A Python API for estimating statistical high-order epistasis in genotype-phenotype maps.

A package for detecting epistasis by machine learning

Epistatic Net is an algorithm which allows for spectral regularization of deep neural networks to predict biological fitness functions (e.g., protein functions).

Code and Tutorials for Running the MArginal ePIstasis Test (MAPIT)

Code and simulations using interaction-LD score regression

NPDR: Nearest-neighbor Projected-Distance Regression with the generalized linear model

The multivariate MArginal ePIstasis Test

Jupyter notebooks for Genetics paper, "Detecting high-order epistasis in nonlinear genotype-phenotype maps"

A tool to predict missing data in sparsely sampled genotype-phenotype maps

Case-control genetics datasets evolved to be epistatic

Parallel Implementations of the Empirical Bayesian Elastic Net Cross-Validation in R

The W-Model, a tunable Black-Box Discrete Optimization Benchmarking (BB-DOB) problem, implemented for the BB-DOB@GECCO Workshop.

Inference of Epistatic Gene Networks

GWAS third-level epistatic search tool for cluster architectures

A Python tool to calculate penetrance tables for high-order epistasis models

A library for calculating penetrance tables of any bivariate epistasis model.

PAGER is an efficient genotype encoding strategy designed to improve the detection of non-additive genetic variation in complex trait association studies and epistasis investigation. PAGER dynamically encodes genetic variants or multi-locus genotypes (MLG) by normalizing mean phenotypic differences between genotype/MLG classes.

PAGER is an efficient genotype encoding strategy designed to improve the detection of non-additive genetic variation in complex trait association studies and epistasis investigation. PAGER dynamically encodes genetic variants or multi-locus genotypes (MLG) by normalizing mean phenotypic differences between genotype/MLG classes.

Non-linear Genetic Effects for Complex Traits