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
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

The storage, processing, description, transmission, connection, and analysis of the waves of new genomic data have made bioinformatics skills essential for scientists working with DNA sequences. In Bioinformatics for DNA Sequence Analysis, experts in the field provide practical guidance and troubleshooting advice for the computational analysis of DNA sequences, covering a range of issues and methods that unveil the multitude of applications and the vital relevance that the use of bioinformatics has today. Individual book chapters explore the use of specific bioinformatic tools, accompanied by practical examples, a discussion on the interpretation of results, and specific comments on strengths and limitations of the methods and tools. As a part of the highly successful Methods in Molecular Biology™ series, this work provides the kind of detailed description and implementation advice that is crucial for getting optimal results.

Focused and cutting-edge, Bioinformatics for DNA Sequence Analysis serves molecular biologists, geneticists, and biochemists as an enriched task-oriented manual, offering step-by-step guidance for the analysis of DNA sequences in a simple but meaningful fashion.

Product Description

The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. When used in conjunction with specialized informatics, possibilities of real-world applications are achieved. Probabilistic Methods for BioInformatics explains the application of probability and statistics, in particular Bayesian networks, to genetics. This book provides background material on probability, statistics, and genetics, and then moves on to discuss Bayesian networks and applications to bioinformatics.

Customer Reviews (1)

Lots of examples
This book really helps in bridging formalism to understanding by providing lots of examples and walking through the examples.It's a pleasure to read.
One can skim what seems basic.But if something is not clear, one can work through a few examples. It's strength is pedagogical.

... Read more

Product Description
There is an increasing need throughout the biomedical sciences for a greater understanding of knowledge-based systems and their application to genomic and proteomic research. This book discusses knowledge-based and statistical approaches, along with applications in bioinformatics and systems biology. The text emphasizes the integration of different methods for analysing and interpreting biomedical data. This, in turn, can lead to breakthrough biomolecular discoveries, with applications in personalized medicine.

Key Features:

• Explores the fundamentals and applications of knowledge-based and statistical approaches in bioinformatics and systems biology.
• Helps readers to interpret genomic, proteomic, and metabolomic data in understanding complex biological molecules and their interactions.
• Provides useful guidance on dealing with large datasets in knowledge bases, a common issue in bioinformatics.
• Written by leading international experts in this field.

Students, researchers, and industry professionals with a background in biomedical sciences, mathematics, statistics, or computer science will benefit from this book. It will also be useful for readers worldwide who want to master the application of bioinformatics to real-world situations and understand biological problems that motivate algorithms. ... Read more

Product Description

Programming knowledge is often necessary for finding a solution to a biological problem. Based on the author’s experience working for an agricultural biotechnology company, Python for Bioinformatics helps scientists solve their biological problems by helping them understand the basics of programming. Requiring no prior knowledge of programming-related concepts, the book focuses on the easy-to-use, yet powerful, Python computer language.

The book begins with a very basic introduction that teaches the principles of programming. It then introduces the Biopython package, which can be useful in solving life science problems. The next section covers sophisticated tools for bioinformatics, including relational database management systems and XML. The last part illustrates applications with source code, such as sequence manipulation, filtering vector contamination, calculating DNA melting temperature, parsing a genbank file, inferring splicing sites, and more. The appendices provide a wealth of supplementary information, including instructions for installing Python and Biopython and a Python language and style guide.

By incorporating examples in biology as well as code fragments throughout, the author places a special emphasis on practice, encouraging readers to experiment with the code. He shows how to use Python and the Biopython package for building web applications, genomic annotation, data manipulation, and countless other applications.

Customer Reviews (1)

Valuable resource
I can only say that I highly recommend this book, especially for the biologist that is beginning in bioinformatics or python (or both). I cannot compare it to any other Python and Bioinformatics books (I'm planning to buy the another one), but I can say that I could learn a thing or two from Sebastian's book. Evidently is not a perfect book, as some of the explanations are a little bit rushed and might be difficult for a beginner. At the same time this is a very carefully thought and planned book and has more than enough for one to learn Python and apply the language to solve biological problems. I really liked the BioPython section, and this section made me use BioPython for the first time. Some of BioPython's examples in the book are light years ahead of the examples in the tool's website.

