Product Description

One of the most pressing tasks in biotechnology today is to unlock the function of each of the thousands of new genes identified every day. Scientists do this by analyzing and interpreting proteins, which are considered the task force of a gene. This single source reference covers all aspects of proteins, explaining fundamentals, synthesizing the latest literature, and demonstrating the most important bioinformatics tools available today for protein analysis, interpretation and prediction. Students and researchers of biotechnology, bioinformatics, proteomics, protein engineering, biophysics, computational biology, molecular modeling, and drug design will find this a ready reference for staying current and productive in this fast evolving interdisciplinary field.

Product Description

XML for Bioinformatics aims to provide biologists, software engineers, and bioinformatics professionals with a comprehensive introduction to XML and current XML applications in bioinformatics. The book will assume no background in XML, and take readers from basic to intermediate XML concepts. Core topics will include: fundamentals of XML, creating XML grammars, web services via SOAP, and parsing XML documents in Perl and Java.

Customer Reviews (1)

Nice Brief Overview
This is a nice brief introductory overview of xml in general and its implementations in biological research. The thing I liked about it was it was not too intensive on any topic, and it provided a lot of useful resources both online and literature-wise for me to look up for more information.

It definitely sparked my interest in certain areas of xml and how Imay apply it towards my research. One of the things that I really liked was that the author always spelled out all the mentioned acronyms (thank you, finally someone exists out there that does this!). In addition, I also appreciated that the author did not get too bogged down in anyone topic resulting in a bible-like introductory work - thank you again for that. As a result of this I was literally able to read it in few days and determine the usefulness of this technology called `xml`; and without having to read 1k pages to determine this. In this case 'Less is sometimes definitely more'.

My favorite chapter was the one covering Web Services and now understand the basic concepts behind XML-RPC, REST, and SOAP.
The only reason I give it 4 stars is a.)its pricey for a grad student and b.)I use Perl, and he has a two or so chapters using Java to parse xml. So those chapters were not useful to me. But hey you can't please everyone :)

Overall, it helped me recognize just what xml is, how it may be used, who uses it (esp. in biology) - and how they use it, and a list of references for more info.Definitely a good little book to check out for basic conceptual understanding. I want to say its almost a survey, but its actually a little more than that. ... Read more

Product Description
Completely revised and updated, this new edition of a bestseller will continue to serve as a tool that allows researchers to easily access and use databases to gather information on genes, proteins, and diseases. It covers new approaches to data analysis using databases around the world. It identifies computing techniques that help readers manage the massive flow of information. The structure of this second edition will follow that of the first and its content will reflect the growth in information. New sections cover recent developments in genome projects, microarray, proteonics, brain mapping, and more. ... Read more

Customer Reviews (5)

I liked it but it could use more sample problems
I wanted to learn something about informatics...The book was easy to understand but it lacked practice problems... This is not a religious book...Obviously the book takes an evolutionary stand point since the entire concept of bioinformatics is based mainly on evolutionary similarities of proteins and other cellular components. If you are new to bioinformatics but have some understanding of Biology, then you should get this book...

An expensive hodgepodge
Books about bioinformatics are as diverse as the field itself. It may not be possible to do justice to this new and exciting field in 168 pages. My concern with this book, however, lies not with the choice of topics, but rather with its incoherent presentation. Distracting parenthetical remarks pervade the text as in this excerpt from the beginning of the 3rd chapter:"DNA sequences are often found based on predictive tools, meaning that sequence similarities of newly discovered genes yield information about their physiological functions and structures, PCR (polymerase chain reaction), finding sequence fragments of significance (how is significance judged? By predictive biology, once again), finding distribution of genes or mRNAs (often an indicator of gene activity in an organism), and amplifying the amount of DNA for purification, sequencing, and mutational analysis." (p. 143).

Chapter 5 entitled "The Computer Revolution in Neurobiology" we find the following unexpected text: "One rationale for human cloning is that it could alleviate many of the problems associated with our unique characteristics. The genetic information (DNA) of a cloned organ is identical to that of the individual needing the organ. Hence, the cloned organ would be accepted by the individual's body as part of itself and would have a smaller probability of rejection." (p. 159). [Nevermind the enormous ethical implications!]

