Product Description

This textbook presents mathematical models in bioinformatics and describes biological problems that inspire the computer science tools used to manage the enormous data sets involved. The first part of the book covers mathematical and computational methods, with practical applications presented in the second part. The mathematical presentation avoids unnecessary formalism, while remaining clear and precise. The book closes with a thorough bibliography, reaching from classic research results to very recent findings. This volume is suited for a senior undergraduate or graduate course on bioinformatics, with a strong focus on mathematical and computer science background.

Product Description
In the current era of complete genome sequencing, Bioinformatics and Molecular Evolution provides an up-to-date and comprehensive introduction to bioinformatics in the context of evolutionary biology.This important textbook will equip readers with a thorough understanding of the quantitative methods used in the analysis of molecular evolution, and will be essential reading for advanced undergraduates, graduates, and researchers in molecular biology, genetics, genomics, computational biology, and bioinformatics courses. ... Read more

Customer Reviews (2)

Book okay, very poor delivery
The book was of good quality, but delivery of the product was extremely slow and compensation was almost nothing.

Excellent book
I think this is one of the best books I have read on molecular evolution. The explanations are lucid. Easy to understand examples are given in increasing order of complexity.
The book is not restricted to molecular evolution itself but covers a wide range of topics. I highly recommend it! ... Read more

Product Description
An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.In this book Pierre Baldi and Søren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised. ... Read more

Customer Reviews (16)

Terrible
I'm a graduate student, reading a lot of bioinformatics materials.This is by far the worst text I've read on the subject.Poorly explained, poorly edited.Poor.

the worst book I have ever read
Just a collection of formulae, in an unclear way. Once we tried to use it in our seminar of bioinformatics, but after a few chapters we had to give it up for its bad writing. I could not find any reason to buy it or read it.

Could have been a great one.
This book is decidedly a mix: some very good information, combined with some very puzzling omissions and uneven editing.

First, the good. The description of stochastic context free grammars is the best I've seen. I don't know any other reference that even hint at how to use generative grammars to evaluate likelihoods. Once they caught my interest, though, the authors did not carry through with training and evaluation algorithms I could really use. I suspect that parts of the information are there, but I'll have to go back over their opaque notation again to work out just what they've given and just what's been left out.

This same pattern - an interesting introduction with missing or mysterious development - recurs throughout the book. The discussion on clustering and phylogeny goes the same way: a number of techniques are mentioned but not developed. The authors mention a tree drawing problem, not just building the tree's topology, but ordering the branches for the most informative rendering. Again, a critical topic and one that most authors miss - in the end, these authors miss it, too, by mentioning but not filling in the idea.

Their discussion of neural nets suffers badly from the authors' partial presentation. Evaluation of network output for a given input is relatively straightforward, and they present it in some detail. Training the net is the real problem, though, and is given less than a page.

Baldi and Brunak give more of the fundamentals than most authors. For example, they explain the maximum entropy principle well enough that I'll use it in lots of other areas. They give some coverage to topics of intermediate complexity, such as the forward and backward algorithms for HMM training. Finally, they fizzle out at the higher levels of complexity - the Baum-Welch algorithm could have followed from the forward and backward methods, but is left as a reference to another book.

There is some good here, especially in the fundamentals behind important techniques. The discussions I wanted - the more avanced topics, in forms I can use - are often weak, missing, or impenetrable. Just a bit more work, clearly within the authors' capability, would have made this a landmark reference.

An excellent book.
Very well written, clear, and self-contained. The authors provide a masterly treatment of machine learning methods (neural networks, hidden markov models, etc.) and their applications to fundamental problems in sequence analyis and biology. The book goes all the way from first principles to advanced research topics and should be valuable for both students and researchers. Second edition has many new topics, including DNA microarrays. Requires some concentration but mathematical details are summarized in the appendices. I strongly recommend it for anyone with an interest in bioinformatics and/or machine learning.

A very bad book. A colection of references w/o explanations
I just bought this book and am COMPLETEly disappointed with it.
Here is why. The book is badly written, hard to read and follow. Although it is said that this is a book is for " many readers", it is really for those who have already known all the algorithms. It is simply impossible to learn the algorithms from this book. The chapter on neural network is a few pages. It provieds a few equations for backpropagation. That is it! It is pretty much true for every thing else. Equations, hard to understand sentences, abbreviations with no explnantions, tons of citations everywhere. A book should strive to explain, and not to cite what other papers and go look there all the time. I suspect the few good reviews here are from the authors themselves.

I have a good programming background. I also read some papers on neural network and hidden markov models, This book is a lot worse than anything I have read in explaining the stuff. Very disappointed. Save your money and get something else. ... Read more

Product Description
This book provides an essential understanding of statistical concepts necessary for the analysis of genomic and proteomic data using computational techniques. The author presents both basic and advanced topics, focusing on those that are relevant to the computational analysis of large data sets in biology. Chapters begin with a description of a statistical concept and a current example from biomedical research, followed by more detailed presentation, discussion of limitations, and problems. The book starts with an introduction to probability and statistics for genome-wide data, and moves into topics such as clustering, classification, multi-dimensional visualization, experimental design, statistical resampling, and statistical network analysis.

