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Praise for Digital Code of Life

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

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

Customer Reviews (3)

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

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

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

I'll also note that even though this was published a few years ago, it feels slightly outdated already due to the perpetual advances in throughput and methodology in the field.A new edition (at least with an afterword explaining recent advances) would be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

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

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

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

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

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

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Lucidly Integrates Current Activities

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

Examines Connections between Machine Learning & Bioinformatics

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

Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems

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

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

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

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

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

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Confused by cryptic computer programs, algorithms and formulae?

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

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

Quickly learn to manage bioinformatics!

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The rapidly evolving field of protein science has now come to realize the ubiquity and importance of protein-protein interactions. It had been known for some time that proteins may interact with each other to form functional complexes, but it was thought to be the property of only a handful of key proteins. However, with the advent of high throughput proteomics to monitor protein-protein interactions at an organism level, we can now safely state that protein-protein interactions are the norm and not the exception. Thus, protein function must be understood in the larger context of the various binding complexes that each protein may form with interacting partners at a given time in the life cycle of a cell. Proteins are now seen as forming sophisticated interaction networks subject to remarkable regulation. The study of these interaction networks and regulatory mechanism, which I would like to term "systems proteomics," is one of the thriving fields of proteomics. The bird-eye view that systems proteomics offers should not however mask the fact that proteins are each characterized by a unique set of physical and chemical properties. In other words, no protein looks and behaves like another. This complicates enormously the design of high-throughput proteomics methods. Unlike genes, which, by and large, display similar physico-chemical behaviors and thus can be easily used in a high throughput mode, proteins are not easily amenable to the same treatment. It is thus important to remind researchers active in the proteomics field the fundamental basis of protein chemistry. This book attempts to bridge the two extreme ends of protein science: on one end, systems proteomics, which describes, at a system level, the intricate connection network that proteins form in a cell, and on the other end, protein chemistry and biophysics, which describe the molecular properties of individual proteins and the structural and thermodynamic basis of their interactions within the network. Bridging the two ends of the spectrum is bioinformatics and computational chemistry. Large data sets created by systems proteomics need to be mined for meaningful information, methods need to be designed and implemented to improve experimental designs, extract signal over noise, and reject artifacts, and predictive methods need to be worked out and put to the test. Computational chemistry faces similar challenges. The prediction of binding thermodynamics of protein-protein interaction is still in its infancy. Proteins are large objects, and simplifying assumptions and shortcuts still need to be applied to make simulations manageable, and this despite exponential progress in computer technology. Finally, the study of proteins impacts directly on human health. It is an obvious statement to say that, for decades, enzymes, receptors, and key regulator proteins have been targeted for drug discovery. However, a recent and exciting development is the exploitation of our knowledge of protein-protein interaction for the design of new pharmaceuticals. This presents particular challenges because protein-protein interfaces are generally shallow and interactions are weak. However, progress is clearly being made and the book seeks to provide examples of successes in this area. ... Read more

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

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

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

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With a DVD of color figures, Clustering in Bioinformatics and Drug Discovery provides an expert guide on extracting the most pertinent information from pharmaceutical and biomedical data. It offers a concise overview of common and recent clustering methods used in bioinformatics and drug discovery.

Setting the stage for subsequent material, the first three chapters of the book introduce statistical learning theory, exploratory data analysis, clustering algorithms, different types of data, graph theory, and various clustering forms. In the following chapters on partitional, cluster sampling, and hierarchical algorithms, the book provides readers with enough detail to obtain a basic understanding of cluster analysis for bioinformatics and drug discovery. The remaining chapters cover more advanced methods, such as hybrid and parallel algorithms, as well as details related to specific types of data, including asymmetry, ambiguity, validation measures, and visualization.

This book explores the application of cluster analysis in the areas of bioinformatics and cheminformatics as they relate to drug discovery. Clarifying the use and misuse of clustering methods, it helps readers understand the relative merits of these methods and evaluate results so that useful hypotheses can be developed and tested.

