Ebook: Big Data in Omics and Imaging: Association Analysis
Author: Momiao Xiong
- Tags: Biometry -- Data processing, Imaging systems in biology -- Statistical methods, Big data -- Statistical methods, Biometry -- methods, Genomics, Genetic Association Studies, Machine Learning
- Series: Chapman and Hall/CRC mathematical & computational biology series
- Year: 2018
- Publisher: CRC Press
- Language: English
- pdf
Big Data in Omics and Imaging: Association Analysisaddresses 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.
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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