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- Publisher: Humana
- ISBN-10: 1071609467
- ISBN-13: 9781071609460
- Format: 19.6 x 25.9 x 2.5 cm, hardcover
- Language: English
- SAVE -10% with code: EXTRA
Epistasis (e-book) (used book) | bookbook.eu
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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 Wu14. 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 (τ-SGA) Technique for Complex Interaction Analysis
Elena Kuzmin, Brenda J. Andrews, and Charles Boone
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- Publisher: Humana
- ISBN-10: 1071609467
- ISBN-13: 9781071609460
- Format: 19.6 x 25.9 x 2.5 cm, hardcover
- Language: English English
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 Wu14. 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 (τ-SGA) Technique for Complex Interaction Analysis
Elena Kuzmin, Brenda J. Andrews, and Charles Boone
Reviews