ModelBuildR: an R package for model building and feature selection with erroneous classifications.

ABSTRACT:

Background

Model building is a crucial part of omics based biomedical research to transfer classifications and obtain insights into underlying mechanisms. Feature selection is often based on minimizing error between model predictions and given classification (maximizing accuracy). Human ratings/classifications, however, might be error prone, with discordance rates between experts of 5-15%. We therefore evaluate if a feature pre-filtering step might improve identification of features associated with true underlying groups.

Methods

Data was simulated for up to 100 samples and up to 10,000 features, 10% of which were associated with the ground truth comprising 2-10 normally distributed populations. Binary and semi-quantitative ratings with varying error probabilities were used

SUBMITTER: Knoll M 

PROVIDER: S-EPMC7879945 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature