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

Research output: Contribution to journalArticle

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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