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

The revival of the Gini importance?

(2018) Stefano Nembrini et al.   BIOINFORMATICS

Genome-wide two-locus interaction analysis identifies multiple epistatic SNP pairs that confer risk of prostate cancer: A cross-population study

(2017) Jiawei Shen et al.   INTERNATIONAL JOURNAL OF CANCER

PMLB: a large benchmark suite for machine learning evaluation and comparison

(2017) Randal S. Olson et al.   BioData Mining

Do little interactions get lost in dark random forests?

(2016) Marvin N. Wright et al.   BMC BIOINFORMATICS

Discovery of gene-gene interactions across multiple independent data sets of late onset Alzheimer disease from the Alzheimer Disease Genetics Consortium

(2016) Timothy J. Hohman et al.   NEUROBIOLOGY OF AGING

Detecting gene-gene interactions using a permutation-based random forest method

(2016) Jing Li et al.   BioData Mining

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation

(2015) Alex Goldstein et al.   JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS

A random forest approach to capture genetic effects in the presence of population structure

(2015) Johannes Stephan et al.   Nature Communications

A survey about methods dedicated to epistasis detection

(2015) Clément Niel et al.   Frontiers in Genetics

Why epistasis is important for tackling complex human disease genetics

(2014) Trudy FC Mackay et al.   Genome Medicine

A genome-wide gene–gene interaction analysis identifies an epistatic gene pair for lung cancer susceptibility in Han Chinese

(2013) Minjie Chu et al.   CARCINOGENESIS

ViSEN: Methodology and Software for Visualization of Statistical Epistasis Networks

(2013) Ting Hu et al.   GENETIC EPIDEMIOLOGY

An information-gain approach to detecting three-way epistatic interactions in genetic association studies

(2013) Ting Hu et al.   JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION

An Exhaustive Epistatic SNP Association Analysis on Expanded Wellcome Trust Data

(2013) Christoph Lippert et al.   Scientific Reports

A Review for Detecting Gene-Gene Interactions Using Machine Learning Methods in Genetic Epidemiology

(2013) Ching Lee Koo et al.   Biomed Research International

Random Forests for Genetic Association Studies

(2011) Benjamin A Goldstein et al.   Statistical Applications in Genetics and Molecular Biology

Genome-Wide Association Scan Allowing for Epistasis in Type 2 Diabetes

(2010) Jordana T. Bell et al.   ANNALS OF HUMAN GENETICS

Bioinformatics challenges for genome-wide association studies

(2010) J. H. Moore et al.   BIOINFORMATICS

Permutation importance: a corrected feature importance measure

(2010) André Altmann et al.   BIOINFORMATICS

Feasible and Successful: Genome-Wide Interaction Analysis Involving All 1.9 × 1011 Pair-Wise Interaction Tests

(2010) Michael Steffens et al.   HUMAN HEREDITY

A random forest approach to the detection of epistatic interactions in case-control studies

(2009) Rui Jiang et al.   BMC BIOINFORMATICS

Genome-wide association reveals three SNPs associated with sporadic amyotrophic lateral sclerosis through a two-locus analysis

(2009) Qiuying Sha et al.   BMC Medical Genetics

Epistasis — the essential role of gene interactions in the structure and evolution of genetic systems

(2008) Patrick C. Phillips   NATURE REVIEWS GENETICS