Introduction to Bioinformatics Algorithms - Brossura

Jones Neil J.; Pevzner Pavel A.

Brossura

ISBN 10: 8180520781 ISBN 13: 9788180520785

Casa editrice: ANE Books, 2009

Riassunto

This introductory text offers a clear exposition of the algorithmic principles driving advances in bioinformatics. Accessible to students in both biology and computer science, it strikes a unique balance between rigorous mathematics and practical techniques, emphasizing the ideas underlying algorithms rather than offering a collection of apparently unrelated problems.The book introduces biological and algorithmic ideas together, linking issues in computer science to biology and thus capturing the interest of students in both subjects. It demonstrates that relatively few design techniques can be used to solve a large number of practical problems in biology, and presents this material intuitively.An Introduction to Bioinformatics Algorithms is one of the first books on bioinformatics that can be used by students at an undergraduate level. It includes a dual table of contents, organized by algorithmic idea and biological idea; discussions of biologically relevant problems, including a detailed problem formulation and one or more solutions for each; and brief biographical sketches of leading figures in the field. These interesting vignettes offer students a glimpse of the inspirations and motivations for real work in bioinformatics, making the concepts presented in the text more concrete and the techniques more approachable.PowerPoint presentations, practical bioinformatics problems, sample code, diagrams, demonstrations, and other materials can be found at the Author's website.

Su questo libro

Recensione:

It's ironic that a new field like bioinformatics rarely offers any way for newcomers to feel welcome. Bioinformatics is maturing, and this book is that welcome. It's written as a textbook for a Bioinformatics 101 course, the kind that has both computing and biology students in it. Historically, the two have lived in uneasy truce. The biologists thought that a 'database' was an enzyme that acted on 'datab'. The programmers would, in the authors' words, "spontaneously abort" at the chemistry and informality of biology. Maybe that's less true now, but the authors offer just enough computing basics for the biologists and just enough biology for the computer crowd to be able to discuss the same thing. After that intro, the authors cover many of the classic problems in bioinformatics, including assembly, motif-finding, clustering, HMMs, dynamic programming, and even mass spec analysis. The style is very readable, and discusses both the biology and the computation of every topic presented. Many algorithms are built up in steps, showing how successive insights from both computation and biology can make existing techniques work better. Along the way, they offer biographical notes about the founders and luminaries of modern biological computation. This is a great first book for anyone wanting to enter the field, from either a biology or a computer science background. Advanced students will bottom out quickly, and may lose patience with the informal and gently-paced discussion. Sorry, this book was never meant for them. It's a beginner's book, one that respects the intelligence and capability of its reader. It's broad, basic, and detailed enough that modest programming skill will yield working code. This book has my highest recommendation. --wiredweird

This is the first book that I've read regarding bioinformatics, so Im updating this as my class moves along. You better have a grasp of basic data structures prior to beginning this book and background with a programming language as there is very little hand-holding in this text. A bio background makes it all more interesting but certainly is not critical. There are no sample code or sources printed with the book nor is there an included CD nor answers to exercises. There is an associated web site where some ideas may be had and errata found/reported, but its not very active that I have seen. The pseudo code in the book is very python-like so easy to make use of. I personally transfer the book's concepts to C/C++ (habit) without much problem, except sometimes my results differ from the book. Apparently these are book bugs, so be sure to check the web site out if unexpected things pop up. Presently my class is in chapter 8 (of 12) and looking back I would like to caution that some data processing algorithms will drive a computer's CPU quite hard so be aware of battery-munching & heat. My only bones with this book so far are the alphabet soup of variables and lack of answers to exercises. It would be nice if variable definitions were refreshed at the beginning of pseudo code samples. I like this book as an algorithms text over traditional texts because the applications are much more fascinating. Imagine searching for something and you don't know where that something is. On top of that add not even knowing exactly what it is you are looking for. And when you do find it, its not even in the data searched! This may sound unlikely or even impossible, but it is neither. Rather, its very cool. --Yoshiro Aoki

Bioinformatics is probably the fastest growing field in both biology and computer science. The problems have come from the computer science department and the biology department having such fundamentally different goals. The computer scientists see the computer as an end in itself with no real thought on trying to do something useful with it. The biologists see the computer as just another tool in their laboratory. And the biological problems are huge, massive computers like the new Cray's and large Linux clusters are being devoted to biological applications. This book is intended to fit into the chasm between biology and computer science. It discusses computer the algorithmic principles in terms of practical techniques that make sense to the undergraduate biologist. The book is well suited for a first class for the budding bioinformaticist. Each main chapter in the book first introduces an algorithm, then it discusses the biologically relevant problems that this algorithm addresses, it includes a detailed problem and one or more solutions. Finally the chapter concludes with brief biographical sketches of leading figures in the field. --John Matlock

L'autore:

Neil C. Jones received his PhD from UCSD and is now a Staff Software Engineer at Google. Pavel Pevzner is Ronald R. Taylor Professor of Computer Science at the University of California, San Diego. He is the author of Computational Molecular Biology: An Algorithmic Approach (MIT Press, 2000).

Su questa edizione

• Editore: ANE Books
• Data di pubblicazione: 2009
• ISBN 10: 8180520781
• ISBN 13: 9788180520785
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 438