Epistasis

<p>Mass-based Protein Phylogenetic Approach to Identify Epistasis.-&nbsp;SNPInt-GPU: Tool for epistasis testing with multiple methods and GPU acceleration.-&nbsp;Epistasis-based Feature Selection Algorithm.-&nbsp;W-test for Genetic Epistasis Testing.-&nbsp;The Combined Analysis of Pleiotropy and Epistasis (CAPE).-&nbsp;Two-Stage Testing for Epistasis: Screening and Veri_cation.-&nbsp;Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Association Studies.-&nbsp;Phenotype Prediction under Epistasis.-&nbsp;Simulating Evolution in Asexual Populations with Epistasis.-&nbsp;Protocol for Construction of Genome-Wide Epistatic SNP Networks using WISH-R Package.-&nbsp;Brief survey on Machine Learning in Epistasis.-&nbsp;First-Order Correction of Statistical Significance&nbsp;for Screening Two-Way Epistatic Interactions.-&nbsp;Gene-Environment Interaction:&nbsp; AVariable Selection Perspective.-&nbsp;Using C-JAMP to Investigate Epistasis and Pleiotropy.-&nbsp;Identifying the Significant Change of Gene Expression in Genomic Series Data.-&nbsp;Analyzing High-Order Epistasis from Genotype-phenotype Maps Using ’Epistasis’ Package.-&nbsp;Deep Neural Networks for Epistatic Sequences Analysis.-&nbsp;Protocol for Epistasis Detection with Machine Learning Using GenEpi Package.-&nbsp;A Belief Degree Associated Fuzzy Multifactor Dimensionality Reduction Framework for Epistasis Detection.-&nbsp;Epistasis Detection Based on Epi-GTBN.-&nbsp;Epistasis Analysis: Classification through Machine Learning Methods.-&nbsp;Genetic Interaction Network Interpretation: A Tidy Data Science Perspective.-&nbsp;Trigenic Synthetic Genetic Array (τ-SGA) Technique for Complex Interaction Analysis.</p>

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