Background Non-additive interactions among genes are frequently associated with a
number of phenotypes, including known complex diseases such as Alzheimer's, diabetes …

The Random Forests (RF) algorithm has become a commonly used machine learning
algorithm for genetic association studies. It is well suited for genetic applications since it is …

Motivation: We developed an EM-random forest (EMRF) for Haseman–Elston quantitative
trait linkage analysis that accounts for marker ambiguity and weighs each sib-pair according …

Background Our aim is to investigate the ability of neural networks to model different two-
locus disease models. We conduct a simulation study to compare neural networks with two …

Background Multiple genetic factors and their interactive effects are speculated to contribute
to complex diseases. Detecting such genetic interactive effects, ie, epistatic interactions …

Motivation: High-dimensional data are frequently generated in genome-wide association
studies (GWAS) and other studies. It is important to identify features such as single …

Background Random forests (RF) have been increasingly used in applications such as
genome-wide association and microarray studies where predictor correlation is frequently …

The explosion of genetic information over the last decade presents an analytical challenge
for genetic association studies. As the number of genetic variables examined per individual …

Background: Genetic association studies, thus far, have focused on the analysis of individual
main effects of SNP markers. Nonetheless, there is a clear need for modeling epistasis or …

Genome-wide association studies (GWAS) have become a powerful and affordable tool to
study the genetic variations associated with common human diseases. However, only few of …