Book Description

An unprecedented wealth of data is being generated by genome sequencing projects and other experimental efforts to determine the structure and function of biological molecules. The demands and opportunities for interpreting these data are expanding rapidly. Bioinformatics is the development and application of computer methods for management, analysis, interpretation, and prediction, as well as for the design of experiments. Machine learning approaches (e.g., neural networks, hidden Markov models, and belief networks) are ideally suited for areas where there is a lot of data but little theory, which is the situation in molecular biology. The goal in machine learning is to extract useful information from a body of data by building good probabilistic models--and to automate the process as much as possible.

In this book Pierre Baldi and Sren Brunak present the key machine learning approaches and apply them to the computational problems encountered in the analysis of biological data. The book is aimed both at biologists and biochemists who need to understand new data-driven algorithms and at those with a primary background in physics, mathematics, statistics, or computer science who need to know more about applications in molecular biology.

This new second edition contains expanded coverage of probabilistic graphical models and of the applications of neural networks, as well as a new chapter on microarrays and gene expression. The entire text has been extensively revised. ... Read more

Reviews (15)

Great book if you have the necessary background
Their bayesian presentation of machine learning algorithms can be hard to follow at times, but the authors cover a large amount of very current practical and theoretical material. One of the the book's unique features is it's broad scope. The authors discuss neural networks, hidden markov models, clustering, gaussian processes and support vector machines. The bibliography contains some of the most useful references for those wishing to implement bioinformatics algorithms. The fast pace may leave some wanting more complete explanations.
You should disregard the claim that this book could be used by those unfamiliar with either molecular biology or computer science. To really make the most of this book, you should be comfortable with the material in Pattern Classification (Duda, Hart and Stork), Biological Sequence Analysis (Durbin, Eddy, Krogh, and Mitchison), and Molecular Biology of the Cell (Alberts et al). That said, this is the best bioinformatics book on the market.

Excellent text both for biologists and computer scientists.
I found the book very readable, and full of information combining the machine learning approach (neural nets and Hidden Markov models) with biological problems. The wealth of specific biological information bridges the background gap for computer scientists and mathematicians, and the organization of topics is excellent.

In the mathematics and computer science community, Baldi is an internationally recognized expert in the fields of neural nets and Hidden Markov models and their applications (for instance, he holds a patent for neural net recognition of fingerprints). Concerning HMM's Baldi and co-workers have found statistical models for protein families, sequence signals for nucleosome centers, etc. Moreover, Baldi, together with Chauvin, has developed a gradient descent parameter update method for HMM's which has no zero probability absorptions, and allows on-line updates, useful features not supported by the standard EM method.

From these and other applications, I found the text very useful.

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

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

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

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

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

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

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

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

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

Book Description

Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies. ... Read more

Book Description

Taking a system approach to expose modern biology, this book presents the fundamental system principles and parameters common to all living species. The straightforward examination begins with a presentation of molecular cell biology and progresses to the complex interrelationship between genes and proteins as observed in metabolic process, signal transduction, cell division and embryonic development. The book's unique approach provides a depiction of the human genome project, a review of high throughput biology and bioinformatic tools and a presentation of gene circuitry and pathway analysis as applied to cell division, development of embryo and metabolic pathways and expose of emerging proteomic science.The volume presents the chemistry of life, macromolecules of life, cells and their housekeeping functions, gene circuits, genomics, cell adhesion and communication, cell division and its regulation, development of multicellular organisms and large scale biology.For computer scientists, physicists, and engineers. ... Read more

Reviews (2)

A must read!
This book was a pleasant surprise. It's one of the few books on bioinformatics that I've read that doesn't assume the reader has a PhD in biochemistry or mathematics. It's a gentle but thorough introduction to many of the problems faced by life scientists who are trying to get a handle on this thing called bioinformatics. I've been working in the life sciences for years, and this is the first book I've read that explains how I can make use of the various search engines, genomic analysis tools, and the dozens of genomics databases worldwide in my day-to-day life.

I especially appreciate the author's frank analysis of the state of the art at the end of each chapter. He seems to put a balanced spin on the field, pointing out the vast potential of bioinformatics computing in practical medicine and materials synthesis, while grounding the reader in current political and economic realities that are limiting many aspects of the field.

I consider it a must read.

Bioinformatics for now and the future
Dr, Beregeron ofers a sensible yet visionary exposition of the field. For me, the future direction of this field is most compelling. It is a primer that can be challenging to the most sophisticated afficionado, also. ... Read more

Book 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.