Throughout, one has the sense of a book rushed to market to take advantage of an exciting scientific trend. The book presents nothing that can't be found on-line and it gives readers no sense of the computational challenges of the field. ...

I own this title in my library and is well worth the money!
I am a novice in bioinformatics and have found this book to be invaluable in getting me started without intimidating me. I have owned the book for over a year and still find it very useful but I am waiting for an update to this title soon since I have enjoyed the organization of the book. It has given me a global picture of informatics and chapters with specific examples and applications. I am sure a second edition to this book will be well recieved by the audience and I am hoping to see it soon since the new bioinformatics book I have just purchased is not to my liking.

Solid starting place for a novice
I'm always looking for a good guide book for undergrads and biocomputing novices that covers the basics without getting carried away with algorithmic minutiae. I found this book a fair place to start for those who have no idea of what is out there. The tools and Web sites described are mainstays and likely won't be going anywhere. However, if you are already moderately knowledgeable, you will exhaust this book immediately. The nature of the field is constant movement toward newer and more thorough tools. This is not a users' guide: go to the referenced Web sites themselves to get the most up to date, accurate information.

I would refer a naive beginner to get this book at the library, but I wouldn't put it on my own shelf.

Bioinformatics to the max!
This is truly a great book.I had always wondered about biological research and the use of computers.The future of medicine and mankind will be changed drastically by new advances created by the coupling of scienceand technology.Bioinformatics is the wave of the future and Rashidi showsin his book that he is far ahead of his time.He has written the kind ofbook, a "gold standard" if you will, upon which all other textsin the field shall be judged.If I were taking a class which covered thesubject of bioinformatics, I would definitely want this to be my resourcefor information.Clear, concise and unpretentious, Rashidi lays everythingout vividly so that anyone from the undergraduate to the post-graduatestudent may marvel at the wonders of bioinformatics.My only complaint isthat I think it should have been titled "The Bible ofBioinformatics" because its wealth of information is so epic andcomplete.There is no need to look for another book.Rashidi's has itall! ... Read more

Product Description

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

Customer Reviews (3)

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

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

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

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

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

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

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

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

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

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

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

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

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

Product Description

Bioinformatics is growing by leaps and bounds; theories/algorithms/statistical techniques are constantly evolving. Nevertheless, a core body of algorithmic ideas have emerged and researchers are beginning to adopt a "problem solving" approach to bioinformatics, wherein they use solutions to well-abstracted problems as building blocks to solve larger scope problems.

The Problem Solving Handbook for Computational Biology and Bioinformatics is an edited volume contributed by world renowned leaders in this field. This comprehensive handbook with problem solving emphasis, covers all relevant areas of computational biology and bioinformatics. Web resources and related themes are highlighted at every opportunity in this central easy-to-read reference.

Designed for advanced-level students, researchers and professors in computer science and bioengineering as a reference or secondary text, this handbook is also suitable for professionals working in this industry.

... Read more

Product Description
Stefanie Hartmann and Joachim Selbig teach bioinformatics courses at the University of Potsdam. For their undergraduate course they wanted a textbook that introduced students to the field of bioinformatics, with a focus on the analysis of sequences and structures. Because they use the Linux operating system and the statistics software R for the computer exercises, they also wanted an introductory text on these topics. Since the authors couldn't find a book that contained the exact mix of things they are teaching in their course, they decided to write their own. Their book also contains computer exercises, the data for which can be downloaded from the companion web-site. ... Read more

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

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

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

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

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

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

• Discusses systems biology approaches and applications

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

Product Description
The goal of this book is to help readers understand state-of-the-art techniques in biological data mining & data management & includes topics such as: * preprocessing tasks such as data cleaning & data integration as applied to biological data * classification & clustering techniques for microarrays * comparison of RNA structures based on string properties & energetics * discovery of the sequence characteristics of different parts of the genome * mining of haplotypes to find disease markers * sequencing of events leading to the folding of a protein * inference of the subcellular location of protein activity * classification of chemical compounds based on structure * special purpose metrics & index structures for phylogenetic applications * a new query language for protein searching based on the shape of proteins * very fast indexing schemes for sequences & pathways Aimed at computer scientists, necessary biology is explained. ... Read more

Product Description
Recent advances in biotechnology, spurred by the Human Genome Project, have resulted in the accumulation of vast amounts of new data. Ontologies—computer-readable, precise formulations of concepts (and the relationship among them) in a given field—are a critical framework for coping with the exponential growth of valuable biological data generated by high-output technologies. This book introduces the key concepts and applications of ontologies and ontology languages in bioinformatics and will be an essential guide for bioinformaticists, computer scientists, and life science researchers.

