Description
IGI Global Handbook of Research on Emerging Trends and Applications of Machine Learning by Arun Solanki, Sandeep Kumar, Anand Nayyar
As today's world continues to advance, Artificial Intelligence (AI) is a field that has become a staple of technological development and led to the advancement of numerous professional industries. An application within AI that has gained attention is machine learning. Machine learning uses statistical techniques and algorithms to give computer systems the ability to understand and its popularity has circulated through many trades. Understanding this technology and its countless implementations is pivotal for scientists and researchers across the world.The Handbook of Research on Emerging Trends and Applications of Machine Learning provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, this book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science. While highlighting topics including image processing, predictive analytics, and smart grid management, this book is ideally designed for developers, data scientists, business analysts, information architects, finance agents, healthcare professionals, researchers, retail traders, professors, and graduate students seeking current research on the benefits, implementations, and trends of machine learning. Table of contents : - 1. R for Genome-Wide Association StudiesCedric Gondro, Laercio R. Porto-Neto, and Seung Hwan Lee2. Descriptive Statistics of Data: Understanding the Data Set and Phenotypes of InterestSonja Dominik3. Designing a Genome-Wide Association Studies (GWAS): Power, Sample Size, and Data StructureRoderick D. Ball4. Managing Large SNP Datasets with SNPpyFaheem Mitha5. Quality Control for Genome-Wide Association StudiesCedric Gondro, Seung Hwan Lee, Hak Kyo Lee, and Laercio R. Porto-Neto6. Overview of Statistical Methods for Genome-Wide Association Studies (GWAS)Ben Hayes7. Statistical Analysis of Genomic DataRoderick D. Ball8. Using PLINK for Genome-Wide Association Studies (GWAS) and Data AnalysisMiguel E. Renteria, Adrian Cortes, and Sarah E. Medland9. Genome-Wide Complex Trait Analysis (GCTA): Methods, Data Analyses, and InterpretationsJian Yang, Sang Hong Lee, Michael E. Goddard, and Peter M. Visscher10. Bayesian Methods Applied to Genome-Wide Association Studies (GWAS)Rohan L. Fernando and Dorian J. Garrick11. Implementing a QTL Detection Study (GWAS) Using Genomic Prediction MethodologyDorian J. Garrick and Rohan L. Fernando12. Genome-Enabled Prediction Using the BLR (Bayesian Linear Regression) R-PackageGustavo de los Campos, Paulino Perez, Ana I. Vazquez, and Jose Crossa13. Genomic Best Linear Unbiased Prediction (gBLUP) for the Estimation of Genomic Breeding ValuesSamuel A. Clark and Julius van der Werf14. Detecting Regions of Homozygosity to Map the Cause of Recessively Inherited DiseaseJames W. Kijas15. Use of Ancestral Haplotypes in Genome-Wide Association StudiesTom Druet and Frederic Farnir16. Genotype Phasing in Populations of Closely Related IndividualsJohn M. Hickey17. Genotype Imputation to Increase Sample Size in Pedigreed PopulationsJohn M. Hickey, Matthew A. Cleveland, Christian Maltecca, Gregor Gorjanc, Birgit Gredler, and Andreas Kranis18. Validation of Genome-Wide Association Studies (GWAS) ResultsJohn M. Henshall19. Detection of Signatures of Selection Using FSTLaercio R. Porto-Neto, Seung Hwan Lee, Hak Kyo Lee, and Cedric Gondro20. Association Weight Matrix: A Network-Based Approach Towards Functional Genome-Wide Association StudiesAntonio Reverter and Marina R.S. Fortes21. Mixed Effects Structural Equation Models and Phenotypic Causal NetworksBruno Dourado Valente and Guilherme Jordao de Magalhaes Rosa22. Epistasis, Complexity, and Multifactor Dimensionality ReductionQinxin Pan, Ting Hu, and Jason H. Moore23. Applications of Multifactor Dimensionality Reduction to Genome-Wide Data Using the R Package 'MDR'Stacey Winham24. Higher Order Interactions: Detection of Epistasis Using Machine Learning and Evolutionary ComputationRonald M. Nelson, Marcin Kierczak, and OErjan Carlborg25. Incorporating Prior Knowledge to Increase the Power of Genome-Wide Association StudiesAshley Petersen, Justin Spratt, and Nathan L. Tintle26. Genomic Selection in Animal Breeding ProgramsJulius van der Werf.