Lastly, I would like to congratulate Sebastian for his work and effort in putting together a nice tome for Python and Bioinformatics. It's a valuable resource for everyone in the field and certainly will help spread Python in the community. ... Read more

Product Description

Thoroughly Describes Biological Applications, Computational Problems, and Various Algorithmic Solutions

Developed from the author’s own teaching material, Algorithms in Bioinformatics: A Practical Introduction provides an in-depth introduction to the algorithmic techniques applied in bioinformatics. For each topic, the author clearly details the biological motivation and precisely defines the corresponding computational problems. He also includes detailed examples to illustrate each algorithm and end-of-chapter exercises for students to familiarize themselves with the topics. Supplementary material is available at http://www.comp.nus.edu.sg/~ksung/algo_in_bioinfo/

This classroom-tested textbook begins with basic molecular biology concepts. It then describes ways to measure sequence similarity, presents simple applications of the suffix tree, and discusses the problem of searching sequence databases. After introducing methods for aligning multiple biological sequences and genomes, the text explores applications of the phylogenetic tree, methods for comparing phylogenetic trees, the problem of genome rearrangement, and the problem of motif finding. It also covers methods for predicting the secondary structure of RNA and for reconstructing the peptide sequence using mass spectrometry. The final chapter examines the computational problem related to population genetics.

Product Description

This book looks at the mathematical foundations of the models which is crucial for correct interpretation of the outputs of the models. A bioinformatician should be able not only use software packages, but also know the mathematics behind these packages. From this point of view, mathematics departments throughout the world have a major role to play in bioinformatics education by teaching courses on the mathematical foundations of bioinformatics. The author wrote this book based on his lecture notes for his courses. It combines several topics in biological sequence analysis with mathematical and statistical material required for such analysis.

Product Description

Collaborative research in bioinformatics and systems biology is a key element of modern biology and health research. This book highlights and provides access to many of the methods, environments, results and resources involved, including integral laboratory data generation and experimentation and clinical activities. Collaborative projects embody a research paradigm that connects many of the top scientists, institutions, their resources and research worldwide, resulting in first-class contributions to bioinformatics and systems biology. Central themes include describing processes and results in collaborative research projects using computational biology and providing a guide for researchers to access them.

The book is also a practical guide on how science is managed. It shows how collaborative researchers are putting results together in a way accessible to the entire biomedical community.

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
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

KEY BENEFIT: Discovering Genomics is the first genomics text that combines web activities and case studies with a problem-solving approach to teach upper-level undergraduates and first-year graduate students the fundamentals of genomic analysis. More of a workbook than a traditional text, Discovering Genomics, Second Edition allows students to work with real genomic data in solving problems and provides the user with an active learning experience. KEY TOPICS: Genomic Medicine Case Study: What’s wrong with my child? Genome Sequence Acquisition and Analysis, Comparative Genomics in Evolution and Medicine, Genome Variations, Genomic Medicine Case Study: Why Can’t I Just Take a Pill to Lose Weight? Basic Research with DNA Microarrays, Applied Research with DNA Microarrays, Proteomics, Genomic Medicine Case Study: Why Can’t We Cure More Diseases? Genomic Circuits in Single Genes, Integrated Genomic Circuits, Modeling Whole-Genome Circuits. MARKET: For all readers interested in genomics.

Customer Reviews (7)

Good!
very nice! Don't remember if I brought it as new or else, but the condition is NEW! fast shipping. Very good, overall.

Genomics, Proteomics, and Bioinformatics textbook
This book is for students who have considerable knowledge of Biology that has included basic Genetics and Genomics. For those of us who studied Human Genetics in the 1960's it is a challenge to read and understand. If one is willing to study and learn completely new information including many new words and terms this book will enlighten one as to where we are in Genomics today and give insight into the future of Medicine. In the forty-five years that I have been a Family Physician there is nothing that has changed the practice of Medicine like this new field of Genomics and Proteomics most likely will. My suggestion to any physician who plans to practice more than five years
is to buy this book and find a Professor like Dr. Campbell to guide you to understand this new and exciting subject.