The three parts of Ontologies for Bioinformatics ask, and answer, three pivotal questions: what ontologies are; how ontologies are used; and what ontologies could be (which focuses on how ontologies could be used for reasoning with uncertainty). The authors first introduce the notion of an ontology, from hierarchically organized ontologies to more general network organizations, and survey the best-known ontologies in biology and medicine. They show how to construct and use ontologies, classifying uses into three categories: querying, viewing, and transforming data to serve diverse purposes. Contrasting deductive, or Boolean, logic with inductive reasoning, they describe the goal of a synthesis that supports both styles of reasoning. They discuss Bayesian networks as a way of expressing uncertainty, describe data fusion, and propose that the World Wide Web can be extended to support reasoning with uncertainty. They call this inductive reasoning web the Bayesian web. ... Read more

Customer Reviews (3)

Very lucid explanations
One of the most clearly written books I have found on the topic. I agree with a previous reviewer that some areas were not covered, but the ones that were are well described.

I would love to see a follow-on that reviews major ontology works in progress and explains when and how to use them and how to effectively map from one domain or specialty to another.

Excellent book of how we apply Ontology into real life application.
I have been working on Model Driven Architecture, Meta-data driven software, Modeling tools and Ontology for over 4 years. I have also implemented a suite of software supporting MDA and Ontologies. The material that this books covered just speaks my words out. Now, I can easily articulate my MDA/Ontology problems out with this book. Life is so easy with the help of a book which is in line of my work. Hooray!

Title can mislead: Greater focus on methods than content of ontologies
I was disappointed in this book in the lack of depth or breadth on a couple of key areas as follows:

Item 1 - It appeared to me that ontologies were not explained in sufficient detail to help "newbies" determine exactly why a specific ontology was created and the specific functions it is designed to support.I'm considering the UMLS to prototype a search engine because it is a compilation of a number of ontologies.However, this provides numerous options for subsetting the UMLS; for those relatively new to these ontologies it is not clear which ontology subsets are the most important (i.e., which play a pivotal or lesser role for my intended use).For example, I can include SNOMED and HL7 subsets, but do they provide redundant concepts for my needs and if so, in which areas?It is not practical for me to learn then all, so I was hoping to obtain this information from this book.Unfortunately I was unable to do so quickly and have to dig more on my own (which was the reason to buy the book.....).

Item 2 - I noticed a couple of areas that may be missing for those more interested in the tools and process of using ontologies.For example, I did not see the Protege and LexGrid tools for visualization, from Stanford and Mayo Clinic respectively, in the index (I had located them in prior web search).In a more general sense, as I've been working with ontologies more and more, I've noticed that platform selection and interoperability seem to be stumbling blocks.This topic was not a major focus in the book.

Due to the breadth and complexity of the platforms and tools needed to effectively use ontologies, and the complexity and size of the ontologies themselves, addressing both of these large subjects in sufficient detail in one book may be very difficult.Perhaps this is the reason that this book did not reach the 5 stars potential.Perhaps it would if it had addressed one or the other, but not both.

Perhaps if the description and title clarified the focus of the book, I would have gotten a better idea of the content before purchasing (and unfortunately, returning).

