Big Data in Omics and Imaging: Integrated Analysis and Causal
Inference addresses the recent development of integrated genomic,
epigenomic and imaging data analysis and causal inference in big
data era. Despite significant progress in dissecting the genetic
architecture of complex diseases by genome-wide association studies
(GWAS), genome-wide expression studies (GWES), and epigenome-wide
association studies (EWAS), the overall contribution of the new
identified genetic variants is small and a large fraction of
genetic variants is still hidden. Understanding the etiology and
causal chain of mechanism underlying complex diseases remains
elusive. It is time to bring big data, machine learning and causal
revolution to developing a new generation of genetic analysis for
shifting the current paradigm of genetic analysis from shallow
association analysis to deep causal inference and from genetic
analysis alone to integrated omics and imaging data analysis for
unraveling the mechanism of complex diseases. FEATURES 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, epigenomics, medical image,
bioinformatics, and data science. Topics covered are: mathematical
formulation of causal inference, information geometry for causal
inference, topology group and Haar measure, additive noise models,
distance correlation, multivariate causal inference and causal
networks, dynamic causal networks, multivariate and functional
structural equation models, mixed structural equation models,
causal inference with confounders, integer programming, deep
learning and differential equations for wearable computing, genetic
analysis of function-valued traits, RNA-seq data analysis, causal
networks for genetic methylation analysis, gene expression and
methylation deconvolution, cell -specific causal networks, deep
learning for image segmentation and image analysis, imaging and
genomic data analysis, integrated multilevel causal genomic,
epigenomic and imaging data analysis.
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