Convoluted layout, authors stray off topic, web links and problem sets are outdated
This book follows a convoluted path to describe basic methodologies that could be taught in a much more straightforward manner. The authors get so mired down in the biology of specific applications of bioinformatic tools that the tool itself falls into the background. The poor layout of the book even makes it difficult to read. The main text of the book is interspersed with examples, "Math Minutes" and other text which are not properly set off from the main text.

The web links associated with the book are outdated and do not appear to be updated by the publisher to keep up with changes. If a web site is associated with a book, it should at the very least keep up with changes. In a quickly changing field such as bioinformatics these updates are absolutely critical.

The book is also overpriced given the low quality content and paperback binding. The figures are subpar with only purple and gray coloring. I would expect at least a few full color figures for a book at this price point.

Overall I would say that this book is not a useful tool for teaching bioinformatics or genomics.

Textbook + website =great new textbook
This was a great textbook.The website was very helpful and I liked how the author did not waste paper/printing/money on images and half of the information was on the web. It was nice not carrying around a heavy text all semester, even if half my reading/work was done in front of a computer.A lot of information packed into this book.One sentence sometimes requires a lot of knowledge (thank goodness for my professor who explained it all).I would have to say, without a lecture, I would walk away from this text thinking I knew something but not knowing much at all.For an amature like me, I definitely need a lecture to go along with this text.Although some mistakes were found, a lot less than the first edition (so I'm told).

Great New Format to get students out of a dull book
This book represents a breakthrough in textbook design. It starts with a 'case study' for a child visiting you the physician. You get the basic symptoms from the mother, then you are sent to the web to go attempt to establish a diagnosis. And you are not sent to some private web site, but to the Online Mendelian Inheitance in Man (OMIN) database of human diseases and genes, and to the National Center for Biotechnology Information. Immediately the student is exposed to a wealth of information far beyond what any book could provide. It's rare that you see a textbook that attempts to take the student into the real world.

Intermixed with the case studies is textual materials that provide the student with the basic background that they need. In addition there are almost random Math Minutes and Discovery Questions that direct the student into further depth of understanding.

If you are planning to teach this kind of class, you owe it to yourself to at least investigate this book before selecting a text. ... 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
This book covers a wide range of subjects in applying machine learning approaches for bioinformatics projects. The book succeeds on two key unique features. First, it introduces the most widely used machine learning approaches in bioinformatics and discusses, with evaluations from real case studies, how they are used in individual bioinformatics projects. Second, it introduces state-of-the-art bioinformatics research methods. The theoretical parts and the practical parts are well integrated for readers to follow the existing procedures in individual research.

Unlike most of the bioinformatics books on the market, the content coverage is not limited to just one subject. A broad spectrum of relevant topics in bioinformatics including systematic data mining and computational systems biology researches are brought together in this book, thereby offering an efficient and convenient platform for teaching purposes.

An essential reference for both final year undergraduates and graduate students in universities, as well as a comprehensive handbook for new researchers, this book will also serve as a practical guide for software development in relevant bioinformatics projects. ... Read more

Product Description

Bioinformatics is a relatively new field of research. It evolved from the requirement to process, characterize, and apply the information being produced by DNA sequencing technology. The production of DNA sequence data continues to grow exponentially. At the same time, improved bioinformatics such as faster DNA sequence search methods have been combined with increasingly powerful computer systems to process this information. Methods are being developed for the ever more detailed quantification of gene expression, providing an insight into the function of the newly discovered genes, while molecular genetic tools provide a link between these genes and heritable traits. Genetic tests are now available to determine the likelihood of suffering specific ailments and can predict how plant cultivars may respond to the environment. The steps in the translation of the genetic blueprint to the observed phenotype is being increasingly understood through proteome, metabolome and phenome analysis, all underpinned by advances in bioinformatics. Bioinformatics is becoming increasingly central to the study of biology, and a day at a computer can often save a year or more in the laboratory.

