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.

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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

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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.

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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

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The second edition of Instant Notes in Bioinformatics introduced the readers to the themes and terminology of bioinformatics. It is divided into three parts: the first being an introduction to bioinformatics in biology; the second covering the physical, mathematical, statistical and computational basis of bioinformatics, using biological examples wherever possible; the third describing applications, giving specific detail and including data standards. The applications covered are sequence analysis and annotation, transcriptomics, proteomics, metabolite study, supramolecular organization, systems biology and the integration of-omic data, physiology, image analysis, and text analysis.

Customer Reviews (1)

Concise primer. Not bad.
If you want a concise primer on bioinformatics, then this book may be of interest. I read this book as a review, but it seems that it may serve well for newcomers alike.

Compared to other primers such as "Developing Bioinformatics Computer Skills", this book contains less unnecessasary figures (e.g., central dogma, etc.), covers wider range of topics, tries to be less verbose.

A drawback is that there is little description at an algorithmic level (e.g., dynamic programming). However, the book does a pretty good job in conveying the main ideas about what such algorithms do and why they are needed. I like this book's concise and accurate presentation style much better than lengthy and confusing style found in many other books (e.g., Bioinformatics - David Mount). Another drawback is that font is small.

Overall, this book is not bad. I think this book's preface tells you what you can expect from this book, so below I excerpted a paragraph.

"We will tell you how to do things, but this is not a software manual for commonly used packages. They have their own manuals that are (mostly) much better than anything we could provide. Many of the methods we describe rely on quite complex mathematical, statistical or computational techniques. Often we choose not to describe these at all, but where we do we have aimed for a simple conceptual understanding." ... Read more

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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

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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
Introduction to Bioinformatics: A Theoretical and Practical Approach was written as an introductory text for the undergraduate, graduate, or professional. This text provides scientists with both a biological framework to understand the questions life scientist confront in the context of the computational issues and tools that are currently available for scientific research It also provides the life scientist with a resource to the various computational tools that are available all supported with their underlying mathematical foundations. The book is divided into four main sections. The first two sections provide an overview of the various biological processes that govern an organism and impact health. The first section, Biochemistry, Cell and Molecular Biology, describes basic cellular structure and the decoding of the genome. The second section, Molecular Genetics covers the regulation of genomes and the molecular genetic basis of disease as a consequence of genetic replication. Clinical human genetics and the various clinical databases are also reviewed. The third section, the Unix Operating System, demystifies the Unix system used throughout the world to support advanced computation tools. In addition to information on the installation and management of Unix-based software tools, examples of command line sequence analyses are presented that will enable the research to become as comfortable in a command-line environment as they are in the Graphical-User Interface environment. The final section, Computer Applications, provides information on the management and analysis of DNA sequencing projects, along with a review of how DNA can be modeled as a statistical series of patterns. It follows with a discussion of the various genome databases, the representation of genomes, and methods for their large scale analyses. Protein visualization, and transcription profiling including the use of analysis software for systems biology round out the coverage. The volume also includes a bonus CD-ROM containing valuable software programs including BioDiscovery (for microarray analysis), ClustalX (a sequence alignment program) Ensembl, MicroAnalyser (for microarray analysis on the Macintosh), Staden Sequence Analysis Package, Tree View (for displaying phylogenies) an others. Also included is a complete set of color illustrations from each chapter that will prove invaluable for professors preparing their next bioinformatics course or seminar. ... 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

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.

