A comparison of methods for interpreting random forest models of genetic association in the presence of non-additive interactions.

ABSTRACT:

Background

Non-additive interactions among genes are frequently associated with a number of phenotypes, including known complex diseases such as Alzheimer's, diabetes, and cardiovascular disease. Detecting interactions requires careful selection of analytical methods, and some machine learning algorithms are unable or underpowered to detect or model feature interactions that exhibit non-additivity. The Random Forest method is often employed in these efforts due to its ability to detect and model non-additive interactions. In addition, Random Forest has the built-in ability to estimate feature importance scores, a characteristic that allows the model to be interpreted with the order and effect size of the feature association with the outcome. This characteristic is very important fo

SUBMITTER: Orlenko A 

PROVIDER: S-EPMC7847145 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature