Recent advances in neuroimaging technology and molecular genetics provide the unique
opportunity to investigate genetic influence on the variation of brain attributes. Since the year …

Complex diseases are defined to be determined by multiple genetic and environmental
factors alone as well as in interactions. To analyze interactions in genetic data, many …

In this article we introduce modern statistical machine learning and bioinformatics
approaches that have been used in learning statistical relationships from big data in …

Abstract The Chinese Imaging Genetics (CHIMGEN) study establishes the largest Chinese
neuroimaging genetics cohort and aims to identify genetic and environmental factors and …

We present an extension of the two-class multifactor dimensionality reduction (MDR)
algorithm that enables detection and characterization of epistatic SNP-SNP interactions in …

Because of the complexity of gene–phenotype relationships machine learning approaches
have considerable appeal as a strategy for modelling interactions. A number of such …

Identified genetic variants from genome wide association studies frequently show only
modest effects on the disease risk, leading to the “missing heritability” problem. An avenue …

The goal of this unit is to introduce gene‐gene interactions (epistasis) as a significant
complicating factor in the search for disease susceptibility genes. This unit begins with an …

In this era of genome-wide association studies (GWAS), the quest for understanding the
genetic architecture of complex diseases is rapidly increasing more than ever before. The …

This volume is an attempt to provide a graduate level introduction to various aspects of
stochastic geometry, spatial statistics and random fields, with special emphasis placed on …