Product Description
Essential Bioinformatics is a concise yet comprehensive textbook of bioinformatics, which provides a broad introduction to the entire field. Written specifically for a life science audience, the basics of bioinformatics are explained, followed by discussions of the state-of-the-art computational tools available to solve biological research problems. All key areas of bioinformatics are covered including biological databases, sequence alignment, genes and promoter prediction, molecular phylogenetics, structural bioinformatics, genomics and proteomics. The book emphasizes how computational methods work and compares the strengths and weaknesses of different methods. This balanced yet easily accessible text will be invaluable to students who do not have sophisticated computational backgrounds. Technical details of computational algorithms are explained with a minimum use of mathematical formulae; graphical illustrations are used in their place to aid understanding. The effective synthesis of existing literature as well as in-depth and up-to-date coverage of all key topics in bioinformatics make this an ideal textbook for all bioinformatics courses taken by life science students and for researchers wishing to develop their knowledge of bioinformatics to facilitate their own research. ... Read more

Customer Reviews (5)

amazone great!
the book is very good. The shipping was very fast that I was able to complete my homework on time. If I were you I would rather buy this book through amazon than through the bookstore where it is much much more expensive!

Very simple
I used this book for a bioinformatics class and I also worked as a biotechnology programmer for around 8 years. It is a simple well written introduction to some topics in bioinformatics. But it not high level enough to use a reference for my work; but a introduction is probably what the author intended.

Good bookfor beginners
The book is written in an easy and concise way. It is a very useful book for beginners. If the reader knows the basics, he needs a more advanced book.

Essential Bioinformatics for Life Scientists.
This compact, economical book (at least for bioinformatics) covers the usual basics of bioinformatics (Databases, alignments, phylogeny, gene prediction, structure prediction, transcriptome analysis, proteome analysis) but is unique in its approach.Recognizing most life scientists need to understand basic bioinformatis, but lack extensive mathematical modeling, computer command line or programming experience Jin Xiong has written a text that describes common bioinformatics tools to perform each of the above studies. Using diagrams and figures in lieu of complex mathematical formulas, Xiong explains how the tools work.Each task in bioinformatics has many comoputing tools - the strengths and weaknesses of each, and guidance in critical evaluation of the output are explained.There are capstone problems at the end of the book that are extremely helpful in enhancing understanding of the tools. The text is easy to read.

In the preface. Xiong describes that the book is a compilation of notes from several years of teaching bioinformatics. Therefore they presumably have been revised based on student review.However, this is a first edition -there are a lot of typos, misspellings, and some figures have errors.Hopefully these will be fixed for this is a fine introductory book.

The text is for those new to bioinformatics. Unlike many bioinformatics books, there is no coverage of programming (PERL or SQL for ex,).Therefore, those who are already skilled in this area will likely not find this particularly useful.Familiarity with the UNIX operating system will help readers do the problems.

Good Introductory Book for the Student or Researcher
The author gives a pretty good summary of this book in the preface: 'I needed a text that was comprehensive enough to cover all major aspects in the field [bioinformatics], technical enough for a college level course, and sufficiently up to date to include most current algorithms while at the same time being logical and easy to understand... The book is aimed at graduate and undergraduate students in biology, or any practicing molecular biologise, who has no background in computer algorithms but wishes to understand the fundamental principles of bioinformatics and use this knowledge to tackle his or her own research problems.'

The book was developed over several years, first being issued in the form of Xerox'd lecture notes to test the acceptability by students. Subsequently the notes were revised, expanded and now assembled into book form.

There are now a large number of standard software packages designed for use in the bioinformatics area. Many of these are discussed. However, it is not intended for this book to be a manual on these packages. Instead it discusses the software from a standpoint of when and where specific packages can be used to solve your problem of the moment.

As a field, bioinformatics is expanding and developing at an extremely rapid rate. This book is up to date as of early 2006. ... Read more

Product Description
Praise for Digital Code of Life

"The man who wrote the best history yet of the open-source movement in Rebel Code gives us an equally lucid and penetrating look at bioinformatics. Well done!"
–Eric S. Raymond
Author of The Cathedral and the Bazaar

"This book provides a riveting account of the history of bioinformatics and of the manner in which bioinformatics has contributed to advancing our knowledge of the human genome. Glyn Moody has chronicled through reviews of key scientific papers and through interviews with leading scientists, the major developments in the field of genomics in the past half century, from the discovery of the double helix to the emergence of proteomics, pointing to their relevance to science, medicine, and industry and to the critical contributions of bioinformatics."
–Sam Hanash, University of Michigan
President of The Human Proteome Organisation ... Read more

Customer Reviews (3)

Worth Reading
As the other reviewer notes, this book does what it's meant to do: give you an overview of the myriad developments in bioinformatics since its inception.It's fairly engaging, though, for me, that's mostly due to the subject matter itself and not the writer's abilities.This is why I gave it four stars instead of five.