Product Description
Alternative techniques and tools for analyzing biomolecular networks

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

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

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

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

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

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

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

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

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

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

Customer Reviews (1)

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

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

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

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



... Read more

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DNA microarrays have revolutionized molecular biology and are becoming a standard tool in the field. Dov Stekel's book is a comprehensive guide to the mathematics, statistics and computing required to use microarrays successfully. Unlike traditional molecular biology, the successful use of DNA microarrays requires the application of statistics and computing to design the arrays and experiments, and to analyze and manage the data. This book is written for researchers, clinicians and laboratory managers. ... Read more

Customer Reviews (8)

Very good introduction to microarray bioinformatics
This is a very clearly written and easy to read introduction to microarray bioinformatics. It builds a basic basis of knowledge in a very short time, so it is a great start for beginners in the field. By the end of the book you also have a pretty clear sense of what you might want to read in more depth. It does not treat any of the subjects in great detail, but does have reading suggestions for each chapter, including books and original papers.Also, the bioinformatics concepts are explained in a very logical and easy to grasp manner, so the biologist or physician with no computational background will find it particularly useful.

Mildly disappointing
I'm not a biologist but became curious about the microarray technology. Although the book seems to cover the important aspects (as far as I can tell from browsing), it managed to annoy me. The author did not make effort to provide something very basic like a clear vocabulary.

Example: he starts using "spot" and "feature" without making effort to explain what they mean in the context of microarrays. At times it seems he treats them as synonyms which is confusing. I consulted the index hoping to find somewhere precise definition of these terms but to no avail. At the end, I had to go to Wikipedia and various other pages which did the job but then if you have to use internet to understand the book than why not just get everything from the web and save the money for the book?

Noting that the target audience are novices in the area of microarrays (experts won't find anything valuable here), the book does a poor job of serving them. To recap: "Nice try. Could do better"

Great Introduction to Microarray Analysis
This is an excellent introduction to microarray analysis. It is great at explaining the theory behind normalization, clustering, and dimensionality reduction without getting hung up on the statistics behind it. If you are looking for an exhaustive statistical treatment on the topic, this is not the book. But it will give you excellent background on these techniques that make reading statistical papers on the topic much easier for the non-statistics biologist.

Highly recommended.

Neat little book on microarrays
Without question this short paperback is a nifty little text. What it does is provide the beginner with a basic brief overview in covering all major aspects of microarrays.

What you have to keep in mind is this book is intended for those who want a brief overview of all aspects of microarrays. Its a "forest for the trees" book on microarrays. The writing is very good and easy to follow, and its a great introductory text and reasonably priced.

Regardless of ones formal training, (e.g. Biology, Statistics, Computer Science, ... , health science) I think it would make an excellent little basic reference on ones bookshelf or to just have around in the lab for undergraduates/beginning graduate students.

Bottomline: If you prefer to learn things by starting at the start and not at the end then consider this book; Indeed its a great starter book to get your feet a little wet before jumping in over your head to the more gnarly stuff.

If you are new to microarray, get this book.
This book describes basic concepts and procedures for those who are new to microarray. I'd recommend that a reader should use this book to grasp what microarray is. You won't be able to know anything in depth from this book but it will be nice to have this if you have trouble in understanding a more challenging book. Once you read this book, please go ahead and read another book since this book doesn't tell you everything about microarray. It's just a basic overview... i was glad that I used this book as my first microarray textbook.... ... Read more

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

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

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

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New sequencing technologies have broken many experimental barriers to genome scale sequencing, leading to the extraction of huge quantities of sequence data. This expansion of biological databases established the need for new ways to harness and apply the astounding amount of available genomic information and convert it into substantive biological understanding.

A complilation of recent approaches from prominent researchers, Bioinformatics: High Performance Parallel Computer Architectures discusses how to take advantage of bioinformatics applications and algorithms on a variety of modern parallel architectures. Two factors continue to drive the increasing use of modern parallel computer architectures to address problems in computational biology and bioinformatics: high-throughput techniques for DNA sequencing and gene expression analysis—which have led to an exponential growth in the amount of digital biological data—and the multi- and many-core revolution within computer architecture.