Keep in mind that I'm relatively new to using ontologies. ... Read more

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

Product Description
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
Univ. College, London, UK. Covers both the traditional approaches including gene and protein sequence analysis and structure prediction, and more recent technologies such as datamining to provide insights on cellular mechanisms.Written specifically for advanced level courses for undergraduates. Softcover. ... Read more

Product Description
Mathematics of Bioinformatics: Theory, Methods, and Applications provides a comprehensive format for connecting and integrating information derived from mathematical methods and applying it to the understanding of biological sequences, structures, and networks. Each chapter is divided into a number of sections based on the bioinformatics topics and related mathematical theory and methods. Each topic of the section is comprised of the following three parts: an introduction to the biological problems in bioinformatics; a presentation of relevant topics of mathematical theory and methods to the bioinformatics problems introduced in the first part; an integrative overview that draws the connections and interfaces between bioinformatics problems/issues and mathematical theory/methods/applications. ... Read more

Product Description
This unique volume presents major developments and trends in bioinformatics and its applications. Comprising high-quality scientific research papers and state-of-the-art survey articles, the book has been divided into three main sections: Sequences, Structures, and Functions; Pathways, Biological Networks, and System Biology; and Microarray Data, Medical Informatics, and Applications. These results of these investigations help the practicing biologist in many ways: in identifying unknown connections, in narrowing down possibilities for a search, in suggesting new hypotheses, designing new experiments, validating existing models or proposing new ones. It is an essential source of reference for researchers and graduate students in bioinformatics, computer science, mathematics, statistics, and biological sciences based on selected papers from the "The International Conference on Bioinformatics and Its Application" (ICBA), held December 16-19, 2004 in Fort Lauderdale, Florida, USA. ... Read more

Customer Reviews (1)

Good Reference for researchers and graduate students
This book is a valuable reference for researchers and Ph.D. students who want to stay on top of today's cutting edge frontiers in applied bioinformatics.
... Read more

Product Description
This book is written by an experienced author team representing the many areas from which the new discipline of Bioinformatics is emerging. Their common sense approach and carefully detailed presentations in Bioinformatics: A Computing Perspective blends computing and biological sciences in an engaging and unique way.

Customer Reviews (2)

Decent introduction, but needs work
Overall, this book serves as a decent springboard into further exploration of topics in bioinformatics. It gives a broad sampling of the terms and basic techniques in the field, making the reader conversant enough to research the details elsewhere. This level of involvement is probably the best approach for an emerging field like bioinformatics, where the rules of the game are hardly written down before they change again.

On the other hand, I had several issues with this book. For one, it seemed like a rough draft in many places. In addition to some copyediting issues and stylistic inconsistencies, a few of the illustrations were poorly constructed and unclear.

The book aims (according to the back cover) to be a "standalone text" focusing on the computing side of bioinformatics, but with relevant background in biology and mathematics. Though it handled computing, math, and statistics fairly well, I felt that the biology side of the material was lacking, and sometimes even off the mark. The authors have a tendency to make analogies to computing whenever they try to explain a biological process. This is not necessarily a bad idea, but they often spend too much time on the analogies rather than the actual explanation, confusing the issue by bogging down the reader in the parts that did not correspond. My recommendation: more (and better) illustrations, and fewer analogies.

Even the computing part had some problems. The exercises were all over the place. Some were trivial and had tenuous relevance ("decode this Morse code message using a table"), while others were large programming projects with latent, difficult graph theory problems. Meanwhile, a few code listings demonstrated horrible programming practices (a state machine implemented with recursive function calls, anyone?). Moreover, they were all written in Java, which is fine in itself--choose a language and stick with it--but the text sometimes referred to Java idiosyncrasies that made discussions inapplicable to other languages.

I do not want to make this book sound worthless. It had many good qualities, and it was certainly valuable as a starting point. Still, I think the authors and the publisher need to consider some major revisions to many of its sections. I hope that there will be further editions that address some of these issues: the book has lots of potential that is mired by its rough edges.

nice delivery!
MY first review. For this newly published book, there is not much to say. If you are familiar with the logrithms and biochemistry, it's a good text book. What I want to say is that this is a very nice shopper, unbilievable fast delivery, and cheapest price for this book in nice condition. ... Read more

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

Customer Reviews (3)

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

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

good undergrad/opening text
Features

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

Focus on fundamentally important algorithms at the core of bioinformatics.