• Clearly explains the use of bioinformatics tools in life sciences research without requiring an advanced background in math/statistics
• Enables biomedical and life sciences researchers to successfully evaluate the validity of their results and make inferences
• Enables statistical and quantitative researchers to rapidly learn novel statistical concepts and techniques appropriate for large biological data analysis
• Carefully revisits frequently used statistical approaches and highlights their limitations in large biological data analysis
• Offers programming examples and datasets
• Includes chapter problem sets, a glossary, a list of statistical notations, and appendices with references to background mathematical and technical material
• Features supplementary materials, including datasets, links, and a statistical package available online

Statistical Bioinformatics is an ideal textbook for students in medicine, life sciences, and bioengineering, aimed at researchers who utilize computational tools for the analysis of genomic, proteomic, and many other emerging high-throughput molecular data. It may also serve as a rapid introduction to the bioinformatics science for statistical and computational students and audiences who have not experienced such analysis tasks before. ... Read more

Product Description
This book is designed to introduce biologists, clinicians and computational researchers to fundamental data analysis principles, techniques and tools for supporting the discovery of biomarkers and the implementation of diagnostic/prognostic systems.

The focus of the book is on how fundamental statistical and data mining approaches can support biomarker discovery and evaluation, emphasising applications based on different types of "omic" data. The book also discusses design factors, requirements and techniques for disease screening, diagnostic and prognostic applications.

Readers are provided with the knowledge needed to assess the requirements, computational approaches and outputs in disease biomarker research. Commentaries from guest experts are also included, containing detailed discussions of methodologies and applications based on specific types of "omic" data, as well as their integration. Covers the main range of data sources currently used for biomarker discovery

• Covers the main range of data sources currently used for biomarker discovery

• Puts emphasis on concepts, design principles and methodologies that can be extended or tailored to more specific applications

• Offers principles and methods for assessing the bioinformatic/biostatistic limitations, strengths and challenges in biomarker discovery studies

• Discusses systems biology approaches and applications

• Includes expert chapter commentaries to further discuss relevance of techniques, summarize biological/clinical implications and provide alternative interpretations

Product Description

Designed for a one or two semester senior undergraduate or graduate bioinformatics course, Statistical Bioinformatics takes a broad view of the subject - not just gene expression and sequence analysis, but a careful balance of statistical theory in the context of bioinformatics applications. The inclusion of R  code as well as the development of advanced methodology such as Bayesian and Markov models provides students with the important foundation needed to conduct bioinformatics.

Customer Reviews (3)

a disappointing book
I would like to learn more of statistical bioinformatics and R programming and therefore expected this book to be published. Unfortunately, reading this book turned to be a disappointing experience. First of all, the presentation is pretty uneven: a lot of things are very basic, whereas some mathematics is much more difficult and requires good math training. Second, R programming is not at all interesting in this book and one cannot learn useful skills in this area. Another problem is that the book has a rather limited scope not highlighting many important areas of statistical bioinformatics of current interest. Finally, there are some factual mistakes in the theory, e.g. alternative hypothesis in ANOVA does not test that all the means are unequal, that is, no two or more means are equal to each other. To conclude, I appreciate that the author put a lot of work into this book, but the result still needs a lot to be useful and popular with bioinformatics students.

Excellent textbook on Statistical Bioinformatics
This book gives a nice introduction to Statistics, bioinformatics, and develops R codes for beginners. The concepts in statistics are very well laid out. After first few chapters, it takes a reader to a level where the reader can handle multivariate multisample files in bioinformatics.

The author has justified material included in the book. In the preface, author has given a plan to readers from different background. I like the way introductions to MCMC, Design of Experiments, Bayesian Inference, and Multiple Comparisons are provided.Most of the examples, and material provided in the text are taken from research articles, and references have been provided. It provides practical applications to problems.

The author has aimed the book to educate a diverse group of students who have some knowledge of statistics, but not of R and Bioinformatics.Sufficient material and basic beginner examples in R are provided for simplicity for beginners in the book, which I think will help most of the students to make their own sophisticated programs in R later on in the text.

Examples from students' point of view are provided. Some of the examples provided are easy to understand and easy to replicate. This book is based on class teaching experience of author as he mentions in the preface, and author really knows how to pitch a topic to diverse student population.

This book is not an encyclopedia of bioinformatics or statistics or R, hence, experts in bioinformatics or statistics or R may find other research level specialized books in the area more useful. As I mentioned earlier, this book is mainly written for upper level undergraduate students and first year graduate students having diverse educational background.

I like this book a lot, and I think it is an excellent textbook in the area.

A statistics textbook masquerading as a statistical bioinformatics textbook, forget about R
This is a statistics and probability textbook with some of the author's limited exposure to bioinformatics thrown in - the bioinformatics material is absurdly narrow in scope and most of the R code might as well be omitted it is so worthless.

The treatment of statistics is decent - a thorough overview of probability, distributions, inference, and Bayesian statistics is presented. There are so many summation and integral symbols in here it will make your eyes glaze over. At its core this might be a decent statistics textbook. Most of the examples seem fairly generic - such that biological concepts were placed in phrases where terms from the social sciences or engineering could have been used just as easily.

The R code is woefully repetitious, or in other cases so elementary it just takes up space. For example,
>E1R<-2
>E1G<-7
>E1B<-5
>E1N<-E1R+E1G+E1B
>E1N
[1] 14 -> gee thanks!

