Big Data in Omics and Imaging, Two Volume Set (Chapman & Hall/CRC Computational Biology Series)

FEATURESBridges 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 dataProvides tools for high dimensional data reductionDiscusses searching algorithms for model and variable selection including randomization algorithms, Proximal methods and matrix subset selectionProvides real-world examples and case studiesWill have an accompanying website with R codeProvides 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