The scientific explanations are usually not that great, even if the concepts aren't that difficult to understand given some understanding of the underlying biological concepts.I had to quit reading and go in Wikipedia to understand some of these concepts because I felt the author's explanations were just unnecessarily confusing.And the author often decides to jump from the narrative and devote a page to the science, which isn't a horrible thing to do, but I feel maybe the science could have either been explained more succinctly or integrated with the narrative better.So that, along with the worst proofreading I've encountered in a published book (multiple instances of missing words [like 'a' or 'the'], missing punctuation [periods, parentheses], inconsistent punctuation, etc.), prompts me to give a four-star rating.

That said, it's certainly worth reading; and, from what I've surmised, it's the only published book on the history of bioinformatics (that isn't solely concerned with the Human Genome Project), so it's not like there are (m)any alternatives.

I'll also note that even though this was published a few years ago, it feels slightly outdated already due to the perpetual advances in throughput and methodology in the field.A new edition (at least with an afterword explaining recent advances) would be nice.

A delightful reading
It's been a while since I read this book. So I try to get back on my impressions.
The book has a good sketch on the key developments of modern genomics and bioinformatics, full of gossips and vivid stories. It is a very difficult job to write a history or something close to that for a fast evolving field. And there are limited accounts on the business side too. To her credit, the author has done an excellent job.
I had concerns over the accuracy and coverage of some contents and opinions. But given the breadth of the book, probably this is how good it can get. Other than that, it was a very interesting reading. I recommended it to a friend right the way.

Excellent Laymen's overview
Digital Code of Life is an excellent overview of the convergence of IT and life sciences which has occurred nearly overnight.In the space of a few short years, the human genetic code has been mapped, and we now are seeing how this will play out for healthcare, drug development, and the marketplace.If you are interested in getting a sense of what is behind all the headlines, it's a worthwhile read. ... Read more

Product Description
Lucidly Integrates Current Activities

Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other.

Examines Connections between Machine Learning & Bioinformatics

The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website.

Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems

Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments. ... Read more

Product Description
An introduction to machine learning methods and their applications to problems in bioinformatics

Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization.

From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more.

Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels. ... Read more

Product Description

Confused by cryptic computer programs, algorithms and formulae?

In this book, anyone who can operate a PC, standard software and the Internet will learn to understand the biological basis of bioinformatics of the existence as well as the source and availability of bioinformatics software how to apply these tools and interpret results with confidence.

This is aided by introductory chapters to important aspects of bioinformatics, detailed bioinformatics exercises, including solutions and a glossary of definitions and terminology relating to bioinformatics.

Quickly learn to manage bioinformatics!

Product Description
An invaluable tool in Bioinformatics, this unique volume provides both theoretical and experimental results, and describes basic principles of computational intelligence and pattern analysis while deepening the reader's understanding of the ways in which these principles can be used for analyzing biological data in an efficient manner.

This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful information and insight from it. Biological data for analysis include sequence data, secondary and tertiary structure data, and microarray data. These data types are complex and advanced methods are required, including the use of domain-specific knowledge for reducing search space, dealing with uncertainty, partial truth and imprecision, efficient linear and/or sub-linear scalability, incremental approaches to knowledge discovery, and increased level and intelligence of interactivity with human experts and decision makers

• Chapters authored by leading researchers in CI in biology informatics.
• Covers highly relevant topics: rational drug design; analysis of microRNAs and their involvement in human diseases.
• Supplementary material included: program code and relevant data sets correspond to chapters.