The volume is intended for graduate-level biology students as well as researchers who wish to gain a better understanding of applied bioinformatics and who wish to use bioinformatics technologies to assist in their research. The volume would also be of value to bioinformatics developers, particularly those from a computing background, who would like to understand the application of computational tools for biological research. Each chapter would include a comprehensive introduction giving an overview of the fundamentals, aimed at introducing graduate students and researchers from diverse backgrounds to the field and bring them up-to-date on the current state of knowledge. To accommodate the broad range of topics in applied bioinformatics, chapters have been grouped into themes: gene and genome analysis, molecular genetic analysis, gene expression analysis, protein and proteome analysis, metabolome analysis, phenome data analysis, literature mining and bioinformatics tool development. Each chapter and theme provides an introduction to the biology behind the data describes the requirements for data processing and details some of the methods applied to the data to enhance biological understanding.

Product Description
Utilizing high speed computational methods to extrapolate to the rest of the protein universe, the knowledge accumulated on a subset of examples, protein bioinformatics seeks to accomplish what was impossible before its invention, namely the assignment of functions or functional hypotheses for all known proteins.

The Ten most Wanted Solutions in Protein Bioinformatics considers the ten most significant problems occupying those looking to identify the biological properties and functional roles of proteins.

- Problem One considers the challenge involved with detecting the existence of an evolutionary relationship between proteins.- Two and Three studies the detection of local similarities between protein sequences and analysis in order to determine functional assignment. - Four, Five, and Six look at how the knowledge of the three-dimensional structures of proteins can be experimentally determined or inferred, and then exploited to understand the role of a protein. - Seven and Eight explore how proteins interact with each other and with ligands, both physically and logically.- Nine moves us out of the realm of observation to discuss the possibility of designing completely new proteins tailored to specific tasks. - And lastly, Problem Ten considers ways to modify the functional properties of proteins.

After summarizing each problem, the author looks at and evaluates the current approaches being utilized, before going on to consider some potential approaches.

introbul>Features---------------------Features---------------------· Presents introductory material on protein structure and function, with an evolutionary perspective· Describes ten of the most cogent problems in computational biology· Considers future routes that are likely to improve our understanding of the exquisitely specific and efficient mechanisms of protein function· Includes a suggested reading list for further research at the end of each chapter· ... Read more

Customer Reviews (3)

Useful, but the title doesn't really describe it
The title of this book is misleading; at least, it misled me. Before opening it I thought it would deal with ten unsolved problems in protein bioinformatics that we should like to be able to solve but at present cannot. In other words, I expected that it would be a book for researchers that would challenge them to find solutions to major problems where none are currently available. I was, however, surprised that there should be as many as ten of these. In fact, this is not really a book for researchers, but one for students and others new to protein bioinformatics: these are the things we will want to know when we approach a protein with a bioinformatic approach, these are the sort of methods currently in use, this is the sort of information we can get from them, and these are the respects in which we may hope they will be improved in the future. In short, we are not dealing with questions that at present have no answers, but with ones that we may hope to be able to answer better. In this respect, however, protein bioinformatics is just like any other discipline: few, if any of the methods we use in science are so good that we cannot conceive of anything better.

As an example, Problem 1 concerns protein sequence alignment: the account begins with a discussion of protein evolution, leading to the distinction between orthology and paralogy and the ideas of protein families, similarity matrices and gap penalties. The chapter then proceeds to a description, at times quite advanced, of methods in current use for comparing and aligning sequences, including multiple alignments. Only in the last of nearly thirty pages discussing this problem does the author turn her attention to the ways in which the methods in use might be improved, but she provides almost no detail.

The other chapters deal with secondary-structure prediction from sequence information, prediction of biological function, tertiary-structure prediction, and so on, ending with more engineering topics such as the design of artificial proteins and the modification of existing proteins to fulfil novel functions. In all of these the presentation is competent, and the book will be very useful to anyone wanting to learn about protein bioinformatics, in particular about the state of the art today. On the other hand, with none of the problems are we dealing with a "most wanted solution" in the sense of seeking a way ahead when the road appears at present to be completely blocked. Nowhere does the author throw down her gauntlet before her colleagues, saying this is where you have failed, and must provide a solution to this vital problem.

Depends on what you want
This book delivers reasonably well on the promise in its title: it does a good job in stating the most computational interesting problems relating to proteins. It assumes the reader knows a little about biochemistry, biology, and computational techniques, but only a little about each. Given that base, it does a fair job in describing problems related to protein structure, function, analysis, and design. It's not an advanced text, in either its computational or biological sides, but not an elementary introduction, either. Someone a bit above novice level will probably get the most out of it.

