Bioinformatics: An Introduction (Computational Biology) [Hardcover]

Jeremy Ramsden (Author)

Book Description

Publication Date: April 14, 2009 | ISBN-10: 1848002564 | ISBN-13: 978-1848002562 | Edition: 2nd ed.

The field of bioinformatics continues to develop energetically. This comprehensive second edition covers new findings using the successful formula of the original text. Bioinformatics: An Introduction is structured into three parts devoted to Information, Biology and Applications. Every section of the book has been thoroughly updated, and expanded where relevant, to take account of significant new discoveries and realizations of the importance of certain concepts. Furthermore new chapters on Algorithms, Data Processing and Biotechnology have been added. Emphasis is placed on the underlying fundamentals and acquisitions of a broad and comprehensive grasp of the field as a whole. This second edition is intended to be a complete study companion for the advanced undergraduate or graduate student. It is self-contained, bringing together the multiple disciplines necessary for a profound grasp of the field into a coherent whole, allowing the reader to gain much insight into the state of art of bioinformatics.

Editorial Reviews

Review

From the reviews of the second edition: "This book is a self-contained guide to bioinformatics that targets undergraduates interested in this field. Bioinformatics is an interdisciplinary field that involves computer science, engineering, biology, and medicine. This book covers the basics of these areas, so readers will acquaint themselves with the fundamental building blocks of bioinformatics. … In short, this book is a wonderful text for entry-level students." (Hsun-Hsien Chang, ACM Computing Reviews, June, 2009)

From the Back Cover

Bioinformatics is interpreted as the application of information science to biology, in which it plays a fundamental and all-pervasive role. The field continues to develop intensively in both academia and commercially, and is highly interdisciplinary. This broad-ranging and thoroughly updated second edition covers new findings while retaining the successful formula of the original text. Bioinformatics: An Introduction (2nd Edition) is structured into three parts devoted to Information, Biology, and Applications. Every section of the book has been completely revised for currency, and expanded where relevant, to take account of significant new discoveries and realizations of the importance of key concepts. Furthermore, two new chapters provide instruction about algorithms and knowledge representation. Emphases are placed on the underlying fundamentals and on acquisition of a broad and comprehensive grasp of the field as a whole. Features: • Provides a solid foundation in, and self-contained introduction to, the field of bioinformatics and its state-of-the-art as it relates to computational biology research • Offers additional chapters on algorithms and knowledge representation, including text mining [NEW] • Imparts a thorough grounding of core concepts, enabling the reader to understand contemporary work within an optimal context • Contains a greatly expanded chapter on interactions and regulatory networks [NEW] • Includes examples, definitions, problems and a comprehensive and useful bibliography • Presents new experimental methods, and serves as a springboard for new research [NEW] • Incorporates discussion of the method of drawing inferences from abstract sequence analysis based on frequency dictionaries • Contains an extensively revised chapter on medical applications [NEW] • Emphasizes the underlying fundamentals and acquisition of a broad and comprehensive grasp of the field as a whole This significantly improved second edition of a successful textbook is intended to be a complete study companion for the advanced undergraduate or graduate student. It is self-contained, bringing together the multiple disciplines necessary for a profound grasp of the field into a coherent whole, thereby allowing the reader to gain much insight into the state-of-the-art of bioinformatics.

Product Details

• Hardcover: 288 pages
• Publisher: Springer; 2nd ed. edition (April 14, 2009)
• Language: English
• ISBN-10: 1848002564
• ISBN-13: 978-1848002562
• Product Dimensions: 9.3 x 6.2 x 1.1 inches
• Shipping Weight: 1.2 pounds (View shipping rates and policies)
• Average Customer Review: 3.0 out of 5 stars  See all reviews (1 customer review)
• Amazon Best Sellers Rank: #1,499,191 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

3.0 out of 5 stars Not what I expected, September 11, 2011

By 

Eric "Siggy" Scott (Annandale, VA) - See all my reviews

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This review is from: Bioinformatics: An Introduction (Computational Biology) (Hardcover)

To those who (like me) think of bioinformatics as a set of tools used to study large-scale genetic and proteomic data, this book feels a little bit out of place. Its emphasis is on big-picture theory -- specifically, the rôle played by information theory in biology. It is not very unified -- for example the extensive theoretical ideas developed in the first part rarely make an appearance in part III, "applications."

The flavor of the book is perhaps best summed up by the following comparison. In Ramsden we find:

A) ~80 pages on information theory, probability, and complexity.
B) ~25 pages pages on sequence databases, alignment algorithms, and phylogeny.
C) ~5 pages on protein folding/structure/function prediction and drug discovery.

Compare to Lesk's Introduction to Bioinformatics:

A) ~8 pages.
B) ~129 pages.
C) ~90 pages.

Ramsden's approach reminds me of high-level, grandioise complexity science books (ex. Érdi's Complexity Explained or Mitchell's Complexity: A Guided Tour). To Ramsden, bioinformatics is not a set of tools but rather the more general study of "deviations from randomness" in the genome.

This sort of discussion can be very enlightening, and is important for developing a personal research world view. His discussions of information theory, for instance, helped give me a wider appreciation for the meaning and importance it has in science at large.

However, his big-picture discussion left precision by the wayside, and it wasn't always possible to determine what he meant as he hopped fleetingly from discussing the definitions of microstates/macrostates/KL-divergence to vaguely referring to the Shannon Index as "remembered information" and its quantity as the "intensity of selection," and later discussing the "durability of information." All these analogies are swell, but they are not defined with any degree of precision, and so the possible interpretations of what the author means are underdetermined by the text.

I suppose that is to say that the book, while chalked full of mathematical equations and concepts, is at times difficult to follow for its lack of rigor. More than once I wrote "WTF?" in the margin.

This does not mean it is worthless, and many researchers who have a sharp interest in unifying theory can probably find value in it while developing their own vista on what biological information should be and how it relates to probability theory.

While sections of this book are good and stand-alone introductions to basic concepts of information theory, molecular biology, and bioinformatics tools, this book is not a practical introduction to the tools and methods of bioinformatics as it is practiced on a regular basis. For that, read Lesk, or for a more mathematically rigorous (if outdated) treatment, see Waterman's (Introduction to Computational Biology: Maps, Sequences and Genomes.