Big Data in Omics and Imaging: Integrated Analysis and Causal Inference Contributor(s): Xiong, Momiao (Author) |
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ISBN: 0815387105 ISBN-13: 9780815387107 Publisher: CRC Press
Binding Type: Hardcover - See All Available Formats & Editions Published: June 2018 Click for more in this series: Chapman & Hall/CRC Mathematical and Computational Biology |
Additional Information |
BISAC Categories: - Mathematics | Probability & Statistics - General - Science | Life Sciences - Biology - Science | Biotechnology |
Dewey: 610.151 |
Series: Chapman & Hall/CRC Mathematical and Computational Biology |
Physical Information: 1.9" H x 6.3" W x 9.4" L (3.25 lbs) 766 pages |
Descriptions, Reviews, Etc. |
Publisher Description: 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
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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