A few things left me a bit leery about this text, though. Despite its 2005 copyright date, the author (p.53) cites an estimate of human 50,000 genes. I'm not sure where (or when) that number comes from, because most estimates today are closer to 30,000. There was another a minor annoyance in the discussion of convolution as a tool in protein docking. The failure to distinguish convolution from correlation is minor and forgivable. Saying that one "convolutes" a convolution is like say that one "revolutes" a revolution. Revolve: revolution, convolve: convolution. Also, the Fourier transform step in correlation, especially when docking a small molecule to a protein, is an optimization rather than a requirement. Transform-based correlation gives better performance for asymptotically large models. In some computing environments, for models of realistic sizes, the simplicity of direct correlation gives a performance advantage - and allows non-linear scoring algorithms that would be impossible with the transform approach.

This is a fair introduction to many of the ways people study proteins computationally, and to the kinds of tools required. There is very little computaitonal detail, however. It may help a tool-builder create a conceptual base for studying proteins, but won't help much with the specific calculations.

//wiredweird

Comprehensive but a little dated
This is a very useful overview of the very broad subject of bioinformatics, and it provides a good background on a variety of approaches to topics like protein conformation prediction.The translation is excellent - the subject-matter is clear and there are no obvious errors, which is unusual for such a technical subject.The main drawback of the book is that, because this is a field in which progress is being made rapidly, the book is already out of date in places.For example, in the chapter dealing with protein structure prediction, there is scant mention of the most successful approach to date, namely the Rosetta project initiated at the University of Washington in Seattle.

Nevertheless, this is a very useful primer for people coming into the area of bioinformatics and it covers topics that will not age as rapidly, such as certain statistical models.Indeed, the author's exposition of how Hidden Markov Models work is as clear as anything I've read anywhere. ... Read more

Product Description

Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are following closely behind the advances in data generation methods. The statistical methods required by bioinformatics present many new and difficult problems for the research community.

This book provides an introduction to some of these new methods. The main biological topics treated include sequence analysis, BLAST, microarray analysis, gene finding, and the analysis of evolutionary processes. The main statistical techniques covered include hypothesis testing and estimation, Poisson processes, Markov models and Hidden Markov models, and multiple testing methods.

The second edition features new chapters on microarray analysis and on statistical inference, including a discussion of ANOVA, and discussions of the statistical theory of motifs and methods based on the hypergeometric distribution. Much material has been clarified and reorganized.

The book is written so as to appeal to biologists and computer scientists who wish to know more about the statistical methods of the field, as well as to trained statisticians who wish to become involved with bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level, but with an emphasis on material relevant to later chapters and often not covered in standard introductory texts. Later chapters should be immediately accessible to the trained statistician. Sufficient mathematical background consists of introductory courses in calculus and linear algebra. The basic biological concepts that are used are explained, or can be understood from the context, and standard mathematical concepts are summarized in an Appendix. Problems are provided at the end of each chapter allowing the reader to develop aspects of the theory outlined in the main text.

Warren J. Ewens holds the Christopher H. Brown Distinguished Professorship at the University of Pennsylvania. He is the author of two books, Population Genetics and Mathematical Population Genetics. He is a senior editor of Annals of Human Genetics and has served on the editorial boards of Theoretical Population Biology, GENETICS, Proceedings of the Royal Society B and SIAM Journal in Mathematical Biology. He is a fellow of the Royal Society and the Australian Academy of Science.

Gregory R. Grant is a senior bioinformatics researcher in the University of Pennsylvania Computational Biology and Informatics Laboratory. He obtained his Ph.D. in number theory from the University of Maryland in 1995 and his Masters in Computer Science from the University of Pennsylvania in 1999.

