Big Data in Omics and Imaging

Association Analysis

Nonfiction, Science & Nature, Science, Biological Sciences, Biotechnology, Mathematics, Statistics, Biology
Cover of the book Big Data in Omics and Imaging by Momiao Xiong, CRC Press
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Author: Momiao Xiong ISBN: 9781315353418
Publisher: CRC Press Publication: December 1, 2017
Imprint: Chapman and Hall/CRC Language: English
Author: Momiao Xiong
ISBN: 9781315353418
Publisher: CRC Press
Publication: December 1, 2017
Imprint: Chapman and Hall/CRC
Language: English

Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both population and family genomic data in sequencing era. It is unique in that it presents both hypothesis testing and a data mining approach to holistically dissecting the genetic structure of complex traits and to designing efficient strategies for precision medicine. The general frameworks for association analysis and machine learning, developed in the text, can be applied to genomic, epigenomic and imaging data.

FEATURES

Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data analysis and the modern advanced statistical methods for big data

Provides tools for high dimensional data reduction

Discusses searching algorithms for model and variable selection including randomization algorithms, Proximal methods and matrix subset selection

Provides real-world examples and case studies

Will have an accompanying website with R code

The book is designed for graduate students and researchers in genomics, bioinformatics, and data science. It represents the paradigm shift of genetic studies of complex diseases– from shallow to deep genomic analysis, from low-dimensional to high dimensional, multivariate to functional data analysis with next-generation sequencing (NGS) data, and from homogeneous populations to heterogeneous population and pedigree data analysis. Topics covered are: advanced matrix theory, convex optimization algorithms, generalized low rank models, functional data analysis techniques, deep learning principle and machine learning methods for modern association, interaction, pathway and network analysis of rare and common variants, biomarker identification, disease risk and drug response prediction.

 

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

Big Data in Omics and Imaging: Association Analysis addresses the recent development of association analysis and machine learning for both population and family genomic data in sequencing era. It is unique in that it presents both hypothesis testing and a data mining approach to holistically dissecting the genetic structure of complex traits and to designing efficient strategies for precision medicine. The general frameworks for association analysis and machine learning, developed in the text, can be applied to genomic, epigenomic and imaging data.

FEATURES

Bridges the gap between the traditional statistical methods and computational tools for small genetic and epigenetic data analysis and the modern advanced statistical methods for big data

Provides tools for high dimensional data reduction

Discusses searching algorithms for model and variable selection including randomization algorithms, Proximal methods and matrix subset selection

Provides real-world examples and case studies

Will have an accompanying website with R code

The book is designed for graduate students and researchers in genomics, bioinformatics, and data science. It represents the paradigm shift of genetic studies of complex diseases– from shallow to deep genomic analysis, from low-dimensional to high dimensional, multivariate to functional data analysis with next-generation sequencing (NGS) data, and from homogeneous populations to heterogeneous population and pedigree data analysis. Topics covered are: advanced matrix theory, convex optimization algorithms, generalized low rank models, functional data analysis techniques, deep learning principle and machine learning methods for modern association, interaction, pathway and network analysis of rare and common variants, biomarker identification, disease risk and drug response prediction.

 

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