Perhaps due to the author's research interests this is a very microarray-centric textbook, and would have been more relevant 6 years ago. I wish authors would do a brief metasearch before sitting down to organize a textbook. There are probably 1000+ bioinformatics papers in 2009 dealing in the statistics of genome wide association analysis, and a handful of papers on MCMC or using the Metropolis Hastings algorithm. Yes parameter estimation deserves attention but not 100% weight. Protein microarrays, many of whose manufacturers have gone belly up, gets its own section for some reason. Next generation sequencing gets no exposure whatsoever (the copyright on this book is 2010 people!). Statistical genetics - QTLs, linkage disequilibrium - nothing. I'm pretty sure there is some sophisticated statistics involved in BLAST alignments and estimating homology but it is not mentioned here. The author instead focused on explaining stuff like "single fractal analysis" for binding kinetics (isn't that biochemistry?)

All the color figures are at the back, and consist of low-resolution gifs obviously taken from the web. The author did not contribute much in way of figures to aid understanding of posterior probability or Markov Chains lest it take space from the umpteenth formula or R "print" statement.

This hardcover is an expensive way to learn statistics and the author's myopic view of bioinformatics and weak code will not serve students well in their careers. ... Read more

Product Description
Fundamental Concepts of Bioinformatics is the first book co-authored by a biologist and computer scientist that is specifically designed to make bioinformatics accessible and provide readers for more advanced work. Readers learn what programs are available for analyzing data, how to understand the basic algorithms that underlie these programs, what bioinformatic research is like, and other basic concepts. Information flows easily from one topic to the next, with enough detail to support the major concepts without overwhelming readers. Problems at the end of each chapter use real data to help readers apply what they have learned so they know how to critically evaluate results from both a statistical and biological point of view.Focus on fundamentally important algorithms at the core of bioinformatics.For anyone interested in bioinformatics (in biology or computer science), computational biology, molecular biology, or genomics. ... Read more

Customer Reviews (3)

Condition of book mislabeled?
Condition was stated as like new condition with little highlighting. Well there was highlighting and writing all throughout the book. Book was in decent condition, just definitely not like new condition

A great textbook and reference book for both students and researchers.
Bioinformatics is a burgeoning interdisciplinary field that holds great promise in handling large scale biomedical data by computational approaches. The book "Fundamental Concepts of Bioinformatics" is a very important textbook and reference book for both biology and computer science students and researchers, as well as for those professionals in medical science, and the pharmaceutical industry. It goes with saying that many laboratory approaches are expensive and time consuming, and cannot hope to keep up with the rapid growth of available data, making computational approaches indispensable.While a number of books dealing with bioinformatics, most of them are generally limited in scope, and very few of them provide a comprehensive but easy understandable treatment from both computer science and biomedical principles.This book is unique and is well-organized, and provides a systematic but straightforward treatment of the various techniques used for bioinformatics. One of the attractive features of the book is the comprehensive coverage of the various types of data use in bioinformatics analysis, followed by computational approaches that are most suited to the particular data type. This book also helps researchers entering bioinformatics. The reader can quickly identify the chapters that are most relevant to their own interest. It could also be used as a textbook for a senior undergraduate or a graduate level bioinformatics course. It is a valuable resource to both students and researchers, no matter whether they perform experimental research or computer science studies. Computer scientists, mathematicians, and statisticians seeking to discover how bioinformatics is related to well-defined paradigms in computer science could also benefit greatly from this book. Professors Michael L. Raymer and Dan E. Kranehave authored many research articles in both computer science and biological science. I highly recommend this book as a great textbook and reference book for both students and researchers.

good undergrad/opening text
Features

First bioinformatics primer for undergraduates. Personable writing style and numerous analogies make this text accessible to undergraduates.

Focus on fundamentally important algorithms at the core of bioinformatics.

Easy-to-do "paper and pencil" calculations make fundamental algorithms unintimidating for biology students and accessible to students with limited experience in computer programming.

Combined expertise (biology and computer science) of author team ensures an integrated approach and an appreciation for the biology and computer science tools and perspectives.

End-of-Chapter summaries tie together key concepts and provide real-world examples of the algorithms presented.

Detailed solutions to selected text questions are provided in the back of the text so students can check their answers.

Annotated Reading Material sections at the end of each chapter direct students to additional resources for further explanation.

Questions and problems at the end of each chapter help students apply their understanding of the material.

Contents

MOLECULAR BIOLOGY AND BIOLOGICAL CHEMISTRY.
DATA SEARCHES AND PAIRWISE ALIGNMENTS.
SUBSTITUTION PATTERNS.
DISTANCE-BASED METHODS OF PHYLOGENETICS.
CHARACTER-BASED APPROACHES TO PHYLOGENETICS.
GENOMICS AND GENE RECOGNITION.
PROTEIN FOLDING.
PROTEOMICS. ... Read more

Product Description
In this introduction to computational molecular biology, Rex Dwyer explains many basic computational problems and gives concise, working programs to solve them in the Perl programming language.With minimal prerequisites, he covers the biological background for each problem, develops a model for the solution, and then introduces the Perl concepts needed to implement the solution.The chapters discuss pairwise and multiple sequence alignment, fast database searches for homologous sequences, protein motif identification, genome rearrangement, physical mapping, phylogeny reconstruction, satellite identification, sequence assembly, gene finding, and RNA secondary structure. Concrete examples and a step-by-step approach enable readers to grasp the computational and statistical methods. ... Read more

Customer Reviews (2)