Visit this website for exercises with solutions, computer programs, errata and additional material:

http://www.ii.uib.no/proteinbioinformatics/ ... Read more

Book Description

Sequence Analysis in a Nutshell: A Guide to Common Tools and Databases pulls together all of the vital information about the most commonly used databases, analytical tools, and tables used in sequence analysis.The book is partitioned into three fundamental areas to help you maximize your use of the content. The first section, "Databases" contains examples of flatfiles from key databases (GenBank, EMBL, SWISS-PROT), the definitions of the codes or fields used in each database, and the sequence feature types/terms and qualifiers for the nucleotide and protein databases.The second section, "Tools" provides the command line syntax for popular applications such as ReadSeq, MEME/MAST, BLAST, ClustalW, and the EMBOSS suite of analytical tools. The third section, "Appendixes" concentrates on information essential to understanding the individual components that make up a biological sequence. The tables in this section include nucleotide and protein codes, genetic codes, as well as other relevant information.Written in O'Reilly's enormously popular, straightforward "Nutshell" format, this book draws together essential information for bioinformaticians in industry and academia, as well as for students.If sequence analysis is part of your daily life, you'll want this easy-to-use book on your desk. ... Read more

Reviews (3)

Sequence Analysis: Not in a Nutshell
The O'Reilly Press is normally the gold standard when it comes to very well-written and very well-edited hard science documents. That is why I purchased this one. Sadly, this falls very far below the norm. The first 73 pages of the text may be skipped. Most of the rest is EMBOSS. Sadly, you can get this directly, and freely, from the original authors. Just use a normal search and type "EMBOSS". Nothing else pertains.

Wayne

from a bioinformatics student and programmer
I picked this book up at BioCon 2003 and, to be honest, I wasn't sure at first how useful it was going to be. Flipping through a few of the sections, it seemed to be little more than an assemblage of man pages for each of the tools and programs it covered. So I put it on a shelf above my monitor at work for a while and as the days went by I found myself grabbing it more and more often to look something up.

For example, I found myself needing reminding of the option for tabular output when doing a psi-blast. Grab the book ... a ha! ... -m 8. You can use the man pages or tutorials for many of things like this but sometimes there is a lot to wade through to find what you were looking for. Also, if you're like me you like the feel of a book sometimes and the ability to scrawl notes in the margins. It's just nice having all the options right there on the page.

To be fair to the authors, I don't think that Chia-hsiu Tu was very accurate in his review by saying that "this books focus on EMBOSS only" (sic). EMBOSS coverage does make up about 58% of the book, but it is a suite of 150 useful programs. Unless you want only a sentence or two about each one you're going to have to use up a few pages. You get just enough info to learn about each and a quick guide to their usage. If you want to know more, there are links to their full documentation online.

Some sections are stronger than others. The MEME/MAST chapter, for instance, doesn't just list out options but has great command line examples and a paragraph for each explaining what is going on. On the other hand, I wanted to use stretcher (in the EMBOSS package) and there was only a quick example (the syntax of which didn't work for me) and a listing of six options. I needed to find out how to make it work in a non-interactive way and write its output to STDOUT, neither of which were illustrated (-auto and -stdout, by the way).

Ok, let's get to what this book covers. The first section goes really in depth to cover the data-exchange formats that we nerds find ourselves writing parsing scripts for all the time. (yes, yes, bioperl, biojava, etc. are great, but they aren't in this book. Hopefully one will cover them soon.) What I found most useful were the example files for each format (EMBL, DDBJ, Genbank, FASTA, SWISS-PROT, PFAM, & PROSITE) and the tables that were laid out for them. For example, there are nice little tables listing every feature (62 at my count) and feature qualifier (74!) that you can expect to find in a DDBJ/EMBL/Genbank file. And for each of those there is a little descripton of what they represent. Very nice.

The second part of the book covers these specific tools: ReadSeq, the BLAST suite (7 progs), BLAT, CLUSTALW, HMMER (10 progs), MEME, MAST, and the EMBOSS suite (~150 progs). These sections are pretty decent and while you won't find much info on how the algorithms behind the programs work, you will have everything you need to run the programs and fine-tune their options to control their behavior.

Lastly, the third part of the book has a really valuable quick reference of a variety of things such as a listing of the amino acids, their 1 and 3-letter abbreviations, structures and properties. In the genetic codes section you'll be able to quickly remind yourself that the transcriptional product for AUA in invertebrate mitochondriates differs from the norm, coding for methionine instead of isoleucine. (you knew that right?)

