Epistasis

TRAMA

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.

SOMMARIO

1. Mass-based Protein Phylogenetic Approach to Identify Epistasis Kevin M. Downard   2. SNPInt-GPU: Tool for epistasis testing with multiple methods and GPU acceleration Lars Wienbrandt, Jan Christian Kässens, and David Ellinghaus   3. Epistasis-based Feature Selection Algorithm Lauro Cássio Martins de Paula   4. W-test for Genetic Epistasis Testing Rui Sun, Haoyi Weng, and Maggie Haitian Wang   5. The Combined Analysis of Pleiotropy and Epistasis (CAPE) Anna L. Tyler, Jake Emerson, Baha El Kassaby, Ann E. Wells, Vivek M. Philip, and Gregory W. Carter   6. Two-Stage Testing for Epistasis: Screening and Veri_cation Jakub Pecanka and Marianne A. Jonker   7. Using Collaborative Mixed Models to Account for Imputation Uncertainty in Transcriptome-Wide Association Studies Xingjie Shi, Can Yang, and Jin Liu   8. Phenotype Prediction under Epistasis Elaheh Vojgani, Torsten Pook, and Henner Simianer   9. Simulating Evolution in Asexual Populations with Epistasis Ramon Diaz-Uriarte   10. Protocol for Construction of Genome-Wide Epistatic SNP Networks using WISH-R Package Haja N. Kadarmideen and Victor AO. Carmelo   11. Brief survey on Machine Learning in Epistasis Davide Chicco and Trent Faultless   12. First-Order Correction of Statistical Significance for Screening Two-Way Epistatic Interactions Lu Cheng and Mu Zhu   13. Gene-Environment Interaction:  AVariable Selection Perspective Fei Zhou, Jie Ren, Xi Lu, Shuangge Ma, and Cen Wu 14. Using C-JAMP to Investigate Epistasis and Pleiotropy Stefan Konigorski and Benjamin S. Glicksberg   15. Identifying the Significant Change of Gene Expression in Genomic Series Data Hiu-Hin Tam   16. Analyzing High-Order Epistasis from Genotype-phenotype Maps Using ’Epistasis’ Package Junyi Chen and Ka-Chun Wong   17. Deep Neural Networks for Epistatic Sequences Analysis Jiecong Lin   18. Protocol for Epistasis Detection with Machine Learning Using GenEpi Package Olutomilayo Olayemi Petinrin,and Ka-Chun Wong   19. A Belief Degree Associated Fuzzy Multifactor Dimensionality Reduction Framework for Epistasis Detection Saifur Rahaman and Ka-Chun Wong   20. Epistasis Detection Based on Epi-GTBN Xingjian Chen and Ka-Chun Wong   21. Epistasis Analysis: Classification through Machine Learning Methods Linjing Liu and Ka-Chun Wong   22. Genetic Interaction Network Interpretation: A Tidy Data Science Perspective Lulu Jiang and Hai Fang   23. Trigenic Synthetic Genetic Array (t-SGA) Technique for Complex Interaction Analysis Elena Kuzmin, Brenda J. Andrews, and Charles Boone

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9781071609460
• Collana: Methods in Molecular Biology
• Dimensioni: 254 x 178 mm Ø 969 gr
• Formato: Copertina rigida
• Illustration Notes: X, 402 p. 167 illus., 85 illus. in color.
• Pagine Arabe: 402
• Pagine Romane: x