Not a good perl programming book period!
This books tries to combine and explain both bioinformatics and perl programming yet fails miserably at both. Though I have taken a class on learning perl this code is difficult to read and poorly explained. The bioinformatics is useless because the examples are simply stupid. For example instead of using free energy to determine RNA folding the author uses hydrogen bonding which is completely irrelavent or predicting species by using %gc or %at content between two organsims also useless.If you are looking for bioinformatics programming tips this book will not help you.
Variables are introduced that are not explained and the program is written in the most condensed possible way making it difficult to read and leaving you wading through each line. I am thankful I have taken programming perl and bioinformatics or this book would be of zero value. If I could I would give this book a -5 stars. Check it out at a library before you BUY!!!!!!! Even if perl.com reviews the book favorably the biology is at best completely WRONG!!! Buy O'Riely's advanced bioinformatics.

Develops effective genomic toolkits for UNIX, Windows & Mac
Combines intuitive derivations of most key algorithms, thoughtful use of key references to illustrate solutions of main problems with a detailed example, and develop well documented, carefully programmed,perl toolkit.The 65 routines on the CD in UNIX, Windows, and Mac formats perform most of the essential maipulations of GenBank sequences. I only miss Hidden Markov Model routines. ... Read more

Product Description
“… this book was written from start to finish by one extremely dedicated and erudite individual. The author has done an excellent job of covering the many topics that fall under the umbrella of computational biology for vaccine design, demonstrating an admirable command of subject matter in fields as disparate as object-oriented databases and regulation of T cell response. Simply put, it has just the right breadth and depth, and it reads well. In fact, readability is one of its virtues—making the book enticing and useful, all at once…” Human Vaccines, 2010

"... This book has several strong points. Although there are many textbooks that deal with vaccinology, few attempts have been made to bring together descriptions of vaccines in history, basic bioinformatics, various computational solutions and challenges in vaccinology, detailed experimental methodologies, and cutting-edge technologies... This book may well serve as a first line of reference for all biologists and computer scientists..." –Virology Journal, 2009

Vaccines have probably saved more lives and reduced suffering in a greater number of people than any other medical intervention in human history, succeeding in eradicating smallpox and significantly reducing the mortality and incidence of other diseases. However, with the emergence of diseases such as SARS and the threat of biological warfare, vaccination has once again become a topic of major interest in public health. 

Vaccinology now has at its disposal an array of post-genomic approaches of great power. None has a more persuasive potential impact than the application of computational informatics to vaccine discovery; the recent expansion in genome data and the parallel increase in cheap computing power have placed the bioinformatics exploration of pathogen genomes centre stage for vaccine researchers. 

This is the first book to address the area of bioinformatics as applied to rational vaccine design, discussing the ways in which bioinformatics can contribute to improved vaccine development by

• introducing the subject of harnessing the mathematical and computing power inherent in bioinformatics to the study of vaccinology
• putting it into a historical and societal context, and 
• exploring the scope of its methods and applications.

Bioinformatics for Vaccinology is a one-stop introduction to computational vaccinology. It will be of particular interest to bioinformaticians with an interest in immunology, as well as to immunologists, and other biologists who need to understand how advances in theoretical and computational immunobiology can transform their working practices. ... Read more

Customer Reviews (2)

Weird and not exactly cohesive, but I like it
I work in this very field (bioinformatics and vaccinology), so I expect that I am right in the middle of this book's target audience.My impressions overall have been mixed, but at the end of the day I think this book is very good.The copy I had read came from a colleague, but I like it enough that I'll be buying a copy for myself.

This book is all over the place in ways that are good (even great) and bad.My initial reaction to this book was surprise:the first 50 pages was about the history of vaccination, followed by another 20 pages about the contemporary use of vaccines.So right there, 25% of the book is a historical and contemporary background -- a perspective neither technical nor heavily biological, as the title would otherwise imply.

At this point -- even though I was enjoying the material -- I felt that the title was misleading.And yet it still took another 40 pages of immunological background before Flower really kicks into the meat of the text.Here, however, is where the book turns into a terrific overview.I learned heaps -- about databases and analytical methods that I didn't even know existed.I was able to apply topics from the book immediately into my work, and I'm sure I'll reference it again in the future.It's like a terrific survey course.It won't teach you how to program, or model databases, or analyze data, or how to use any of the tools/methods that are discussed, but it gives you a nice scenic tour of all the important landmarks you should know about.

I mentioned that my reaction was mixed, and indeed this book has some downsides worth mentioning.The largest is that the author never properly settled on who the audience should be.It seems to me that the book is mostly geared toward graduate students or professional practitioners in molecular biology who want to learn about how developments in bioinformatics can expand their research horizons.(I.e., those people who are heavy on the bio-, light on the -informatics.)This is because the book itself is technically light.Still, the book features a bizarre, inconsistent mix of pedagogical background and assumed knowledge.

I mentioned previously that there are 40 pages of background about how vaccines and the immune system works, written at a good depth for the biologically-naive.Yet much of the successive content is seriously biobabble-rich, perfectly suited for any molbio Ph.D. candidate.For example (to pick a sentence at random):"The molecule may exist as a canonical ensemble of tautomers, thus necessitating the the explicit construction of all tautomeric forms."If you don't know what tautomers are, or why this statement might be important, don't look for an explanation in this book.And this was a frequent occurrence with me:I would come across some content that was out of my biological depth, then glaze over it and move on.This happened at least once per page, and on a few occasions entire pages-long sections were skipped.