Comments on the first edition:

"This book would be an ideal text for a postgraduate course…[and] is equally well suited to individual study…. I would recommend the book highly." (Biometrics)

"Ewens and Grant have given us a very welcome introduction to what is behind those pretty [graphical user] interfaces." (Naturwissenschaften)

"The authors do an excellent job of presenting the essence of the material without getting bogged down in mathematical details." (Journal American Statistical Association)

"The authors have restructured classical material to a great extent and the new organization of the different topics is one of the outstanding services of the book." (Metrika)

Customer Reviews (7)

modern bioinformatics
This topic should be of prime interest to statisticians. The authors are mathematical biologists and they bring out the theory and methodology in probability and statistics that is applicable to DNA and protein sequencing and matching. They provide a treatment of probability, stochastic processes and statistics that starts with the very basics and builds up.
Topics include basic probability and statistical inference, Poisson processes and Markov chains, DNA sequencing, hidden Markov models, computer intensive methods, evolutionary models and phylogenetic tree estimation.

Of particular interest to me is the material on permutation methods and the bootstrap. The bootstrap has been applied in phylogenetics and there has been some controversy about its application there. The authors cover this in Chapter 14 where they appear to have a resolution for the controversy.

Permutation tests are first discussed in Chapter 3 "A Introduction to Statistical Inferrence" and are compared with other computer intensive methods in Chapter 12. In Section 12.3 they discuss the Behrens-Fisher problem pointing out why permutation tests are not possible due to the unequal variances. They give the bootstrap t solution. Section 12.2.2 gives a brief, but nicely described, account of bootstrap estimation and confidence intervals and provides a number of references including the following books: Efron and Tibshirani (1993), Davison and Hinkley (1997), Efron (1982), Hall (1992), Manly (1997), Sprent (1998) and Chernick (1999). Bootstrap and permutation approaches to multiple testing are covered in Section 12.4.

My review does not really do justice to the detail and significance of the methodology of statistical genetics described in this book.For that I refer you to the detailed amazon review by Lee Carlson.

Misleading title!
A better title for this book would be 'How Blast works' because this book is centered around this topic. If you expect a general overview of statistical methods used in bioinformtics you should buy another book (e.g. Hastie, Baldi, Pevzner, Duda, Eddy which provide more general methods). If you want to know in mathematical detail how blast works, this is your book. I think the level is advanced and one needs some mathematical background to appreciate it (certainly not to recommend for biologists).

I don't think it is a really bad book but I think it gives a wrong impression of (statistical) methods in bioinformatics. Another reviewer wrote ...This is one of the books I have been waiting for. For a population geneticist who wants to learn bioinformatics, most texts are unacceptable: They present heuristic methods in a cookbook fashion, with little reference to what is going on biologically as well as mathematically....
This is exactly the problem with this book!! Bioinformatics is more machine learning than statistics and more heuristic then exact.

Great all-around review of probability
The book's title says 'Statistical Methods', but all of statistics is derived from probability theory. That's really where Ewens and Grant start, with the best high-density review of probability I know.

The first two chapters cover probabilities of one and many variables, respectively. This includes several topics that other authors equently skip, including conditional and marginal probabilities, probability- and moment-generating functions, a little about entropy, distributions of sums, and extreme value statistics. All that takes about 100 pages. Two later chapters cover statistical inference (parameter estimation, hypothesis testing, and Bayesian techniques), two more cover stochastic processes including Markov models, a short chapter includes hidden Markov models and their training, and another chapter covers sampling techniques: bootstraps, permutation tests and such.

If the book contained only that material, it would still be a valuable review and summary of basic probability. It's way too dense to be a beginner's text. That's OK, those chapters were really intended as a review and as a statement of the terms and notation used in the book's real objectives: models of biological systems.

The chapters on biological applications are interspersed with chapters on basics, so that each application is presented as soon as its elements are covered. Those chapters describe statistical properties of a single DNA or protein string, relationships between two strings, BLAST and its scoring models, mutation modeling, and construction of phylogenetic trees. Coverage of each topic is brief but very dense. A surprising amount of information is packed into each brief chapter, and it's surprisingly readable. Still, these are big topics. Ewens and Grant don't and don't try to present any topic to its full depth. Instead, they give enough discussion that a determined reader can learn the basics, and can understand more advanced discussions of specific topics.

The book does require a determined reader with some background in probability - this shouldn't be anyone's first book, unless you have a very skilled teacher. The prepared and careful reader will be very well rewarded, however. Despite the book's title about statistics and bioinformatics, this is a reference you may use for probability models in any field. It's certainly one that I keep coming back to.

