Overview

Full Product Details

9780815387107

Table of Contents

Reviews

"""I would like to recommend a new option in the library market, Big Data in Omics and Imaging: Integrated Analysis and Causal Inference, written by Momiao Xiong, a Professor of Biostatistics at the University of Texas Health Science Center in Houston. It is an extensive and comprehensive textbook on big data inbiomedical sciences. Indeed, its contents is very valuable, because it concerns the analysis of large-scale datasets, which now regularly occur in computational biology and medicine, in particular in ‘omics’ problems... The book introduces in detail the currently developed statistical methods and software for big genomic and epi-genomic, wearable biosensors, computing, and image data analysis. It covers important topics in this area, such as: genotype-phenotype network analysis, causal analysis and network biology, wearable computing and genetic analysis of function-valued traits, RNA-seq data analysis, methylation data analysis, imaging, and genomics... It was really interesting and fascinating to go through the pages of the book. It would hold a very valuable position on the home shelf-book or university library; I warmly recommend the book."" - Malgorzata Cwiklinska-Jurkowska, ISCB, December 2019 ""In his book, Professor Xiong introduces, discusses, and implements a rich variety of statistical tools that can be used to study large-scale features obtained from the human brain and genome, map neural and genetic signatures to behavioral and disease outcomes, and make causal enquiries into their relationships. The scope of the book is comprehensive, the concepts deep, and technicalities oftentimes mathematically heavy...the book discusses statistical concepts and devices that readers may find useful in studying general problems in human neuroscience and human genetics."" - Oliver Y. Chén, Journal of the American Statistical Association, March 2020 ""I would like to recommend a new option in the library market, Big Data in Omics and Imaging: Integrated Analysis and Causal Inference, written by Momiao Xiong, a Professor of Biostatistics at the University of Texas Health Science Center in Houston. It is an extensive and comprehensive textbook on big data inbiomedical sciences. Indeed, its contents is very valuable, because it concerns the analysis of large-scale datasets, which now regularly occur in computational biology and medicine, in particular in ‘omics’ problems... The book introduces in detail the currently developed statistical methods and software for big genomic and epi-genomic, wearable biosensors, computing, and image data analysis. It covers important topics in this area, such as: genotype-phenotype network analysis, causal analysis and network biology, wearable computing and genetic analysis of function-valued traits, RNA-seq data analysis, methylation data analysis, imaging, and genomics... It was really interesting and fascinating to go through the pages of the book. It would hold a very valuable position on the home shelf-book or university library; I warmly recommend the book."" - Malgorzata Cwiklinska-Jurkowska, ISCB, December 2019 ""In his book, Professor Xiong introduces, discusses, and implements a rich variety of statistical tools that can be used to study large-scale features obtained from the human brain and genome, map neural and genetic signatures to behavioral and disease outcomes, and make causal enquiries into their relationships. The scope of the book is comprehensive, the concepts deep, and technicalities oftentimes mathematically heavy...the book discusses statistical concepts and devices that readers may find useful in studying general problems in human neuroscience and human genetics."" - Oliver Y. Chén, Journal of the American Statistical Association, March 2020"