(From the other perspective, there are methods and tools described in the text that strict biologists would probably regard as out of their depth if they were to try to implement them, although I suspect that they would find the descriptions easier reading, as this book wasn't as infused with technobabble or mathbabble.)

And here's a personal gripe:some references aren't fully cited, which is a shame for an overview like this.For example, the table of z-scales on page 195 only covers 18 of 20 amino acids, so I tried to find the table via the original source.The text only references "Svante Wold and coworkers" and didn't cite an actual paper.And unfortunately, due to the long and many-storied history of Wold's z-scales, it took a while for me to track down the specific paper that contained the most-recent version of the table.So I find it ironic:the purpose of the book is to provide a sampler of methods and tools to use in the field of computational vaccinology, but here I was, trying to implement one of the described methods, and there was a surprisingly large barrier in accomplishing it due to an inadequate amount of information.

Still, in spite of its weaknesses, I like this book a lot, for what it is:an overview for people who are already fairly entrenched in the field.You'll probably find the information more consistently enriching if you have at least some graduate-level exposure to biology, although if you work more on the computational side, just snooze through the biobabble and you should find some great nuggets in this book as well.

No better book on the subject exists
The author has done an excellent job of covering the many topics that fall under the umbrella of computational biology for vaccine design, demonstrating an admirable command of subject matter in fields as disparate as object-oriented databases and regulation of T cell response. Simply put, it has just the right breadth and depth, and it reads well. In fact, readability is one of its virtues. Full review can be found at - Hum Vaccin. 2010 May 23;6(5). ... Read more

Product Description

The Beauty of Protein Structures and the Mathematics behind Structural Bioinformatics
Providing the framework for a one-semester undergraduate course, Structural Bioinformatics: An Algorithmic Approach shows how to apply key algorithms to solve problems related to macromolecular structure.

Helps Students Go Further in Their Study of Structural Biology
Following some introductory material in the first few chapters, the text solves the longest common subsequence problem using dynamic programming and explains the science models for the Nussinov and MFOLD algorithms. It then reviews sequence alignment, along with the basic mathematical calculations needed for measuring the geometric properties of macromolecules. After looking at how coordinate transformations facilitate the translation and rotation of molecules in a 3D space, the author introduces structural comparison techniques, superposition algorithms, and algorithms that compare relationships within a protein. The final chapter explores how regression and classification are becoming more useful in protein analysis and drug design.

At the Crossroads of Biology, Mathematics, and Computer Science
Connecting biology, mathematics, and computer science, this practical text presents various bioinformatics topics and problems within a scientific methodology that emphasizes nature (the source of empirical observations), science (the mathematical modeling of the natural process), and computation (the science of calculating predictions and mathematical objects based on mathematical models).

Customer Reviews (2)

For whom is this intended?
This book does a good job of explaining the common algorithms used in Structural bioinformatics. The reason why I am not giving it more than 3 stars is because this kind of information isn't very useful to many people. Most of these algorithms have been realized by various programmers, and it is all available in many servers. There are several internet sites and freely downloadble programs where you can upload/input PDB files and do various things like calculate Ramachandran plots, align structures, calculate RMSDs between various structures, do structural classifications, look for novel folds, calculate surface areas, determine the interactions between proteins in complexes and several other things structural bioinformaticians normally do. This book is useful for people who want to develop programs which can do all the aforementioned. But why would anyone, other than pastime codewriters, want to do something that has already been done several times before?

good start for students
I find the book by Forbes J.Burkowski to be a good text for students with some programming skills. Instead of focusing on the vast array of bioinformatics tools available in this field, the author chooses to show the details of a few fundamental algorithms that are behind many of those tools. I think this is an efficient way of teaching serious, critical users and also future developers. However, the book is not as comprehensive as I expected as it misses important bits such as side-chain design or fold recognition algorithms. Nevertheless, reading this book will help you grasp these absent subjects more easily, and I guess that might have been the author's intention. ... Read more

Product Description
With the ever-increasing volume of information in clinical medicine, researchers and health professionals need computer-based storage, processing and dissemination. In "Clinical Bioinformatics", leading experts in the field provide a series of articles focusing on software applications used to translate information into outcomes of clinical relevance. Covering such topics as gene discovery, gene function (microarrays), DNA mutation analysis, proteomics, online approaches and resources, and informatics in clinical practice, this volume concisely yet thoroughly explores its cutting edge subject. In this emerging "omics" era, "Clinical Bioinformatics" is the perfect guide for researchers and clinical scientists to unlock the complex, dense, and ever-growing accumulation of medical information. ... Read more

Product Description
Discover how to streamline complex bioinformatics applications with parallel computing


This publication enables readers to handle more complex bioinformatics applications and larger and richer data sets. As the editor clearly shows, using powerful parallel computing tools can lead to significant breakthroughs in deciphering genomes, understanding genetic disease, designing customized drug therapies, and understanding evolution.

A broad range of bioinformatics applications is covered with demonstrations on how each one can be parallelized to improve performance and gain faster rates of computation. Current parallel computing techniques and technologies are examined, including distributed computing and grid computing. Readers are provided with a mixture of algorithms, experiments, and simulations that provide not only qualitative but also quantitative insights into the dynamic field of bioinformatics.