On the whole, I think this reference is a great review of the most common tools out there for sequence analysis and a quick guide to their use. While at times examples and verbose explanations are lacking, one must keep in mind that this is a book in the "nutshell" series, not in the "definitive guide" one. If you find yourself scouring for online docs and searching man pages for special options often, you should definitely get this book.

an excellent rteference book
This books focus on EMBOSS only, a high quality open source bioinformatic toolkit. It can be a useful reference book when write web interface of those programs in this book. Also it provides the urls where we can download from? where the original idea come from? ... Read more

Book Description

Introduction to Bioinformatics by Arthur Lesk is a timely and much-needed textbook which provides an accessible and thorough introduction to a subject which is becoming a fundamental part of biological science today. As a pioneer of the use of bioinformatics techniques in research, Dr Lesk brings unrivalled experience and expertise to the study of this field. The aim of the book is to generate an understanding of the biological background of bioinformatics, and to integrate this with an introduction to the use of computational skills. Without describing computer science or sophisticated programming skills in detail, the book supports and encourages the application of the many powerful computational tools of bioinformatics in a way that is both relevant to and stimulating for the reader. The book contains numerous problems and innovative Weblems (for Web-based Problems) to encourage students to engage with the subject and with the accompanying web site and to develop a working understanding and appreciation of the power of bioinformatics as a research tool. ... Read more

Book Description

Instant Notes in Bioinformatics, provides concise yet comprehensive coverage of bioinformatics at an undergraduate level, with easy access to the fundamentals in this complex field. All the important areas in bioinformatics are covered in a format which is ideal for learning and rapid revision and reference. ... Read more

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

Book Description

The purpose of this book is to give a thorough and systematic introduction to probabilistic modeling in bioinformatics. The book contains a mathematically strict and extensive presentation of the kind of probabilistic models that have turned out to be useful in genome analysis. Questions of parametric inference, selection between model families, and various architectures are treated. Several examples are given of known architectures (e.g., profile HMM) used in genome analysis.Audience: This book will be of interest to advanced undergraduate and graduate students with a fairly limited background in probability theory, but otherwise well trained in mathematics and already familiar with at least some of the techniques of algorithmic sequence analysis. ... Read more

Reviews (3)

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, including shotgun 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

Book 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

Book Description

Many methods and models have been proposed for solving difficult problems such as prediction, planning and knowledge discovery in application areas such as bioinformatics, speech and image analysis. Most, however, are designed to deal with static processes which will not change over time. Some processes - such as speech, biological information and brain signals - are not static, however, and in these cases different models need to be used which can trace, and adapt to, the changes in the processes in an incremental, on-line mode, and often in real time. This book presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems. The models and techniques used are connectionist-based (as the evolving brain is a highly suitable paradigm) and, where possible, existing connectionist models have been used and extended. The first part of the book covers methods and techniques, and the second focuses on applications in bioinformatics, brain study, speech, image, and multimodal systems. It also includes an extensive bibliography and an extended glossary. Evolving Connectionist Systems is aimed at anyone who is interested in developing adaptive models and systems to solve challenging real world problems in computing science or engineering. It will also be of interest to researchers and students in life sciences who are interested in finding out how information science and intelligent information processing methods can be applied to their domains. ... Read more

Reviews (2)

An exceptional book for computational biologists
This exceptional book provides a broad overview of the methods of extracting the knowledge (or in other words building model/system/theory) from the data in various areas: from information theory and artificial intelligence to genetics. It can be very useful for biologists, who wish to use modern computational methods for analysis of microarray data, regulatory networks, cancer, analysis of clinical trials, etc.

The first part (first seven chapters) of the book is devoted to the methods used in connectionists systems and here readers can find detailed description of the algorithms. In the second part (six chapters), which represents application of these methods, the book has a chapter devoted to the data analysis, modeling, and knowledge discovery in bioinformatics so it can be interesting for the biologists. This chapter describes how the neural network paradigm can be used in molecular biology and, in particular, for analysis in relatively new area -- microarray technology. The huge amount of data that were obtained in this area is still waiting for the efficient methods of knowledge extracting. In this chapter readers can also find the examples of using evolving connectionists learning systems for solving the problems of finding the patterns from DNA/RNA sequences, identification of intron/exon binding sites, gene profiling, protein structure prediction and dynamic cell modeling.

This excellent book is full of interesting examples, classification schemes, and figures.
Although this book will be more interesting for readers, which have been working in networking, it can be useful also for all researchers and students and any type of readers interesting in data analysis. This book is outstanding introduction for readers unfamiliar with the learning systems. The extended glossary and full-length reference list will help a lot for readers inexperienced in this area.

