Author	Ewens, Warren J. author
Title	Statistical Methods in Bioinformatics [electronic resource] : An Introduction / by Warren J. Ewens, Gregory R. Grant
Imprint	New York, NY : Springer New York : Imprint: Springer, 2001
Connect to	http://dx.doi.org/10.1007/978-1-4757-3247-4
Descript	XIX, 476 p. online resource

Advances in computers and biotechnology have had an immense impact on the biomedical fields, with broad consequences for humanity. Correspondingly, new areas of probability and statistics are being developed specifically to meet the needs of this area. There is now a necessity for a text that introduces probability and statistics in the bioinformatics context. This book also describes some of the main statistical applications in the field, including BLAST, gene finding, and evolutionary inference, much of which has not yet been summarized in an introductory textbook format. This book grew out of the bioinformatics courses given at the University of Pennsylvania. The material is, however, organized to appeal to biologists or computer scientists who wish to know more about the statistical methods of the field, as well as to trained statisticians who wish to become involved in bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level. Later chapters should be immediately accessible to the trained statistician. Sufficient mathematics background consists of courses in calculus and linear algebra. The basic biological concepts that are used are explained, or can be understood from the context

1 Probability Theory (i): One Random Variable -- 2 Probability Theory (ii): Many Random Variables -- 3 Statistics (i): An Introduction to Statistical Inference -- 4 Stochastic Processes (i): Poisson Processes and Markov Chains -- 5 The Analysis of One DNA Sequence -- 6 The Analysis of Multiple DNA or Protein Sequences -- 7 Stochastic Processes (ii): Random Walks -- 8 Statistics (ii): Classical Estimation and Hypothesis Testing -- 9 BLAST -- 10 Stochastic Processes (iii): Markov Chains -- 11 Hidden Markov Models -- 12 Computationally Intensive Methods -- 13 Evolutionary Models -- 14 Phylogenetic Tree Estimation -- A Basic Notions in Biology -- B Mathematical Formulae and Results -- B.1 Numbers and Intervals -- B.2 Sets and Set Notation -- B.3 Factorials -- B.4 Binomial Coefficients -- B.5 The Binomial Theorem -- B.6 Permutations and Combinations -- B.7 Limits -- B.8 Asymptotics -- B.9 Stirlingโs Approximation -- B.10 Entropy as Information -- B.11 Infinite Series -- B.12 Taylor Series -- B.13 Uniqueness of Taylor series -- B.14 Laurent Series -- B.15 Numerical Solutions of Equations -- B.16 Statistical Differentials -- B.17 The Gamma Function -- B.18 Proofs by Induction -- B.19 Linear Algebra and Matrices -- C Computational Aspects of the Binomial and Generalized Geometric Distribution Functions -- D BLAST: Sums of Normalized Scores -- References -- Author Index

1. Statistics
2. Bioinformatics
3. Computational biology
4. Statistics
5. Statistics for Life Sciences
6. Medicine
7. Health Sciences
8. Biomedicine general
9. Computer Appl. in Life Sciences