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Life Sciences - Plant Sciences | Principles of Statistical Genomics

Principles of Statistical Genomics

Xu, Shizhong

2013, XVI, 428 p.

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  • Covers microarray data analysis
  • Introduces the Bayesian method
  • Features rigorous mathematical approaches to derive the statistical methods

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. 

Content Level » Research

Keywords » currentjks - genomics - statistical genomics - statistical methodology

Related subjects » Animal Sciences - Plant Sciences

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