//wiredweird

Disappointing overview
This book is a tremendous disappointment, given other Amazon reviews and the impressive Table of Contents. I picked several topics about which I know something: Likelihoods, P-values, bootstraps. I would have had NO idea about either of these subjects based on the poor delivery in this book. Topics are not well introduced, there are virtually no examples, and the introduction/discussion of most topics is wordy and not informative.

A topic such as the two-sample t-statistic is scattered throughout the book, with the main part not even cited in the index!

Unfortunately there are not a lot of books in the field of Statistics in Bioinformatics. However, I would recommend "The Elements of Statistical Learning" (Hastie et al.) for classifiers etc (Duda and Hart's classic is also good). I would recommend "Biostatistical Analysis" by Zar for a general coverage, and Terry Speed's "stat Labs: Mathematical Statistics ..." which is not comprehensive but has good lab examples with associated statistical analysis.

Pretty good overview
This book is a timely introduction to the mathematical statistics used in computational biology and bioinformatics. The authors have done a superb job in the overview of a subject that students of biology and bioinformatics can rely on for study and for reference. The mathematics is done at an advanced undergraduate level, but the authors are pragmatic in their approach, and interlace the discussion with biological applications immediately after the appropriate mathematical background has been developed. It thus seems appropriateto discuss the quality of the presentation with these applications in mind.

Chapter one begins, appropriately, with an introduction to probability theory, with a consideration of discrete probability distributions of one variable beginning the chapter. The Bernoulli, binomial, uniform, geometric, generalized geometric, and Poisson distributions are discussed. The authors point out the use of geometric-like distributions in the BLAST application. The also caution the reader as to the difference between the mean and the average of a random variable. They then move on to consider continuous distributions, discussing briefly the uniform, Normal, exponential, gamma, and beta distributions. Moment-generating functions are also introduced, and they prove a "convexity" theorem for these functions that is important in the BLAST application. The authors also introduce the relative entropy and generalized support statistics, the later also being used in BLAST.

The next chapter is an overview of probability theory in many random variables. The results in chapter one are discussed in this context, and the authors give an interesting application to the sequencing of EST libraries. The authors also point out that the variance of the maximum of a collection random variables is finite as the number of variables increases, a fact that is used quite often in bioinformatics. Transformations of random variables are also discussed, with the goal of showing how these can be used to find the density function of a single random variable, this also being important in BLAST.

The most important subject of the book begins in chapter 3, wherein the authors introduce statistical inference. They begin with a very brief discussion of the differences between the frequentist and Bayesian approaches to statistical inference and then move on to classical hypothesis testing and nonparametric tests. This chapter is of great value to those readers, for example biologists/would-be bioinformaticists who are approaching statistics for the first time.

Chapter 4 introduces concepts that are of upmost importance in probabilistic computational biology, namely Markov chains. The discussion in this chapter sets up the strategies used in the next chapter on analyzing a single DNA sequence and a latter chapter on hidden Markov models. Shotgun sequencing is discussed as a tool to determine the an actual DNA sequence, and the authors discuss the probabilistic issues that arise in the reconstruction of long DNA sequences from shorter sequences. Missing in this chapter is a mathematical analysis of the advantages/disadvantages between shotgun and whole genome sequencing strategies.

Chapter 6 then generalizes the analysis of chapter 5 to multiple DNA and protein sequences. It is here that one begins to talk about alignments between sequences, which bring about some very subtle mathematical problems in computational biology. The computational complexity of the (global) alignment problem entails the use of softer techniques, such as dynamic programming, which is discussed in this chapter. The (local) alignment problem is also discussed in some detail, using the linear gap model. The alignment problem and the issues with scoring for protein sequences are also discussed in detail. The reader first encounters the famous PAM and BLOSUM matrices in this chapter. The authors do not discuss any connections with the protein folding problem, unfortunately.

The next chapter introduces the basic probability theory behind the BLAST algorithm, namely random walks. They do so with emphasis on moment generating functions, which might be a little abstract for the biologist reader.

The authors return to tatistical estimation and hypothesis testing in chapter 8, with maximum liklihood and fixed sample size tests discussed in some detail. Again connecting with the BLAST algorithm, the sequential probability ratio test is treated.

