bibliographic Details
Main Authors: Reinoso-Peláez, Edgar L, Gianola, Daniel, González-Recio, Oscar
Other Authors: Reinoso-Peláez, Edgar L [0000-0002-5918-5447]
Format: capítulo de libro biblioteca
Language:English
Published: Springer 2022-04-22
Subjects:Bayesian methods, Complex traits, Ensemble methods, GWP, Meta-analysis, Machine learning, Neural networks,
Online Access:http://hdl.handle.net/10261/274558
https://api.elsevier.com/content/abstract/scopus_id/85129215899
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spelling dig-inia-es-10261-2745582022-07-16T03:05:49Z Genome-Enabled Prediction Methods Based on Machine Learning Methods and protocols Reinoso-Peláez, Edgar L Gianola, Daniel González-Recio, Oscar Reinoso-Peláez, Edgar L [0000-0002-5918-5447] Gianola, Daniel [0000-0001-8217-2348] González-Recio, Oscar [0000-0002-9106-4063] Bayesian methods Complex traits Ensemble methods GWP Meta-analysis Machine learning Neural networks 189–218 pp Growth of artificial intelligence and machine learning (ML) methodology has been explosive in recent years. In this class of procedures, computers get knowledge from sets of experiences and provide forecasts or classification. In genome-wide based prediction (GWP), many ML studies have been carried out. This chapter provides a description of main semiparametric and nonparametric algorithms used in GWP in animals and plants. Thirty-four ML comparative studies conducted in the last decade were used to develop a meta-analysis through a Thurstonian model, to evaluate algorithms with the best predictive qualities. It was found that some kernel, Bayesian, and ensemble methods displayed greater robustness and predictive ability. However, the type of study and data distribution must be considered in order to choose the most appropriate model for a given problem. Peer reviewed 2022-07-01T09:30:52Z 2022-07-01T09:30:52Z 2022-04-22 capítulo de libro Genome-Enabled Prediction Methods Based on Machine Learning 978-1-0716-2205-6 (eBook) 978-1-0716-2204-9 1064-3745 http://hdl.handle.net/10261/274558 10.1007/978-1-0716-2205-6_7 1940-6029 35451777 2-s2.0-85129215899 https://api.elsevier.com/content/abstract/scopus_id/85129215899 en Methods in molecular biology (Clifton, N.J.) Methods in Molecular Biology 2467 Postprint https://doi.org/10.1007/978-1-0716-2205-6 Sí open Springer
institution INIA ES
collection DSpace
country España
countrycode ES
component Bibliográfico
access En linea
databasecode dig-inia-es
tag biblioteca
region Europa del Sur
libraryname Biblioteca del INIA España
language English
topic Bayesian methods
Complex traits
Ensemble methods
GWP
Meta-analysis
Machine learning
Neural networks
Bayesian methods
Complex traits
Ensemble methods
GWP
Meta-analysis
Machine learning
Neural networks
spellingShingle Bayesian methods
Complex traits
Ensemble methods
GWP
Meta-analysis
Machine learning
Neural networks
Bayesian methods
Complex traits
Ensemble methods
GWP
Meta-analysis
Machine learning
Neural networks
Reinoso-Peláez, Edgar L
Gianola, Daniel
González-Recio, Oscar
Genome-Enabled Prediction Methods Based on Machine Learning
description 189–218 pp
author2 Reinoso-Peláez, Edgar L [0000-0002-5918-5447]
author_facet Reinoso-Peláez, Edgar L [0000-0002-5918-5447]
Reinoso-Peláez, Edgar L
Gianola, Daniel
González-Recio, Oscar
format capítulo de libro
topic_facet Bayesian methods
Complex traits
Ensemble methods
GWP
Meta-analysis
Machine learning
Neural networks
author Reinoso-Peláez, Edgar L
Gianola, Daniel
González-Recio, Oscar
author_sort Reinoso-Peláez, Edgar L
title Genome-Enabled Prediction Methods Based on Machine Learning
title_short Genome-Enabled Prediction Methods Based on Machine Learning
title_full Genome-Enabled Prediction Methods Based on Machine Learning
title_fullStr Genome-Enabled Prediction Methods Based on Machine Learning
title_full_unstemmed Genome-Enabled Prediction Methods Based on Machine Learning
title_sort genome-enabled prediction methods based on machine learning
publisher Springer
publishDate 2022-04-22
url http://hdl.handle.net/10261/274558
https://api.elsevier.com/content/abstract/scopus_id/85129215899
work_keys_str_mv AT reinosopelaezedgarl genomeenabledpredictionmethodsbasedonmachinelearning
AT gianoladaniel genomeenabledpredictionmethodsbasedonmachinelearning
AT gonzalezreciooscar genomeenabledpredictionmethodsbasedonmachinelearning
AT reinosopelaezedgarl methodsandprotocols
AT gianoladaniel methodsandprotocols
AT gonzalezreciooscar methodsandprotocols
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