Product Description
Exploring Bioinformatics: A Project-Based Approach is intended for an introductory course in bioinformatics at the undergraduate level. Through hands-on projects, students are introduced to current biological problems and then explore and develop bioinformatic solutions to these issues. Each chapter presents a key problem, provides basic biological concepts, introduces computational techniques to address the problem, and guides students through the use of existing web-based tools and existing software solutions. This progression prepares students to tackle the On-Your-Own Project, where they develop their own software solutions. Topics such as antibiotic resistance, genetic disease, and genome sequencing provide context and relevance to capture student interest. ... Read more

Customer Reviews (1)

Not worth the price
This book is completely not worth the price in any way shape or form. I had to use this book for a semester and this book caused the most pain I have ever had in any class. Let's start with the print quality. You would think that a book with this price would have the best print quality, you would be wrong. Try black and green, no color images at all. This wouldn't be such an issue if there weren't parts of the book that refer to charts with colors like yellow, red, and blue. This means that the text of the book tells you to look at charts in parts of the book you simply can't see. This book screams cheap in design and the rest of the book supports the impression.

It gets worst, the content of the chapters is simply terrible. Lets start at the beginning, the book claims that it is suitable for beginners it is not. The first chapter starts with a simple hello word program in perl, simple enough. That is the extent of basic perl that you are taught. Seriously the gulf in perl knowledge needed from chapter one to two is massive. You are taught nothing about perl before you are expected to use quite a lot perl. I am talking about a class of twenty which was completely lost in the transition from chapter one to two. For a book to be so badly designed for that many people to get lost, it says something. The book has more lapses in logical progression.

One of the book features is an on your section at the end of each chapter which is supposed to practice your skills that you learned in the chapter. It is completely useless. The end of chapter section actually requires knowledge that isn't taught until latter in the book to solve. For example, one of the hints to solve the problem was recursion, which is a fairly advanced technique, it wasn't taught until several chapters later in the book, but was needed at that point in the book. Even better the explanation on how to do it was beyond substandard, and it lacked a useful code example, which made learning how to do recursion in this book impossible. In general a lot of the code examples are lacking. One of the things that my instructor told me was to comment on every line of my code, otherwise it would be difficult for another person to follow. The book doesn't do this. A lot of the code in the book lack any type of meaningful comments making understand the examples very difficult, not very conductive to learning.

In all this book is not worth the money and is not suitable for any type of instruction.
... Read more

Product Description
Alternative techniques and tools for analyzing biomolecular networks

With the recent rapid advances in molecular biology, high-throughput experimental methods have resulted in enormous amounts of data that can be used to study biomolecular networks in living organisms. With this development has come recognition of the fact that a complicated living organism cannot be fully understood by merely analyzing individual components. Rather, it is the interactions of components or biomolecular networks that are ultimately responsible for an organism's form and function. This book addresses the important need for a new set of computational tools to reveal essential biological mechanisms from a systems biology approach.

Readers will get comprehensive coverage of analyzing biomolecular networks in cellular systems based on available experimental data with an emphasis on the aspects of network, system, integration, and engineering. Each topic is treated in depth with specific biological problems and novel computational methods:

• GENE NETWORKS—Transcriptional regulation; reconstruction of gene regulatory networks; and inference of transcriptional regulatory networks

• PROTEIN INTERACTION NETWORKS—Prediction of protein-protein interactions; topological structure of biomolecular networks; alignment of biomolecular networks; and network-based prediction of protein function

• METABOLIC NETWORKS AND SIGNALING NETWORKS—Analysis, reconstruction, and applications of metabolic networks; modeling and inference of signaling networks; and other topics and new trends

In addition to theoretical results and methods, many computational software tools are referenced and available from the authors' Web sites. Biomolecular Networks is an indispensable reference for researchers and graduate students in bioinformatics, computational biology, systems biology, computer science, and applied mathematics. ... Read more