Big Data in Omics and Imaging_Association Analysis-CRC (2018).pdf下载

The next generation of genomic, sensing, and imaging technologies has generated a deluge of DNA sequencing, transcriptomes, epigenomic, metabolic, physiological (ECG, EEG, EMG, and MEG), image (CT, MRI, fMRI, DTI, PET), behavioral, and clini- cal data with multiple phenotypes and millions of features. Analysis of increasingly larger, deeper, more complex, and more diverse genomic, epigenomic, molecular, and spatiotem- poral physiological and anatomical imaging data provides invaluable information for the holistic discovery of the genetic and epigenetic structure of disease and has the potential to be translated into better understanding of basic biomedical mechanisms and to enhance diagnosis of disease, prediction of clinical outcomes, characterization of disease progres- sion, management of health care, and development of treatments. Big data sparks machine learning and causal revolutions and rethinking the entire health and biomedical data analy- sis process. e analysis of big data in genomics, epigenomics, and imaging that covers fundamental changes in these areas is organized into two books: (1) Big Data in Omics and Imaging: Association Analysis and (2) Big Data in Omics and Imaging: Integrated Analysis and Causal Inference. e focus of this book is association analysis and machine learning. e standard approach to genomic association analysis is to perform analysis individually, one trait and one variant at a time. e traditional analytic tools were originally designed for analyzing homogeneous, single phenotype, and common variant data. ey are not suitable to cope with big heterogeneous genomic data due to both methodological and performance issues. Deep analysis of high-dimensional and heterogeneous types of genomic data in the sequenc- ing era demands a paradigm shi in association analysis from standard multivariate data analysis to functional data analysis, from low-dimensional data analysis to high-dimensional data analysis, and from individual PC to multicore cluster and cloud computing. ere has been rapid development of novel and advanced statistical methods and com- putational algorithms for association analysis with next-generation sequencing (NGS) in the past several years. However, very few books have covered their current development. is book intends to bridge the gap between the traditional statistical methods and computational tools for small genetic data analysis and the modern, advanced statistical methods, computa- tional algorithms, and cloud computing for sequencing-based association analysis. is book will bring technologies for statistical modeling, functional data analysis, convex optimization, high-dimensional data reduction, machine learning, and multiple phenotype data analysis together. is book also aims to discuss interesting real data analysis and applications.