The authors finally get down to the BLAST algorithm in chapter 9, using an older version of the software (1.4). The connection of the algorithm with random walks and how to assign scores is immediately apparent, as is the ability of BLAST to do database queries against a chosen sequence. The algorithm is compared with the sequential analysis discussed in the last chapter.

The authors return to Markov chains in chapter 10, and give some numerical examples. In addition, they treat the important topic of Markov chain Monte Carlo via the Hastings-Metropolis algorithm, Gibbs sampling, and simulated annealing. An application of simulated annealing to the double digest problem is described. The authors also spend a litte time discussing continuous-time Markov chains.

Hidden Markov models are finally discussed in chapter 11. These have been the most effective tools in sequence analysis and the authors give a nice overview of their construction and properties in this chapter. The Pfam package is discussed as a software implementation of HMMs for determining protein domains. Unfortunately, they do not discuss the excellent package HMMER for implementing HMMs in sequence analysis.

Chapter 12 discusses computationally intensive methods in classical inference. One of these methods, the bootstrap procedure, which is used for large sample sizes, is described. Used to estimate confidence intervals in situations where there is not enough information to employ classical methods, the authors detail a method using quantiles to estimate the confidence interval for the standard deviation of the expression intensity of a gene. This is followed by a return to the multiple testing problem of chapter 3 in the context of the data analysis of expression arrays.

I did not read the last two chapters on evolutionary models and phylogenetic tree estimation so I will omit their review. ... Read more

Product Description
Bioinformatics, Biocomputing and Perl presents a modern introduction to bioinformatics computing skills and practice. Structuring its presentation around four main areas of study, this book covers the skills vital to the day-to-day activities of today’s bioinformatician. Each chapter contains a series of maxims designed to highlight key points and there are exercises to supplement and cement the introduced material. 

Working with Perl presents an extended tutorial introduction to programming through Perl, the premier programming technology of the bioinformatics community. Even though no previous programming experience is assumed, completing the tutorial equips the reader with the ability to produce powerful custom programs with ease.

Working with Data applies the programming skills acquired to processing a variety of bioinformatics data. In addition to advice on working with important data stores such as the Protein DataBank, SWISS-PROT, EMBL and the GenBank, considerable discussion is devoted to using bioinformatics data to populate relational database systems.  The popular MySQL database is used in all examples.

Working with the Web presents a discussion of the Web-based technologies that allow the bioinformatics researcher to publish both data and applications on the Internet.

Working with Applications shifts gear from creating custom programs to using them. The tools described include Clustal-W, EMBOSS, STRIDE, BLAST and Xmgrace. An introduction to the important Bioperl Project concludes this chapter and rounds off the book.

... Read more

Customer Reviews (2)

Bioinformatics Biocomputing and Perl is a reasonable tutorial to Perl.
I like this book as a tutorial. I am teaching myself Perl with this book. The chapter examples are good practice and the exercises at the end of the chapters are reasonable. I am about 1/4 through the book, and so far am enjoying the learning process.

Worst bioinformatics book I have read
I have been programming and working as a biologist for the past 6 years, but I have had only a small exposure to Perl.When I read this book description, I was excited since it indicated that Perl would be taught from the ground up and from the bioinformatics perspective.While the perspective is as advertised, this is still a terrible book.Unless you know something about Perl (and programming in general) before you begin, you will be lost.The authors organize some material well, but often relevant items are completely missing.They almost completely abandon Windows users when it would only take a few more sentences to address the difference between Unix and Windows.The end of chapter exercises are poorly thought out and do not provide sufficient practice for the novice.Frequently I found myself referring to "Beginning Perl for Bioinformatics" to make sense of the Moorhouse and Barry book. ... Read more

Product Description

Product Description

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 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

• Imparts a thorough grounding of core concepts, enabling the reader to understand contemporary work within an optimal context

• Includes examples, definitions, problems and a comprehensive and useful bibliography

• Offers additional chapters on algorithms and knowledge representation, including text mining [NEW]

• Presents new experimental methods, and serves as a springboard for new research [NEW]

• Contains a greatly expanded chapter on interactions and regulatory networks [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.