Benchmarking machine learning models for the analysis of genetic data using FRESA.CAD Binary Classification Benchmarking

Antonio Martínez Torteya, Javier De Velasco Oriol, Victor Trevino, Jose G. Tamez-Peña, Israel Alanís, Edgar E. Vallejo

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Abstract

Background Machine learning models have proven to be useful tools for the analysis of genetic data. However, with the availability of a wide variety of such methods, model selection has become increasingly difficult, both from the human and computational perspective.

Results We present the R package FRESA.CAD Binary Classification Benchmarking that performs systematic comparisons between a collection of representative machine learning methods for solving binary classification problems on genetic datasets.

Conclusions FRESA.CAD Binary Benchmarking demonstrates to be a useful tool over a variety of binary classification problems comprising the analysis of genetic data showing both quantitative and qualitative advantages over similar packages.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalbioRxiv
DOIs
Publication statusPublished - 13 Aug 2019

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Martínez Torteya, Antonio ; De Velasco Oriol, Javier ; Trevino, Victor ; Tamez-Peña, Jose G. ; Alanís, Israel ; Vallejo, Edgar E. / Benchmarking machine learning models for the analysis of genetic data using FRESA.CAD Binary Classification Benchmarking. In: bioRxiv. 2019 ; pp. 1-11.
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Benchmarking machine learning models for the analysis of genetic data using FRESA.CAD Binary Classification Benchmarking. / Martínez Torteya, Antonio; De Velasco Oriol, Javier; Trevino, Victor; Tamez-Peña, Jose G.; Alanís, Israel; Vallejo, Edgar E.

In: bioRxiv, 13.08.2019, p. 1-11.

Research output: Contribution to journalArticle

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AU - Martínez Torteya, Antonio

AU - De Velasco Oriol, Javier

AU - Trevino, Victor

AU - Tamez-Peña, Jose G.

AU - Alanís, Israel

AU - Vallejo, Edgar E.

PY - 2019/8/13

Y1 - 2019/8/13

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AB - Background Machine learning models have proven to be useful tools for the analysis of genetic data. However, with the availability of a wide variety of such methods, model selection has become increasingly difficult, both from the human and computational perspective.Results We present the R package FRESA.CAD Binary Classification Benchmarking that performs systematic comparisons between a collection of representative machine learning methods for solving binary classification problems on genetic datasets.Conclusions FRESA.CAD Binary Benchmarking demonstrates to be a useful tool over a variety of binary classification problems comprising the analysis of genetic data showing both quantitative and qualitative advantages over similar packages.

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