Wong Epistasis

Methods and Protocols
Erscheinungsjahr 2021
ISBN: 978-1-0716-0947-7
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 2212, 402 Seiten, eBook

Reihe: Methods in Molecular Biology

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 (t-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.   "Simulating Evolution in Asexual Populations with Epistasis” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Wong, Ka-Chun

Inhaltsverzeichnis

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 (t-SGA) Technique for Complex Interaction Analysis.