Inference of Epistatic Gene Networks
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Updated
Aug 25, 2014 - Python
Inference of Epistatic Gene Networks
All materials and analysis as Jupyter notebooks for Genetics paper.
Jupyter notebooks for Genetics paper, "Detecting high-order epistasis in nonlinear genotype-phenotype maps"
Case-control genetics datasets evolved to be epistatic
GWAS third-level epistatic search tool for cluster architectures
A library for calculating penetrance tables of any bivariate epistasis model.
A package for detecting epistasis by machine learning
The W-Model, a tunable Black-Box Discrete Optimization Benchmarking (BB-DOB) problem, implemented for the BB-DOB@GECCO Workshop.
Epistatic Net is an algorithm which allows for spectral regularization of deep neural networks to predict biological fitness functions (e.g., protein functions).
A Python tool to calculate penetrance tables for high-order epistasis models
Approach to VARIantVARIant interaction through VARIable thresholds and hypothesis testing. VARI3 automates the selection and analysis of the most promising SNPs to identify epistasis.
Parallel Implementations of the Empirical Bayesian Elastic Net Cross-Validation in R
Code and Tutorials for Running the MArginal ePIstasis Test (MAPIT)
Non-linear Genetic Effects for Complex Traits
NPDR: Nearest-neighbor Projected-Distance Regression with the generalized linear model
Estimating epistasis in trees for genetic programming problems
A Python API for estimating statistical high-order epistasis in genotype-phenotype maps.
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