FastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies.

ABSTRACT:

Background

Parametric feature selection methods for machine learning and association studies based on genetic data are not robust with respect to outliers or influential observations. While rank-based, distribution-free statistics offer a robust alternative to parametric methods, their practical utility can be limited, as they demand significant computational resources when analyzing high-dimensional data. For genetic studies that seek to identify variants, the hypothesis is constrained, since it is typically assumed that the effect of the genotype on the phenotype is monotone (e.g., an additive genetic effect). Similarly, predictors for machine learning applications may have natural ordering constraints. Cross-validation for feature selection in these high-dimensional contexts nec

SUBMITTER: Lin J 

PROVIDER: S-EPMC6567636 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature