An Introduction to Bioinformatics Algorithms

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An introductory text that emphasizes the underlying algorithmic ideas that are driving advances in bioinformatics.

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.

 

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User Review  - tomhudson - LibraryThing

A bit of a mathematician's view of what parts of biology are interesting or important, but much more readable than Pevzner's previous book. We use it with graduate students, despite its nominal ... Read full review

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Contents

Molecular Biology Primer
57
4
83
Greedy Algorithms
125
Dynamic Programming Algorithms
147
DivideandConquer Algorithms
227
Graph Algorithms
247
Combinatorial Pattern Matching
311
Sequencing
320
Clustering and Trees
339
Hidden Markov Models
387
Randomized Algorithms
409
Using Bioinformatics Tools
419
Index
428
Copyright

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Page 423 - An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database.
Page 421 - B. Alberts, D. Bray, J. Lewis, M. Raff, K. Roberts, and J. Watson. Molecular Biology of the Cell, Garland, NY, 1989.
Page 438 - Professor in the Department of Computer Science and Engineering at the University of NebraskaLincoln (UNL).
Page 421 - SF Altschul, W. Gish, W. Miller, EW Myers, and DJ Lipman. Basic local alignment search tool.

About the author (2004)

Neil C. Jones is a Ph.D. candidate in the Department of Computer Science and Engineering at the University of California, San Diego.

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