Real-time neural network with a host of applications
I found this book to be a landmark contribution to the state-of-the-art in neural networks pardigm. It offers some exciting neural network topologies and a distinctly new kind of thinking -'local learning' in neural networks. The author Prof. Nik Kasabov deserves to be congratulated for writing this excellent book. His explanation throughout the book is very lucid and to the point. He introduces the concept of "evolving connectionism" in a succinct way. He included a rich assortment of connectionist methods, right from the scratch, with a clear exposition of the underlying training algorithms. The applications presented in the latter part of the book are as diverse as bioinformatics, financial engineering, speech recognition, brain study and image & video data processing. The authority with which these topics are presented speaks volumes of the enormous research work undertaken by Prof. Kasabov and his students. The references and extended glosary provided at the end are extremely useful to the reader. Another important aspect of this book is that it is suitable for all levels of readers such as student, researcher and practitioner. I started teaching some aspects of this book from this semester onwards. It is well received by the students. It must be in the shelves of those who look for the latest research in the area of neural networks. I enjoyed reading this book. Finally, if the phrase "real-time neural networks" is also added in the tag line (sub title) of the book, it could attract more users. ... Read more

Book Description

Recent developments in molecular biology, genomics, proteomics, and other areas have produced a wealth of experimental data on sequences and three-dimensional structures of biological macromolecules. As a result, the employment of various computational methods of analysis to obtain useful information is now a major new discipline: bioinformatics.
The Dictionary of Bioinformatics and Computational Biology provides a vital reference for important terms, offering students and researchers a convenient summary of the core knowledge of the field. With concise and accurate definitions of over 600 words, phrases, and concepts, this volume:
* Offers thorough A-Z coverage
* Lists references and Internet links
* Incorporates cross-referencing throughout
* Features a comprehensive index of topics
Anyone working in basic sciences and clinical research today will find the Dictionary of Bioinformatics and Computational Biology to be an essential companion. ... Read more

Book Description

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

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

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

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

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

... Read more

Reviews (2)

Mediocre (but Chapter 3 is worth reading)
Not really worth spending money and time. The price is way too much for a book of this volume. Authors spent too much writing on superficial (length of several paragraphs) introduction to various softwares and databases (e.g., database XXX is produced by YYY research center using ZZZ format, etc.).

Chapter 3 (Protein information resources), however, was worth reading. It presents good figures and tables on various patterns (e.g., pattern, profile, fingerprint, HMM, block) in protein structure.

Also, no errata information is provided from publisher.

Interesting book
This is a good book to introduce begginers into bioinformatics. It is very clear and easy to read, thereby can be useful for both biologists and computer scientists as well. Anyway if you are a computer scientist I would suggest you to better have a basical knowledge of some biology concepts such as gens, proteins and so on. The book explains some concepts of bioinformatics in a general way. For example: some sequence alignment algorithms are noted but not described deeply. I think the book is worth the money. ... Read more

Book Description

Univ. of Manchester, U.K.Practical, strategic guide to the process of DNA sequencing. Covers planning the approach, data acquisition, and extracting findings from the data. Softcover. ... Read more

Reviews (1)

Great plot!
At first I thought it was going to be another one of those books thats all 'facts facts facts' but it was so much more! The characters, the settings.. all described in rich detail, I was engrossed from beginning to end.Couldn't put it down it was that good! ... Read more

Book Description

In the age of global biotechnology, DNA can exist as biological material in a test tube, as a sequence in a computer database, and as economically valuable information in a patent. In The Global Genome, Eugene Thacker asks us to consider the relationship of these three entities and argues that -- by their existence and their interrelationships -- they are fundamentally redefining the notion of biological "life itself."

Biological science and the biotech industry are increasingly organized at a global level, in large part because of the use of the Internet in exchanging biological data. International genome sequencing efforts, genomic databases, the development of World Intellectual Property policies, and the "borderless" business of biotech are all evidence of the global intersections of biology and informatics -- of genetic codes and computer codes. Thacker points out the internal tension in the very concept of biotechnology: the products are more "tech" than "bio," but the technology itself is fully biological, composed of the biomaterial labor of genes, proteins, cells, and tissues. Is biotechnology a technology at all, he asks, or is it a notion of "life itself" that is inseparable from its use in the biotech industry?

The three sections of the book cover the three primary activities of biotechnology today: the encoding of biological materials into digital form -- as in bioinformatics and genomics; its recoding in various ways -- including the "biocolonialism" of mapping genetically isolated ethnic populations and the newly pervasive concern over "biological security"; and its decoding back into biological materiality -- as in tissue engineering and regenerative medicine. Thacker moves easily from science to philosophy to political economics, enlivening his account with ideas from such thinkers as Georges Bataille, Georges Canguilhem, Michel Foucault, Antonio Negri, and Paul Virilio. The "global genome," says Thacker, makes it impossible to consider biotechnology without the context of globalism. ... Read more