Parallel Computing for Bioinformatics and Computational Biology is a contributed work that serves as a repository of case studies, collectively demonstrating how parallel computing streamlines difficult problems in bioinformatics and produces better results. Each of the chapters is authored by an established expert in the field and carefully edited to ensure a consistent approach and high standard throughout the publication.

The work is organized into five parts:
* Algorithms and models
* Sequence analysis and microarrays
* Phylogenetics
* Protein folding
* Platforms and enabling technologies

Researchers, educators, and students in the field of bioinformatics will discover how high-performance computing can enable them to handle more complex data sets, gain deeper insights, and make new discoveries. ... Read more

Product Description

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer. It presents a broad overview of statistical inference, clustering, and classification problems in two main high-throughput platforms: microarray gene expression and phylogenic analysis.

The book explores Bayesian techniques and models for detecting differentially expressed genes, classifying differential gene expression, and identifying biomarkers. It develops novel Bayesian nonparametric approaches for bioinformatics problems, measurement error and survival models for cDNA microarrays, a Bayesian hidden Markov modeling approach for CGH array data, Bayesian approaches for phylogenic analysis, sparsity priors for protein-protein interaction predictions, and Bayesian networks for gene expression data. The text also describes applications of mode-oriented stochastic search algorithms, in vitro to in vivo factor profiling, proportional hazards regression using Bayesian kernel machines, and QTL mapping.

Focusing on design, statistical inference, and data analysis from a Bayesian perspective, this volume explores statistical challenges in bioinformatics data analysis and modeling and offers solutions to these problems. It encourages readers to draw on the evolving technologies and promote statistical development in this area of bioinformatics.

Product Description
This brief, practical, tightly organized text shows you how to perform the biological applications. It is the only guide you need for bioinformatics every time. ... Read more

Product Description
Genomics and bioinformatics play an increasingly important and transformative role in medicine, society and agriculture. The mapping of the human genome has revealed 35,000 or so genes which might code for more than one protein, resulting in 100,000 proteins for the humans alone. Since proteins are attractive targets for developing drugs, efforts are now underway to map sequences and assign functions to many novel proteins. This book takes the novel approach to cover both the sequence and structure analysis of proteins in one volume and from an algorithmic perspective.

Key features of the book include:

• Provides a comprehensive introduction to the analysis of protein sequence and structure analysis.
• Takes an algorithmic approach, relying on computational methods rather than theoretical.
• Provides an integrated presentation of theory, examples, exercises and applications.
• Includes coverage of both protein structure, and sequence, analysis.
• Accessible enough for biologists, yet rigorous enough for computer scientists and mathematicians.
• Supported by a Web site featuring exercises, solutions, images, and computer programs. ... Read more

  Customer Reviews (2)

  Good introduction for undergraduates or as reference.
  The book 'Protein Bioinformatics' tries to cover all aspects of proteins, from sequence to structure. This is of course a very wide field and the difficulty of the algorithms involved in this analysis increases from sequence to structure investigations. From the preface of the book one can read, that this is still not enough for the authors because additionally they are trying to write this book for a broad audience, for researchers and students.
  After reading this book I think it could be used by undergraduate students in Bioinformatics or related fields or as reference. It does not give deep and clear explanations but rather provides short summaries of articles. The good thing is after reading this book you know of the existence of these articles and can consult them to understand the working mechanism of the algorithms in detail.
  
  There is certainly a lack in good books about proteins and especially about protein structure analysis which can partly filled by this book.

  Good intro, but light presentation
  This book gives good, basic coverage of the concepts important in understanding protein sequence and structure.
  
  There are three major sections in this book: sequence, structure, and the relatinship between the two. The sequence section covers all the basics: dynamic programming for string matching, scoring matrices, trees and classification, and profiles of various sorts. The sequence discussion is a bit shorter, but goes over substructures, similarity searching and scoring, and kinds of structures and domains. The third section is even shorter and unites the two areas: predicting structure from sequence, with a good introduction to threading.
  
  The book's strength is its breadth. It sacrifices depth to get that breadth, though. A few analytic techniques are sketched in the text or presented in psuedocode. Most often, however, a programmer will have a hard time gleaning enough detail from this to implement any of the algorithms described.
  
  The authors aim at readers who already understand the significance of protein structure and who are comfortable with ideas like hydrogen bonding. Lots of programmers will have a hard time understanding why problems are important or what the driving phenomena are. Biologists won't be put off by an excessively mathematical treatment, but won't get a detailed understanding of the algorithms or mathematical foundations either. This book comes close to under-serving both kinds of reader.
  
  This book is good for conceptual understanding of the algorithms, where implementable details don't matter, and gives good coverage to protein-specific issues. It's decidedly for someone who wants more than just the how-to of running BLAST or strucuture analysis tools. I think this book will help most if you want more understanding of what goes on inside the tools, or if you want an easy start to a deep and complex topic. Advanced readers may not like it, though - detail and real understanding just aren't there. I give this one four stars, but I had to round up to four.
  