I would like to recommend a new option in the library market, Big Data in Omics and Imaging: Integrated Analysis and Causal Inference, written by Momiao Xiong, a Professor of Biostatistics at the University of Texas Health Science Center in Houston. It is an extensive and comprehensive textbook on big data inbiomedical sciences. Indeed, its contents is very valuable, because it concerns the analysis of large-scale datasets, which now regularly occur in computational biology and medicine, in particular in 'omics' problems... The book introduces in detail the currently developed statistical methods and software for big genomic and epi-genomic, wearable biosensors, computing, and image data analysis. It covers important topics in this area, such as: genotype-phenotype network analysis, causal analysis and network biology, wearable computing and genetic analysis of function-valued traits, RNA-seq data analysis, methylation data analysis, imaging, and genomics... It was really interesting and fascinating to go through the pages of the book. It would hold a very valuable position on the home shelf-book or university library; I warmly recommend the book. - Malgorzata Cwiklinska-Jurkowska, ISCB, December 2019 In his book, Professor Xiong introduces, discusses, and implements a rich variety of statistical tools that can be used to study large-scale features obtained from the human brain and genome, map neural and genetic signatures to behavioral and disease outcomes, and make causal enquiries into their relationships. The scope of the book is comprehensive, the concepts deep, and technicalities oftentimes mathematically heavy...the book discusses statistical concepts and devices that readers may find useful in studying general problems in human neuroscience and human genetics. - Oliver Y. Chen, Journal of the American Statistical Association, March 2020 I would like to recommend a new option in the library market, Big Data in Omics and Imaging: Integrated Analysis and Causal Inference, written by Momiao Xiong, a Professor of Biostatistics at the University of Texas Health Science Center in Houston. It is an extensive and comprehensive textbook on big data inbiomedical sciences. Indeed, its contents is very valuable, because it concerns the analysis of large-scale datasets, which now regularly occur in computational biology and medicine, in particular in 'omics' problems... The book introduces in detail the currently developed statistical methods and software for big genomic and epi-genomic, wearable biosensors, computing, and image data analysis. It covers important topics in this area, such as: genotype-phenotype network analysis, causal analysis and network biology, wearable computing and genetic analysis of function-valued traits, RNA-seq data analysis, methylation data analysis, imaging, and genomics... It was really interesting and fascinating to go through the pages of the book. It would hold a very valuable position on the home shelf-book or university library; I warmly recommend the book. - Malgorzata Cwiklinska-Jurkowska, ISCB, December 2019 In his book, Professor Xiong introduces, discusses, and implements a rich variety of statistical tools that can be used to study large-scale features obtained from the human brain and genome, map neural and genetic signatures to behavioral and disease outcomes, and make causal enquiries into their relationships. The scope of the book is comprehensive, the concepts deep, and technicalities oftentimes mathematically heavy...the book discusses statistical concepts and devices that readers may find useful in studying general problems in human neuroscience and human genetics. - Oliver Y. Chen, Journal of the American Statistical Association, March 2020

I would like to recommend a new option in the library market, Big Data in Omics and Imaging: Integrated Analysis and Causal Inference, written by Momiao Xiong, a Professor of Biostatistics at the University of Texas Health Science Center in Houston. It is an extensive and comprehensive textbook on big data inbiomedical sciences. Indeed, its contents is very valuable, because it concerns the analysis of large-scale datasets, which now regularly occur in computational biology and medicine, in particular in 'omics' problems... The book introduces in detail the currently developed statistical methods and software for big genomic and epi-genomic, wearable biosensors, computing, and image data analysis. It covers important topics in this area, such as: genotype-phenotype network analysis, causal analysis and network biology, wearable computing and genetic analysis of function-valued traits, RNA-seq data analysis, methylation data analysis, imaging, and genomics... It was really interesting and fascinating to go through the pages of the book. It would hold a very valuable position on the home shelf-book or university library; I warmly recommend the book. - Malgorzata Cwiklinska-Jurkowska, ISCB, December 2019 In his book, Professor Xiong introduces, discusses, and implements a rich variety of statistical tools that can be used to study large-scale features obtained from the human brain and genome, map neural and genetic signatures to behavioral and disease outcomes, and make causal enquiries into their relationships. The scope of the book is comprehensive, the concepts deep, and technicalities oftentimes mathematically heavy...the book discusses statistical concepts and devices that readers may find useful in studying general problems in human neuroscience and human genetics. - Oliver Y. Chen, Journal of the American Statistical Association, March 2020

I would like to recommend a new option in the library market, Big Data in Omics and Imaging: Integrated Analysis and Causal Inference, written by Momiao Xiong, a Professor of Biostatistics at the University of Texas Health Science Center in Houston. It is an extensive and comprehensive textbook on big data inbiomedical sciences. Indeed, its contents is very valuable, because it concerns the analysis of large-scale datasets, which now regularly occur in computational biology and medicine, in particular in 'omics' problems... The book introduces in detail the currently developed statistical methods and software for big genomic and epi-genomic, wearable biosensors, computing, and image data analysis. It covers important topics in this area, such as: genotype-phenotype network analysis, causal analysis and network biology, wearable computing and genetic analysis of function-valued traits, RNA-seq data analysis, methylation data analysis, imaging, and genomics... It was really interesting and fascinating to go through the pages of the book. It would hold a very valuable position on the home shelf-book or university library; I warmly recommend the book. - Malgorzata Cwiklinska-Jurkowska, ISCB, December 2019

Author Information

Momiao Xiong is a professor of Biostatistics at the University of Texas Health Science Center in Houston where he has worked since 1997. He received his PhD in 1993 from the University of Georgia.

Tab Content 6

Customer Reviews

Recent Reviews

Countries Available