Introduction to Mathematical Methods in Bioinformatics / Universitext (PDF)

Alexander Isaev is a senior lecturer in mathematics at The Australian National University, Canberra. His mathematical background is the area of several complex variables. After completing a PhD degree in 1990 at the Moscow State University, he taught at the University of Illinois (Urbana-Champaign) and at Chalmers University of Technology, Göteborg, Sweden. One is his current interests is in applying mathematics to biology.

From the reviews:



"The book has two parts corresponding to two courses. The first part is ‘Sequence Analysis’ … . The second part is ‘Mathematical Background for Sequence Analysis’ … . The book concentrates on the mathematical basics of bioinformatics … . It is a comprehensive guide to bioinformatics, which takes into account that a bioinformatician should be able not only to use software packages, but also to know the mathematics behind these packages." (T. Postelnicu, Zentralblatt MATH, Vol. 1053, 2005)

"In Introduction to Mathematical Methods in Bioinformatics, Alexander Isaev describes the mathematical foundations of bioinformatics … . The book succeeds in describing the fundamental mathematics behind computational sequence analysis, and in giving a taste of typical applied bioinformatics. … The number of examples presented in the book is excellent … . is a strong description of the theory behind the standard methods of computational sequence analysis. The book serves as a springboard for considering current bioinformatics research problems … ." (Michael W. Deem, Physics Today, October, 2005)

"Introduction to Mathematical Methods in Bioinformatics introduces several basic topics in biological sequence analysis … . This book is an excellent textbook for students majoring in biology, computer science, and, especially mathematics … . presents a clear and mathematically rigorous exposition of procedures, algorithms, and models commonly used in analyzing biological sequences … . a splendid textbook for students with some mathematical background. It is quite mathematical in its logical approach and rigorous presentation, fulfilling the author’s aim … ." (Yi Huang, Journal of Applied Statistics, Vol. 32 (4), 2005)

"The book’s first half, cover sequence alignment, hidden Markov models, protein folding, and phylogenetic reconstruction. The second half provides necessary grounding in probability and statistics for full