  //wiredwerid ... Read more

Product Description
Bioconductor is a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology. Bioconductor is rooted in the open source statistical computing environment R.This volume's coverage is broad and ranges across most of the key capabilities of the Bioconductor project, including importation and preprocessing of high-throughput data from microarray, proteomic, and flow cytometry platforms:Curation and delivery of biological metadata for use in statistical modeling and interpretationStatistical analysis of high-throughput data, including machine learning and visualizationModeling and visualization of graphs and networksThe developers of the software, who are in many cases leading academic researchers, jointly authored chapters. All methods are illustrated with publicly available data, and a major section of the book is devoted to exposition of fully worked case studies.This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text; it is a dynamic document. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers. ... Read more

Customer Reviews (4)

extremely helpful, but suffers from multiple author problem
This book is great for helping you get started analyzing all types of microarrays in R.However, the chapters are written by several different authors which causes the book to be a little disorganized.This is probably the case with many books that have contributed chapters.In the end, the technical information is there, sometimes you just have to visit a couple of different chapters.

it's not well organized
I find this book is not so good for people without any gene or microarray experiment background. It didn't even give clear definition of the basic concepts.
Another problem is that it's not well organized because every chapter is written by different authors who have different interest and preference and use slightly different terms for the same thing.

technically accurate but pedagogically flawed
If you're like me, you came upon this book because you decided to use R for analysis of microarray data, but you're mired in its gory and frustrating details.

Yes, you need a reference book. But not this one, and certainly not this edition.Better documentation can be found elsewhere (dare I say online?).

The code examples given are technically accurate and run as advertised, but they are of the "monkey see, monkey do" variety.They provide little intuition for how to use R for oneself, outside the covers of this text.For example, Chapter 23 discusses linear models for microarray data (using the "limma" package), and several code examples contain the parameter 'adjust = "fdr"'.The reader is never enlightened that this refers to a "false discovery rate" adjustment.

In other cases, example code is simply missing.Chapter 21 covers the Rgraphviz graphing library, with a figure showing the three common graphical layouts -- but no example code for producing these graphs is given (I had to find it outside the book).

For those trying to use R for computational biology, I recommend getting an overview of the R programming language first (Venables and Ripley's book "Modern Applied Statistics with S" is a great text), and only then wading into references such as this one, if at all.

Book contains many chapters to help get you started
I purchased this book to learn specific details and look at applications for the functions present in bioconductor.I have had trouble applying some of the chapters to custom data because they are written for specific microarray/data formats.Overall, this book is a good value because it contains examples of how bioconductor can be used to aid in hypothesis testing, but I struggle to apply what I have read to the different types of data I have.The section on Statistical analysis for genomic experiments and the section on gaphs and networks should be the reason you purchase this book. They are very helpful and interesting. The case studies were not very helpful in my opinion. ... Read more

Product Description
Despite the fact that advanced bioinformatics methodologies have not been used as extensively in immunology as in other subdisciplines within biology, research in immunological bioinformatics has already developed models of components of the immune system that can be combined and that may help develop therapies, vaccines, and diagnostic tools for such diseases as AIDS, malaria, and cancer.

In a broader perspective, specialized bioinformatics methods in immunology make possible for the first time a systems-level understanding of the immune system. The traditional approaches to immunology are reductionist, avoiding complexity but providing detailed knowledge of a single event, cell, or molecular entity. Today, a variety of experimental bioinformatics techniques connected to the sequencing of the human genome provides a sound scientific basis for a comprehensive description of the complex immunological processes.

This book offers a description of bioinformatics techniques as they are applied to immunology, including a succinct account of the main biological concepts for students and researchers with backgrounds in mathematics, statistics, and computer science as well as explanations of the new data-driven algorithms in the context of biological data that will be useful for immunologists, biologists, and biochemists working on vaccine design. In each chapter the authors show interesting biological insights gained from the bioinformatics approach. The book concludes by explaining how all the methods presented in the book can be integrated to identify immunogenic regions in microorganisms and host genomes. ... Read more

Customer Reviews (1)

Bioinformatics at work
The huge majority of bioinformatics (BI) books seem to treat the topic as an end in itself. I have to admit, there is a lot to enjoy in the algorithms people have developed and in clever implementations. Often, though, the calculations appear to be fine art, to enjoy in abstract, or for creating point solutions to isolated problems.

This book breaks the mold. It addresses every aspect of immunology, using BI as the tool and as the unifying language for discussing immunology's many aspects. The content gets off to a slow start, starting with two chapters describing the topic and its importance. The next three chapters summarize a few of the basic algorithms: alignment and multiple alignment, motif-finding, Gibbs sampling, clustering, and neural networks. The discussion is competent, and the authors' handling of neural nets stands out from the crowd of BI books. Still, the pace is too brisk and the range of topics is too narrow to recommend this book as a general BI text.

It's not one, and never meant to be. That section just reminds the knowledgable reader of the mathematical tools and BI terms to be used in the remaining nine chapters. Here's where an immunology background will help a lot. I know, because I lack one. Still, the discussion holds to a level that a determined reader with a general bio and BI background can follow. Even at my barely-following level, it's exciting stuff.

At this writing, the 2005 H5N1 bird flu is all over the front pages, so medical response to emerging viruses is on people's minds. This book explores the whole range of issues in immunological response to the threat: identifying specific viral features that stand out as vaccine targets, understanding the immunological mechanisms that need to be engaged, evolution of the pathogens to emerging human resistance, and the ways that human variation affects the decisions in medicine and public policy.

