Problem Solving Handbook in Computational Biology and Bioinformatics

ISBN-13: 9780387097596 / Angielski / Twarda / 2010 / 347 str.

Alexander Dinghas; Lenwood S. Heath; Naren Ramikrishnan

Opis

Bioinformatics is growing by leaps and bounds; theories/algorithms/statistical techniques are constantly evolving. Nevertheless, a core body of algorithmic ideas have emerged and researchers are beginning to adopt a "problem solving" approach to bioinformatics, wherein they use solutions to well-abstracted problems as building blocks to solve larger scope problems.Problem Solving Handbook for Computational Biology and Bioinformatics is an edited volume contributed by world renowned leaders in this field. This comprehensive handbook with problem solving emphasis, covers all relevant areas of computational biology and bioinformatics. Web resources and related themes are highlighted at every opportunity in this central easy-to-read reference.Designed for advanced-level students, researchers and professors in computer science and bioengineering as a reference or secondary text, this handbook is also suitable for professionals working in this industry.

Recenzje

From the reviews:

"The editors of this handbook ... have 'collect(ed) together, in one place, the algorithms and methods that form the modern bioinformatician's toolkit.' ... Each chapter contains an introduction to the respective topic, detailed descriptions of the relevant algorithmic methods, software implementations, and applications, and concludes with advanced topics. ... this handbook provides some value for researchers and practitioners in the field. ... In summary, for most of the topics covered, this book complements the standard textbooks in a useful way." (Gunnar Klau, ACM Computing Reviews, June, 2011)

Spis treści

Preface.- Pairwise Sequence Alignment.- Sequence and Sequence Alignment Statistics.- Practical Multiple Sequence Alignment.- Phylogenetic Trees from Sequences.- Phylogenetic Networks.- Modern BLAST Programs.- Stochastic Calculus and Stochastic Simulation.- Genomic Sequence Comparison.- Genome Rearrangements.- Population Genetics Data Analysis.- Genome Wide Association Studies.- Practical Implications of Coalescent Theory.- Networks in Computational Systems Biology.- Practical Use of the Gene Ontology.- Techniques for Protein Structure.- Microarray Experiment Statistics.- Reasoning with Protein Contact Maps .- Identifying Modules in Protein-Protein Interaction Networks.- Motifs for Transcriptional Regulation .- Time Course Analysis.- Computational Simulation of Biochemical Networks.- Biomedical Data Integration.- Functional Annotation and Discovery.- Matrix and Tensor Decompositions.- Text Processing and Mining.- Formal Logics.- Index.