Presenting key information about how to make optimal use of parallel architectures, this book:

• Describes algorithms and tools including pairwise sequence alignment, multiple sequence alignment, BLAST, motif finding, pattern matching, sequence assembly, hidden Markov models, proteomics, and evolutionary tree reconstruction
• Addresses GPGPU technology and the associated massively threaded CUDA programming model

• Reviews FPGA architecture and programming
• Presents several parallel algorithms for computing alignments on the Cell/BE architecture, including linear-space pairwise alignment, syntenic alignment, and spliced alignment
• Assesses underlying concepts and advances in orchestrating the phylogenetic likelihood function on parallel computer architectures (ranging from FPGAs upto the IBM BlueGene/L supercomputer)
• Covers several effective techniques to fully exploit the computing capability of many-core CUDA-enabled GPUs to accelerate protein sequence database searching, multiple sequence alignment, and motif finding
• Explains a parallel CUDA-based method for correcting sequencing base-pair errors in HTSR data

Because the amount of publicly available sequence data is growing faster than single processor core performance speed, modern bioinformatics tools need to take advantage of parallel computer architectures. Now that the era of the many-core processor has begun, it is expected that future mainstream processors will be parallel systems. Beneficial to anyone actively involved in research and applications, this book helps you to get the most out of these tools and create optimal HPC solutions for bioinformatics.

Product Description

Medical science and practice have undergone fundamental changes in the last 5 years, as large-scale genome projects have resulted in the sequencing of a number of important microbial, plant and animal genomes. This book aims to combine industry standard software engineering and design principles with genomics, bioinformatics and cancer research. Rather than an exercise in learning a programming platform, the text focuses on useful analytical tools for the scientific community.

Customer Reviews (2)

Good resource but probably overpriced
This book is more about how to write Java applications that connect to existing systems (such as BLAST) that Bioinformatics and Biomedical professionals already use, rather than how to write Java versions of those systems, the algorithms used in those systems, or algorithms generally relevant to the mentioned fields.While teaching someone to write Java applications that could send data, request service, and parse then display the results received from the queried systems is useful, such a book probably does not warrant such a high price tag as a book that teaches one how to write Java versions of algorithms useful for the Bioinformatics and Biomedical fields.

Excellent Book
Finally, a book that introduces bioinformatics using Java. While Perl is great for pattern matching and basic scripting, it is not at all an ideal choice for complex bioinformatics applications at all levels of complexity. Books like this one help train a new generation of bioinformatics experts with a broad-based training in computing (not just Perl scripting). ... Read more

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Provides an overview of the rapidly evolving field of genomics with coverage of nucleic acid technologies, proteomics and bioinformatics. It includes chapters on applications in human health, agriculture and comparative genomics and also contains two chapters on the legal and ethical issues of genomics, a topic that is becoming increasingly important as genomics moves out of the laboratory into practical applications. ... Read more

Product Description
This book brings together the two disparate worlds of computational text analysis and biology and presents some of the latest methods and applications to proteomics, sequence analysis and gene expression data.Modern genomics generates large and comprehensive data sets but their interpretation requires an understanding of a vast number of genes, their complex functions, and interactions.Keeping up with the literature on a single gene is a challenge itself-for thousands of genes it is simply impossible.
Here, Soumya Raychaudhuri presents the techniques and algorithms needed to access and utilize the vast scientific text, i.e. methods that automatically "read" the literature on all the genes.Including background chapters on the necessary biology, statistics and genomics, in addition to practical examples of interpreting many different types of modern experiments, this book is ideal for students and researchers in computational biology, bioinformatics, genomics, statistics and computer science. ... Read more

Product Description

There are fundamental principles for problem analysis and algorithm design that are continuously used in bioinformatics. This book concentrates on a clear presentation of these principles, presenting them in a self-contained, mathematically clear and precise manner, and illustrating them with lots of case studies from main fields of bioinformatics (e.g. sequencing and mapping, string storage and manipulation, pattern matching, alignment, gene identification, genome rearrangement, structure prediction, regulatory networks, pseudoknot detection). Emphasis is laid on algorithmic "pearls" of bioinformatics, showing that things may get rather simple when taking a proper view into them. The book closes with a thorough bibliography, ranging from classic research results to very recent findings, providing many pointers for future research. Overall, this volume is ideally suited for a senior undergraduate or graduate course on bioinformatics, with a strong focus on its mathematical and computer science background.

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

Customer Reviews (2)

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

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

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


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

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

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

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

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

Product Description
Data Mining for Bioinformatics presents a unified documentary reference of algorithms and methodologies of data mining that have been proposed and applied to problems in the arena of bioinformatics. It covers key research outcomes in the area of data mining and their applications to bioinformatics, including discussions on the theories and principles of data mining, design methodologydata-intensive computational challenges and presentation of algorithms. Taking discussion from journals, conference proceedings, technical reports, keynote presentations, and published books, it covers techniques that have been successfully modeled for bioinformatics applications. ... Read more