Resumen
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.
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.
| Idioma original | English |
|---|---|
| Páginas (desde-hasta) | 1-11 |
| Número de páginas | 11 |
| Publicación | bioRxiv |
| DOI | |
| Estado | Published - 13 ago 2019 |
Huella Profundice en los temas de investigación de 'Benchmarking machine learning models for the analysis of genetic data using FRESA.CAD Binary Classification Benchmarking'. En conjunto forman una huella única.
Citar esto
Martínez Torteya, A., De Velasco Oriol, J., Trevino, V., Tamez-Peña, J. G., Alanís, I., & Vallejo, E. E. (2019). Benchmarking machine learning models for the analysis of genetic data using FRESA.CAD Binary Classification Benchmarking. bioRxiv, 1-11. https://doi.org/10.1101/733675