About this item
Highlights
- Statistical genomics is a rapidly developing field, with more and more people involved in this area.
- About the Author: Shizhong Xu, PhDUniversity of California, Department of Botany and Plant Sciences, Riverside, CA, USA
- 428 Pages
- Science, Life Sciences
Description
About the Book
This book synthesizes current statistical methodologies applied to genome study, exploring statistical models and methods behind major bioinformatics software. Helps researchers choose the optimal algorithm to analyze data and to better interpret results.Book Synopsis
Statistical genomics is a rapidly developing field, with more and more people involved in this area. However, a lack of synthetic reference books and textbooks in statistical genomics has become a major hurdle on the development of the field. Although many books have been published recently in bioinformatics, most of them emphasize DNA sequence analysis under a deterministic approach.
Principles of Statistical Genomics synthesizes the state-of-the-art statistical methodologies (stochastic approaches) applied to genome study. It facilitates understanding of the statistical models and methods behind the major bioinformatics software packages, which will help researchers choose the optimal algorithm to analyze their data and better interpret the results of their analyses. Understanding existing statistical models and algorithms assists researchers to develop improved statistical methods to extract maximum information from their data.
Resourceful and easy to use, Principles of Statistical Genomics is a comprehensive reference for researchers and graduate students studying statistical genomics.
From the Back Cover
Statistical genomics is a rapidly developing field, with more and more people involved in this area. However, a lack of synthetic reference books and textbooks in statistical genomics has become a major hurdle to the development of the field. Although many books have been published recently in bioinformatics, most of them emphasize DNA sequence analysis under a deterministic approach.
Principles of Statistical Genomics synthesizes the state-of-the-art statistical methodologies (stochastic approaches) applied to genome study. It facilitates understanding of the statistical models and methods behind the major bioinformatics software packages, which will help researchers choose the optimal algorithm to analyze their data and better interpret the results of their analyses. Understanding existing statistical models and algorithms assists researchers to develop improved statistical methods to extract maximum information from their data.
Resourceful and easy to use, Principles of Statistical Genomics is a comprehensive reference for researchers and graduate students studying statistical genomics.
Review Quotes
From the reviews:
"The book was compiled from a collection of lecture notes for a statistical genomics course offered to University California Riverside graduate students by the author. It can be used as a textbook for graduate students in statistical genomics, but also by researchers as a reference book. ... For advanced readers of this very modern book in a new field of biometrics, they can choose to read any particular chapters as they desire in this multidisciplinary area." (T. Postelnicu, zbMATH, Vol. 1276, 2014)About the Author
Shizhong Xu, PhDUniversity of California, Department of Botany and Plant Sciences, Riverside, CA, USA