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

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

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

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

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

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

Contents

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

Product Description
The purpose of this book is to give a thorough and systematicintroduction to probabilistic modeling in bioinformatics. The bookcontains a mathematically strict and extensive presentation of thekind of probabilistic models that have turned out to be useful ingenome analysis. Questions of parametric inference, selection betweenmodel families, and various architectures are treated. Severalexamples are given of known architectures (e.g., profile HMM) used ingenome analysis. Audience: This book will be of interest to advancedundergraduate and graduate students with a fairly limited backgroundin probability theory, but otherwise well trained in mathematics andalready familiar with at least some of the techniques of algorithmicsequence analysis. ... Read more

Customer Reviews (4)

An excellent, rigorous exploration of HMMs as used in bioinformatics
"Hidden Markov Models of Bioinformatics" is an excellent exploration of the subject matter: appropriate coverage, well written, and engaging. Hidden Markov Models are a rather broad class of probabilistic models useful for sequential processes. Their use in the modeling and abstraction of motifs in, for example, gene and protein families is a specialization that bears a thorough description, and this book does so very well. This is a book for understanding the theory and core ideas underlying profile HMMs, and if the term Expectation Maximization doesn't sound familiar or interesting to you, this is probably not the book you're looking for. Personally I found it clearer in some ways than the standard reference by Durbin, Eddy, Krogh, and Mitchison, but actually the two complement each other very nicely. If you are interested in constructing an HMM for your favorite protein family you probably want to look at the HMMER or SAM documentation instead; if you want to understand where HMMs come from or how you might architect one, there's probably no better book than this one.

Written by a mathematician for mathematicians
The intended audience of this book are mathematicians. To understand this book, you should have prior coursework experience in at least several upper division undergraduate courses in mathematical statistics and probability theory. The structure of this book is also that of a typical math book; full of proposition, corollary, lemma, etc, and very limited use of illustrations (e.g., there is no single figure up to chapter 6).

I wanted a book with a mathematical sophistication simliar to Durbin's book, but this book is way more than that. On the other hand, I showed this book to a mathematics graduate student and she said this book is perfect for her. So I guess this book is written by a mathematician only for mathematicians.

Good material, but you really have to want it.
The book gives outstanding coverage of all that goes into building HMMs - one of the most important tools in genome analysis and structure prediction. It covers the field in extreme depth. More depth, in fact, than needed for building useful HMM systems. It not only presents the forward and backward algorithms leading up to Baum-Welch, it presents all the extras - convergence, etc.

This additional depth of coverage may go beyond many readers' needs. It is very helpful, though, for people who need more than the usual algorithms. By giving the background in such detail, a persistent reader can follow to a certain point, then create modifications with a clear idea of where the new algorithm actually comes from.

Regarding the current practice of HMM usage, I found it a bit thin. Widely-known tools based on HMMs are mentioned only occasionally and in passing, and HMM-based alignment is discussed only briefly. Well, this book isn't for the tool user. Perhaps more important, I found scant mention of scoring with respect to some background probability model ("null" model, as it's called here).

My one real complaint, and this is truly minor, is the quality of illustration. The line-drawings look like Word pictures - not necessarily a bad thing, if done well. These aren't particularly professional-looking, though, and oddly stretched or squashed in many cases. Still, they're readable enough and make all the needed points.

A lesser point, and not the author's fault, is the editorial implication that this book introduces probabilitic models in general. It does not. This is strictly about HMMs, not Bayesian nets, bootstrap techniques, or any of the dozens of other probabilistic models used in bioinformatics. That is not a flaw of the book, just a flaw in how it's represented.

If you are dedicated to becoming an expert in HMM construction and application, you must have this book. It's a bit much, though, for people who just want the results that HMMs give.

Primarily for bio-mathematicians
The field of computational biology has expanded greatly in the last decade, mainly due to the increasing role of bioinformatics in the genome sequencing projects. This book outlines a particular set of algorithms called hidden Markov models, that are used frequently in genetic sequence search routines. The book is primarily for mathematicians who want to move into bioinformatics, but it could be read by a biologist who has a strong mathematical background. The book is detailed at some places, sparse in others, and reads like a literature survey at times, but many references are given, and there are very interesting exercises at the end of each chapter section. In fact it is really imperative that the reader work some of these exercises, as the author proves some of the results in the main body of the text via the exercises.

