Epistasis : Methods and Protocols / edited by Ka-Chun Wong.

Format:

Book

Contributor:

Wong, Ka-Chun, Editor.

SpringerLink (Online service)

Series:

Springer Protocols (Springer-12345)

Methods in molecular biology 1940-6029 ; 2212

Methods in Molecular Biology, 1940-6029 ; 2212

Language:

English

Subjects (All):

Medical genetics.

Medical Genetics.

Local Subjects:

Medical Genetics.

Physical Description:

1 online resource (X, 402 pages) : 167 illustrations, 85 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

New York, NY : Springer US : Imprint: Humana, 2021.

System Details:

text file PDF

Summary:

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. .

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-1-0716-0947-7

9781071609477

Access Restriction:

Restricted for use by site license.