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A General-Purpose Machine Learning R Library for Sparse Kernels Methods With an Application for Genome-Based Prediction

The adoption of machine learning frameworks in areas beyond computer science have been facilitated by the development of user-friendly software tools that do not require an advanced understanding of computer programming. In this paper, we present a new package (sparse kernel methods, SKM) software d...

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Autores principales: Montesinos López, Osval Antonio, Mosqueda González, Brandon Alejandro, Palafox González, Abel, Montesinos López, Abelardo, Crossa, José
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9205295/
https://www.ncbi.nlm.nih.gov/pubmed/35719365
http://dx.doi.org/10.3389/fgene.2022.887643
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author Montesinos López, Osval Antonio
Mosqueda González, Brandon Alejandro
Palafox González, Abel
Montesinos López, Abelardo
Crossa, José
author_facet Montesinos López, Osval Antonio
Mosqueda González, Brandon Alejandro
Palafox González, Abel
Montesinos López, Abelardo
Crossa, José
author_sort Montesinos López, Osval Antonio
collection PubMed
description The adoption of machine learning frameworks in areas beyond computer science have been facilitated by the development of user-friendly software tools that do not require an advanced understanding of computer programming. In this paper, we present a new package (sparse kernel methods, SKM) software developed in R language for implementing six (generalized boosted machines, generalized linear models, support vector machines, random forest, Bayesian regression models and deep neural networks) of the most popular supervised machine learning algorithms with the optional use of sparse kernels. The SKM focuses on user simplicity, as it does not try to include all the available machine learning algorithms, but rather the most important aspects of these six algorithms in an easy-to-understand format. Another relevant contribution of this package is a function for the computation of seven different kernels. These are Linear, Polynomial, Sigmoid, Gaussian, Exponential, Arc-Cosine 1 and Arc-Cosine L (with L = 2, 3, … ) and their sparse versions, which allow users to create kernel machines without modifying the statistical machine learning algorithm. It is important to point out that the main contribution of our package resides in the functionality for the computation of the sparse version of seven basic kernels, which is indispensable for reducing computational resources to implement kernel machine learning methods without a significant loss in prediction performance. Performance of the SKM is evaluated in a genome-based prediction framework using both a maize and wheat data set. As such, the use of this package is not restricted to genome prediction problems, and can be used in many different applications.
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spelling pubmed-92052952022-06-18 A General-Purpose Machine Learning R Library for Sparse Kernels Methods With an Application for Genome-Based Prediction Montesinos López, Osval Antonio Mosqueda González, Brandon Alejandro Palafox González, Abel Montesinos López, Abelardo Crossa, José Front Genet Genetics The adoption of machine learning frameworks in areas beyond computer science have been facilitated by the development of user-friendly software tools that do not require an advanced understanding of computer programming. In this paper, we present a new package (sparse kernel methods, SKM) software developed in R language for implementing six (generalized boosted machines, generalized linear models, support vector machines, random forest, Bayesian regression models and deep neural networks) of the most popular supervised machine learning algorithms with the optional use of sparse kernels. The SKM focuses on user simplicity, as it does not try to include all the available machine learning algorithms, but rather the most important aspects of these six algorithms in an easy-to-understand format. Another relevant contribution of this package is a function for the computation of seven different kernels. These are Linear, Polynomial, Sigmoid, Gaussian, Exponential, Arc-Cosine 1 and Arc-Cosine L (with L = 2, 3, … ) and their sparse versions, which allow users to create kernel machines without modifying the statistical machine learning algorithm. It is important to point out that the main contribution of our package resides in the functionality for the computation of the sparse version of seven basic kernels, which is indispensable for reducing computational resources to implement kernel machine learning methods without a significant loss in prediction performance. Performance of the SKM is evaluated in a genome-based prediction framework using both a maize and wheat data set. As such, the use of this package is not restricted to genome prediction problems, and can be used in many different applications. Frontiers Media S.A. 2022-06-03 /pmc/articles/PMC9205295/ /pubmed/35719365 http://dx.doi.org/10.3389/fgene.2022.887643 Text en Copyright © 2022 Montesinos López, Mosqueda González, Palafox González, Montesinos López and Crossa. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Montesinos López, Osval Antonio
Mosqueda González, Brandon Alejandro
Palafox González, Abel
Montesinos López, Abelardo
Crossa, José
A General-Purpose Machine Learning R Library for Sparse Kernels Methods With an Application for Genome-Based Prediction
title A General-Purpose Machine Learning R Library for Sparse Kernels Methods With an Application for Genome-Based Prediction
title_full A General-Purpose Machine Learning R Library for Sparse Kernels Methods With an Application for Genome-Based Prediction
title_fullStr A General-Purpose Machine Learning R Library for Sparse Kernels Methods With an Application for Genome-Based Prediction
title_full_unstemmed A General-Purpose Machine Learning R Library for Sparse Kernels Methods With an Application for Genome-Based Prediction
title_short A General-Purpose Machine Learning R Library for Sparse Kernels Methods With an Application for Genome-Based Prediction
title_sort general-purpose machine learning r library for sparse kernels methods with an application for genome-based prediction
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9205295/
https://www.ncbi.nlm.nih.gov/pubmed/35719365
http://dx.doi.org/10.3389/fgene.2022.887643
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