Some of the highlights of the book include: 1. An overview of the probability theory to be used in the book. The material is fairly standard, including a review of continuous and discrete random variables, from the measure-theoretic point of view, i.e the author introduces them via a probability space which is set with its sigma field, and a probability measure on this field. The weight matrix or "profile" as it is sometimes called, is defined, this having many applications in bioinformatics. Bayesian learning is also discussed, and the author introduces what he calls the "missing information principle", and is fundamental to the probabilistic modeling of biological sequences. Applications of probability theory to DNA analysis are discussed, includingshotgun assembly and the distribution of fragment lengths from restriction digests. A collection of interesting exercises is included at the end of the chapter, particularly the one on the null model for pairwise alignments. 2. An introduction to information theory and the relative entropy or "Kullback distance", the latter of which is used to learn sequence models from data. The author defines the mutual information between two probability distributions and the entropy, and calculates the latter for random DNA. He also proves some of the Shannon source coding theorems, one being the convergence to the entropy for independent, identically distributed random variables. The Kullback distance is then defined, as a distance between probability distributions, with the caution that it is not a metric because of lack of symmetry. 3. The overview of probabilistic learning theory, where 'learning from data' is defined as the process of inferring a general principle from observations of instances. 4.The very detailed treatment of the EM algorithm, including the discussion of a model for fragments with motifs. 5. The discussion of alignment and scoring, especially that of global similarity. Local alignment is treated in the exercises. 6. The discussion of the learning of Markov chains via Bayesian modeling applied to a training sequence via a family of Markov models. Frame dependent Markov chains are discussed in the context of Markovian models for DNA sequences. 7. The discussion of influence diagrams and nonstandard hidden Markov models, in particular the excellent diagrams drawn to illustrate the main properties, and excellent discussion is given of an "HMM with duration" in the context of the functional units of a eukaryotic gene. This is important in the GeneMark:hmm software available. 8. The treatment of motif-based HMM, in particular the discussion of the approximate common substring problem. 9. The discussion of the "quasi-stationary" property of some chains and the connection with the "Yaglom limit". 10. The treatment of Derin's formula for the smoothing posterior probability of a standard HMM. The author shows in detail that the probability of a finite length emitted sequence conditioned on a state sequence of the HMM depends only on a subsequence of the state sequence. 11. The treatment of the lumping of Markov chains, i.e. the question as to whether a function of a Markov chain is another Markov chain. 12. The very detailed treatment of the Forward-Backward algorithm and the Viterbi algorithm. 13. The discussion of the learning problem via the quasi-log likelihood function for HMM. 14. The discussion of the limit points for the Baum-Welch algorithm. Since the Baum-Welch algorithm deals with iterations of a map, its convergence can be proved by finding the fixed points of this map. These fixed points are in fact the stationary points of the likelihood function and can be related to the convergence of the algorithm via the Zangwill theory of algorithms. Unfortunately the author does not give the details of the Zangwill theory, but instead delegates it to the references (via an exercise). The Zangwill theory can be discussed in the context of nonlinear programming, with generalizations of it occurring in the field of nonlinear functional analysis. It might be interesting to investigate whether the properties of hidden Markov models, especially their rigorous statistical properties, can all be discussed in the context of nonlinear functional analysis. ... Read more

Product Description

Proteins lie at the heart of almost all biological processes and have an incredibly wide range of activities. Central to the function of all proteins is their ability to adopt, stably or sometimes transiently, structures that allow for interaction with other molecules. An understanding of the structure of a protein can therefore lead us to a much improved picture of its molecular function. This realisation has been a prime motivation of recent Structural Genomics projects, involving large-scale experimental determination of protein structures, often those of proteins about which little is known of function. These initiatives have, in turn, stimulated the massive development of novel methods for prediction of protein function from structure. Since model structures may also take advantage of new function prediction algorithms, the first part of the book deals with the various ways in which protein structures may be predicted or inferred, including specific treatment of membrane and intrinsically disordered proteins. A detailed consideration of current structure-based function prediction methodologies forms the second part of this book, which concludes with two chapters, focusing specifically on case studies, designed to illustrate the real-world application of these methods. With bang up-to-date texts from world experts, and abundant links to publicly available resources, this book will be invaluable to anyone who studies proteins and the endlessly fascinating relationship between their structure and function.

