For more information and a complete list Statistical and Computational Pharmacogenomics Rongling Wu
University of Florida, Gainesville, USA
Stochastic Modelling for Systems Biology
Min Lin
Darren J. Wilkinson
Duke Clinical Research Institute, Durham, North Carolina, USA
Computational Biology 18
Textbook
Newcastle University, UK
Series: Interdisciplinary Statistics
Series: Chapman & Hall/CRC Mathematical & Computational Biology, 11
Due to the tremendous accumulation of data for genetic markers, pharmacogenomics, the study of the functions and interactions of all genes in the overall variability of drug response, is one of the hottest areas of research in biomedical science. Statistical and Computational Pharmacogenomics presents recent developments in statistical methodology with a number of detailed worked examples that outline how these methods can be applied. This comprehensive volume provides key tools needed to understand and model the genetic variation for drug response and equips statisticians with a thorough understanding of this complex field and how computational skills can be employed.
Although stochastic kinetic models are increasingly accepted as the best way to represent and simulate genetic and biochemical networks, most researchers in the field have limited knowledge of stochastic process theory. Stochastic Modelling for Systems Biology provides an accessible introduction to this theory using examples that are familiar to systems biology researchers. Focusing on computer simulation, the author examines the use of stochastic processes for modelling biological systems. Along with the latest simulation techniques and research material, such as parameter inference, the text includes many examples and figures as well as software code in R for various applications.
Catalog no. C8288, ISBN: 978-1-58488-828-4 January 2009, 6-1/8 x 9-1/4, 368 pp. Suggested Price: $79.95 / £44.99
Catalog no. C5408, ISBN: 978-1-58488-540-5 2006, 6-1/8 x 9-1/4, 280 pp. Suggested Price: $88.95 / £49.99
Textbook
Textbook
Statistics in Human Genetics and Molecular Biology
Structural Bioinformatics
An Algorithmic Approach
Cavan Reilly
Forbes J. Burkowski
University of Minnesota, Minneapolis, USA
University of Waterloo, Ontario, Canada
Series: Chapman & Hall/CRC Texts in Statistical Science Series
Series: Chapman & Hall/CRC Mathematical & Computational Biology, 20
Focusing on problems in contemporary genetics and molecular biology, this text describes basic statistical methods used in genetics. It covers cluster analysis, combinatorial optimization, and dynamic programming, along with the core topics of genome mapping, biological sequence analysis, and the analysis of gene expression arrays. The author also explores Bayesian approaches, such as hidden Markov models and block motif methods, as well as modern tools of Bayesian analysis, including Markov chain Monte Carlo (MCMC). The text features a number of worked examples and problem sets at the end of each chapter.
Showcasing the beauty of protein structures, this practical text illustrates how to apply key algorithms to solve problems related to macromolecular structure. It emphasizes a methodology that uses mathematical models to act as links between structural biology and computational algorithms. The author shows that protein structure can produce symmetry and beauty as well as biological function. He also gives many examples of dynamic programming applications, including RNA secondary structure prediction and protein sequence alignment. The book includes problems throughout the text, exercises at the end of each chapter, and a 12-page color insert.
Catalog no. C7263, ISBN: 978-1-4200-7263-1 June 2009, 6-1/8 x 9-1/4, 320 pp. Suggested Price: $59.95 / £36.99
Catalog no. C6838, ISBN: 978-1-58488-683-9 January 2009, 6-1/8 x 9-1/4, 429 pp. Suggested Price: $79.95 / £44.99
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CATITB_K00163_2009 Cat.indd 18
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