Publisher: CRC Press
Series: Chapman & Hall/CRC Mathematical and Computational Biology
Publication Date: June 26, 2018
Binding: Kobo eBook
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.
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 ...