Product Description
This first introductory book designed to train novice programmers is based on a student course taught by the author, and has been optimized for biology students without previous experience in programming.
By interspersing theory chapters with numerous small and large programming exercises, the author quickly shows readers how to do their own programming, and throughout uses anecdotes and real-life examples from the biosciences to 'spice up' the text.
This practical book thus teaches essential programming skills for life scientists who want -- or need -- to write their own bioinformatics software tools. ... Read more

Customer Reviews (5)

incomplete book,inpracticle for programming, lack of real stuffs
This book is a mess. It seems to be an unfinished book with many chapters absent. The organization of this book is poor. It has too much verbose and useless opinions without hitting the points. In a word, this book doesn't have many real contents. It's not worthy to buy this book.

Better book than reviews suggest
OK. So, this book is not a typical how-to guide or a standard reference book, in spite of the title. And this is kind of refreshing. In fact, the book does a good job of presenting useful information in an intellectually engaging and entertaining way.

The author points out in the introduction that his main topic is neither Python nor Bioinformatics. His central purpose seems to be to offer a useful conceptual framework -- with skill-building exercises -- for students (and scientists without a lot of programming experience) to use when they approach a problem whose solution requires a tool that isn't ready-made but might be custom made (or at least designed) by one with a rather basic programming skill set.

You can't take the sub-title literally or you'll end up disappointed with the book--as other reviewers seem to have been. In my mind, the practical application of the book's content is teaching solution-oriented thinking, with bioinformatics and Python as a case-study.

The book is not going to fit the needs of a person who would prefer the "For Dummies" format for speed of learning or a more technical, detail-driven book that leads to a specific outcome. It is probably most useful for one who, at the present moment, needs mainly to grasp the scope of what might be possible using a custom built solution.

This book is not for beginners
This book is not for beginners.
It reads more like an overview of programming languages in the context of bioinformatics.
It is also a bit biased with respect to preferences in languages.
Do not buy this book if you are trying to learn python unless you are an excellent programmer with broad knowledge of multiple programming languages.

Don't waste time or money on this book
If you are looking for a practical guide to learning python and bioinformatics this book is definitely not for you.It is filled with obscure latin phrasings and contains little to no useful information.It is of absolutely no practical value for anyone trying to gain an understanding of python.Please pass on this book.

: pass # No value here
On page 6, I immediately became suspicious of this book when the author states "Speaking for myself, it took me about two hours to learn PYTHON...."

That may well be true, but after reading the bulk of the book I started to wonder if this meant the author spent 2 hours getting a "Hello World" program to work and this is what "to learn PYTHON" referred to.

The book itself comes across as an attempt to take thematic elements of "Zen and the Art of Archery", "Write Great Code, Volume 1: Understanding the Machine", "Programming Language Pragmatics", and scatter about some quotes in Latin, philosophical snippets from Eastern and Western tradition, label chapters by pretentious names such "Chapter 3: Propedeutics", introduce a mascot for the book (a Cobra named Anna the hannah, which sits atop "catchy" and colloquial captions such as "Ready dude - beat me , break me!", and "Error in operator: add beer...") add two catchy words (Bioinformatics and Python) and purport to convey some useful knowledge in an overpriced volume that fails in just about every aspect except to show, what I see as, thinly disguised hubris.

If I were to approach Bioinformatics and Python, with the assumption that you are comfortable working in the life sciences, I would recommend "Python Scripting for Computational Science" and "Programming in Python 3: A Complete Introduction to the Python Language", and getting very familiar with NumPy.

Python is a great language, but depending on your application needs (i.e. for Bioinformatics don't hesitate to explore other less popular (at least in name recognition) but suitable languages such as Haskell and Lua or any other language that you find meets your needs. ... Read more

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

Key features of the book include:

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

  Customer Reviews (2)

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

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