That last surprised me, but makes perfect sense. Different human populations have slightly different sets of alleles for immunological response. It's one of the reasons that humanity does so well in a world of ever-changing antigenic threats. As a species, we have so many possible responses to any challenge that someone somewhere is bound to be able to survive almost any pathogen around. The range of immune-response alleles, their different sensitivities and combinations, and their distributions in different gene pools helps decide how a vaccine must be crafted. If the vaccine antigen generally triggers a good response in African Americans but not East Asians, it answers only part of the question.

This is the first text I know that really shows BI at work in clinically important ways. It's a guided tour of the world of immunological attacks and responses, measured using BI tools - not just pathogens, but allergens, autoimmune triggers, and even possible cancer treatment. Beginners will have a rough time following the discussion, but this is a book for people deep in their specialty. It gave me a good idea of what questions are asked, and why, and how BI answers them. I look forward to seeing an immunological researcher's review of this text - from the pure BI stand point, it's narrow, but shows the versatility of the tools it chooses.

//wiredweird ... Read more

Product Description

The biological sciences are now in the midst of a true life sciences revolution akin to what physics experienced just after the turn of the last century. We are now in a phase of unparalleled growth that is reflected by the amount of data generated from each experiment. At the time of this writing, the rate of data acquisition was approaching 2 terabytes over the course of 5 days with first pass analysis proceeding over the following 2-3 week period. This fundamental shift has provided unprecedented opportunities that for the first time afford us the ability, i.e., means, breadth, and depth of data, to truly address human biology at the systems level. This wealth of information from seemingly disparate datasets and its integration is being realized through bioinformatics. It is with this philosophy that the text Bioinformatics for Systems Biology was born. This revolution has spawned true personalized medicine that encompasses diagnostics and treatment through to cure.

For the physical and computer scientist, this text provides an introduction to the basic biological principles governing a cell. This quickly moves from the fundamentals to exploring the underlying genetic processes. While providing a rudimentary and necessary overview for the life scientist, the physical and computer scientist will be apprised of various nuances within the field reflecting the reality of “wet-bench” science. For those in the life sciences, it is rapidly becoming appreciated that we are progressing from examining our favorite “pet” gene to the system. Statistics is now an essential component to understand the vast datasets and this is emphasized throughout the text.

The majority of the text is devoted to the common ground that these groups share. It provides rich examples of tools, databases, and strategies to mine the databases to reveal novel insights. A host of examples of parsing the data into a series of overlays that use various presentation systems are reviewed. The goal is to provide a representation most comfortable to the user to enable the user to thoroughly explore the data. The text concludes with examples of how the systems information is used to inform personalized medicine in a true “bench to bedside” manner.

Bioinformatics for Systems Biology bridges and unifies many disciplines. It presents the life scientist, computational biologist, and mathematician with a common framework. Only by linking the groups together may the true life sciences revolution move forward in the mostly uncharted and emerging field of Systems Biology.

Customer Reviews (1)

Less Systems Biology Than I Had Hoped
Systems Biology is "a biology-based inter-disciplinary study field that focuses on the systematic study of complex interactions in biological systems, thus using a new perspective (holism instead of reduction) to study them." (Wikipedia)And by studying complex interactions, one can elucidate why the combination of parts gives rise to emergent properties.

This book addresses a lot of the issues in bioinformatics, but only about 10% seems to be something that might go beyond bioinformatics.The problem is that a lot of the material is focused on the complex interactions, which is good, but the material doesn't seem to bridge to a holistic perspective of biological systems.

I give low marks because the title misled me to think that 40-60% would be about deriving emergent properties, etc., or systems biology proper.I give more than 1 star because the book has some potentially useful bioinformatics material.It might be better titled as The Bioinformatics Foundations of Systems Biology.With that title, I would have given it higher marks.

This book might be useful as a supplemental text in an introductory bioinformatics course, but not as the main text, because there should be a better one that would explicitly say "Introduction" in the title.



... Read more

Customer Reviews (2)

good general overview, introduction
This book provides a good general overview/introduction of bioinformatics, genomics and proteomics. It does assume, however, that the reader has at least an introductory background in biology and chemistry. If this is not the case, you will probably find this book too advanced.

I liked all of the websites and recommended readings for additional information as the Internet is sometimes a pain to search, so it is nice to have some good site recommendations. The chapters are concise and written well, but again, if you haven't studied these subjects before, I think the book will go over your head. All in all a good general overview for the beginning scientist- aka high-school level or intro college level.

First contact
If you don't know anything about Bioinformaticas, this book is very adeccuacy for your first time ... Read more

Product Description
An introduction to biological networks and methods for their analysis

Analysis of Biological Networks is the first book of its kind to provide readers with a comprehensive introduction to the structural analysis of biological networks at the interface of biology and computer science. The book begins with a brief overview of biological networks and graph theory/graph algorithms and goes on to explore: global network properties, network centralities, network motifs, network clustering, Petri nets, signal transduction and gene regulation networks, protein interaction networks, metabolic networks, phylogenetic networks, ecological networks, and correlation networks.

Analysis of Biological Networks is a self-contained introduction to this important research topic, assumes no expert knowledge in computer science or biology, and is accessible to professionals and students alike. Each chapter concludes with a summary of main points and with exercises for readers to test their understanding of the material presented. Additionally, an FTP site with links to author-provided data for the book is available for deeper study.

This book is suitable as a resource for researchers in computer science, biology, bioinformatics, advanced biochemistry, and the life sciences, and also serves as an ideal reference text for graduate-level courses in bioinformatics and biological research. ... Read more