Genome-wide association study (GWAS) entails examining a large number of single
nucleotide polymorphisms (SNPs) in a limited sample with hundreds of individuals, implying …

Although nonparametric methods in genome-wide association studies (GWAS) are robust in
quantitative trait nucleotide (QTN) detection, the absence of polygenic background control in …

For the risk, progression, and response to treatment of many complex diseases, it has been
increasingly recognized that genetic interactions (including gene–gene and gene …

Motivation Single-nucleotide polymorphism (SNP)–SNP interactions (SSIs) are popular
markers for understanding disease susceptibility. Multifactor dimensionality reduction (MDR) …

Motivation Gene–gene interaction (GGI) is one of the most popular approaches for finding
and explaining the missing heritability of common complex traits in genome-wide …

Background Gene-gene interaction (GGI) is one of the most popular approaches for finding
the missing heritability of common complex traits in genetic association studies. The …

Aim To analyse the risk factors contributing to the prevalence of periodontitis among clusters
of patients with diabetes and to examine the clustering patterns of clinical blood biochemical …

Data visualization is critical to unraveling hidden information from complex and high-
dimensional data. Interpretable visualization methods are critical, especially in the biology …

Abstract Gene–environment (G–E) interaction analysis has been extensively conducted for
complex diseases. In marginal analysis, the common practice is to conduct likelihood‐based …

Gene-gene interactions (GGIs) are important markers for determining susceptibility to a
disease. Multifactor dimensionality reduction (MDR) is a popular algorithm for detecting …