Epistasis Methods and Protocols
Epistasis Methods and Protocols
edited by Ka-Chun Wong.
- 1st edition 2021
- 1 recurso en línea (X, 402 páginas) 167 ilustraciones, 85 ilustraciones a color
- Methods in Molecular Biology 2212 1940-6029 .
Mass-based Protein Phylogenetic Approach to Identify Epistasis -- SNPInt-GPU: Tool for epistasis testing with multiple methods and GPU acceleration -- Epistasis-based Feature Selection Algorithm -- W-test for Genetic Epistasis Testing -- The Combined Analysis of Pleiotropy and Epistasis (CAPE) -- Two-Stage Testing for Epistasis: Screening and Veri_cation -- Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Association Studies -- Phenotype Prediction under Epistasis -- Simulating Evolution in Asexual Populations with Epistasis -- Protocol for Construction of Genome-Wide Epistatic SNP Networks using WISH-R Package -- Brief survey on Machine Learning in Epistasis -- First-Order Correction of Statistical Significance for Screening Two-Way Epistatic Interactions -- Gene-Environment Interaction: AVariable Selection Perspective -- Using C-JAMP to Investigate Epistasis and Pleiotropy -- Identifying the Significant Change of Gene Expression in Genomic Series Data -- Analyzing High-Order Epistasis from Genotype-phenotype Maps Using 'Epistasis' Package -- Deep Neural Networks for Epistatic Sequences Analysis -- Protocol for Epistasis Detection with Machine Learning Using GenEpi Package -- A Belief Degree Associated Fuzzy Multifactor Dimensionality Reduction Framework for Epistasis Detection -- Epistasis Detection Based on Epi-GTBN -- Epistasis Analysis: Classification through Machine Learning Methods -- Genetic Interaction Network Interpretation: A Tidy Data Science Perspective -- Trigenic Synthetic Genetic Array (τ-SGA) Technique for Complex Interaction Analysis.
This volume explores methods and protocols for detecting epistasis from genetic data. Chapters provide methods and protocols demonstrating approaches to identify epistasis, genetic epistasis testing, genome-wide epistatic SNP networks, epistasis detection through machine learning, and complex interaction analysis using trigenic synthetic genetic array (τ-SGA). Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, application details for both the expert and non-expert reader, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Epistasis: Methods and Protocols aims to ensure successful results in the further study of this vital field. .
9781071609477
10.1007/978-1-0716-0947-7 doi
Mass-based Protein Phylogenetic Approach to Identify Epistasis -- SNPInt-GPU: Tool for epistasis testing with multiple methods and GPU acceleration -- Epistasis-based Feature Selection Algorithm -- W-test for Genetic Epistasis Testing -- The Combined Analysis of Pleiotropy and Epistasis (CAPE) -- Two-Stage Testing for Epistasis: Screening and Veri_cation -- Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Association Studies -- Phenotype Prediction under Epistasis -- Simulating Evolution in Asexual Populations with Epistasis -- Protocol for Construction of Genome-Wide Epistatic SNP Networks using WISH-R Package -- Brief survey on Machine Learning in Epistasis -- First-Order Correction of Statistical Significance for Screening Two-Way Epistatic Interactions -- Gene-Environment Interaction: AVariable Selection Perspective -- Using C-JAMP to Investigate Epistasis and Pleiotropy -- Identifying the Significant Change of Gene Expression in Genomic Series Data -- Analyzing High-Order Epistasis from Genotype-phenotype Maps Using 'Epistasis' Package -- Deep Neural Networks for Epistatic Sequences Analysis -- Protocol for Epistasis Detection with Machine Learning Using GenEpi Package -- A Belief Degree Associated Fuzzy Multifactor Dimensionality Reduction Framework for Epistasis Detection -- Epistasis Detection Based on Epi-GTBN -- Epistasis Analysis: Classification through Machine Learning Methods -- Genetic Interaction Network Interpretation: A Tidy Data Science Perspective -- Trigenic Synthetic Genetic Array (τ-SGA) Technique for Complex Interaction Analysis.
This volume explores methods and protocols for detecting epistasis from genetic data. Chapters provide methods and protocols demonstrating approaches to identify epistasis, genetic epistasis testing, genome-wide epistatic SNP networks, epistasis detection through machine learning, and complex interaction analysis using trigenic synthetic genetic array (τ-SGA). Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, application details for both the expert and non-expert reader, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Epistasis: Methods and Protocols aims to ensure successful results in the further study of this vital field. .
9781071609477
10.1007/978-1-0716-0947-7 doi