Big Data in Omics and Imaging, Two Volume Set
暫譯: 大數據在組學與影像學中的應用（兩卷套裝）

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

Provides a natural extension and companion volume to Big Data in Omic and Imaging: Association Analysis, but can be read independently.

Introduce causal inference theory to genomic, epigenomic and imaging data analysis

Develop novel statistics for genome-wide causation studies and epigenome-wide causation studies.

Bridge the gap between the traditional association analysis and modern causation analysis

Use combinatorial optimization methods and various causal models as a general framework for inferring multilevel omic and image causal networks

Present statistical methods and computational algorithms for searching causal paths from genetic variant to disease

Develop causal machine learning methods integrating causal inference and machine learning

Develop statistics for testing significant difference in directed edge, path, and graphs, and for assessing causal relationships between two networks

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.