Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health)

Book Description

Bioconductor is a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology. Bioconductor is rooted in the open source statistical computing environment R.

This volume's coverage is broad and ranges across most of the key capabilities of the Bioconductor project, including importation and preprocessing of high-throughput data from microarray, proteomic, and flow cytometry platforms:

Curation and delivery of biological metadata for use in statistical modeling and interpretation

Statistical analysis of high-throughput data, including machine learning and visualization

Modeling and visualization of graphs and networks

The developers of the software, who are in many cases leading academic researchers, jointly authored chapters. All methods are illustrated with publicly available data, and a major section of the book is devoted to exposition of fully worked case studies.

This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text; it is a dynamic document. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers.

Customer Reviews:

technically accurate but pedagogically flawed.......2007-02-09

If you're like me, you came upon this book because you decided to use R for analysis of microarray data, but you're mired in its gory and frustrating details.

Yes, you need a reference book. But not this one, and certainly not this edition. Better documentation can be found elsewhere (dare I say online?).

The code examples given are technically accurate and run as advertised, but they are of the "monkey see, monkey do" variety. They provide little intuition for how to use R for oneself, outside the covers of this text. For example, Chapter 23 discusses linear models for microarray data (using the "limma" package), and several code examples contain the parameter 'adjust = "fdr"'. The reader is never enlightened that this refers to a "false discovery rate" adjustment.

In other cases, example code is simply missing. Chapter 21 covers the Rgraphviz graphing library, with a figure showing the three common graphical layouts -- but no example code for producing these graphs is given (I had to find it outside the book).

For those trying to use R for computational biology, I recommend getting an overview of the R programming language first (Venables and Ripley's book "Modern Applied Statistics with S" is a great text), and only then wading into references such as this one, if at all.

Book contains many chapters to help get you started.......2006-06-30

I purchased this book to learn specific details and look at applications for the functions present in bioconductor. I have had trouble applying some of the chapters to custom data because they are written for specific microarray/data formats. Overall, this book is a good value because it contains examples of how bioconductor can be used to aid in hypothesis testing, but I struggle to apply what I have read to the different types of data I have. The section on Statistical analysis for genomic experiments and the section on gaphs and networks should be the reason you purchase this book. They are very helpful and interesting. The case studies were not very helpful in my opinion.

Bioinformatics for Geneticists: A Bioinformatics Primer for the Analysis of Genetic Data

Average customer rating:

A Timely Primer for Researchers about the Analysis of Genetic Data

Bioinformatics for Geneticists: A Bioinformatics Primer for the Analysis of Genetic Data
Michael R., Ed. Barnes
Manufacturer: John Wiley & Sons
ProductGroup: Book
Binding: Paperback

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Statistical Genomics: LINKAGE, MAPPING, AND QTL ANALYSIS
Bioinformatics for Dummies (For Dummies Series)
Essential Bioinformatics
The Genie in Your Genes: Epigenetic Medicine and the New Biology of Intention
Principles of Gene Manipulation and Genomics

ASIN: 0470026200

Book Description

A fully revised version of the successful First Edition, this one-stop reference book enables all geneticists to improve the efficiency of their research.

The study of human genetics is moving into a challenging new era. New technologies and data resources such as the HapMap are enabling genome-wide studies, which could potentially identify most common genetic determinants of human health, disease and drug response. With these tremendous new data resources at hand, more than ever care is required in their use. Faced with the sheer volume of genetics and genomic data, bioinformatics is essential to avoid drowning true signal in noise. Considering these challenges, Bioinformatics for Geneticists, Second Edition works at multiple levels: firstly, for the occasional user who simply wants to extract or analyse specific data; secondly, at the level of the advanced user providing explanations of how and why a tool works and how it can be used to greatest effect. Finally experts from fields allied to genetics give insight into the best genomics tools and data to enhance a genetic experiment.

Hallmark Features of the Second Edition:

Illustrates the value of bioinformatics as a constantly evolving avenue into novel approaches to study genetics
The only book specifically addressing the bioinformatics needs of geneticists
More than 500f chapters are completely new contributions
Dramatically revised content in core areas of gene and genomic characterisation, pathway analysis, SNP functional analysis and statistical genetics
Focused on freely available tools and web-based approaches to bioinformatics analysis, suitable for novices and experienced researchers alike

Praise for the First Edition:

”…a very valuable and important resource for bringing bioinformatics into the work practice of geneticists… we strongly recommend this book…” CLINICAL CHEMISTRY, 2004

”…a useful addition to the library of a seasoned scientist… a useful resource in itself, cataloguing the ‘how’ and ‘why’…” BRIEFINGS IN BIOINFORMATICS, June 2004

Bioinformatics for Geneticists, Second Edition describes the key bioinformatics and genetic analysis processes that are needed to identify human genetic determinants. The book is based upon the combined practical experience of domain experts from academic and industrial research environments and is of interest to a broad audience, including students, researchers and clinicians working in the human genetics domain.

Customer Reviews:

A Timely Primer for Researchers about the Analysis of Genetic Data.......2007-08-23

This book is a welcomed addition to the literature of bioinformatics. It helps one become familiar with how to find and use genetic data. This is a dauting task for most investigators who must navigate through the sea of genetic data now being produced. Analyzing genetic data is not an easy task but this book helps one identify resources and map a course.

Analysis of Phylogenetics and Evolution with R (Use R)

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Analysis of Phylogenetics and Evolution with R (Use R)
Emmanuel Paradis
Manufacturer: Springer
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R Graphics (Chapman & Hall/ CRC Computer Science & Data Analysis)
Inferring Phylogenies
Computational Molecular Evolution (Oxford Series in Ecology and Evolution)
Statistical Methods in Molecular Evolution (STATISTICS FOR BIOLOGY AND HEALTH)
Gene Genealogies, Variation and Evolution: A Primer in Coalescent Theory

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Fundamentals of Clinical Trials
Statistical Monitoring of Clinical Trials: A Unified Approach (STATISTICS FOR BIOLOGY AND HEALTH)

ASIN: 0387329145

Book Description

The increasing availability of molecular and genetic databases coupled with the growing power of computers gives biologists opportunities to address new issues, such as the patterns of molecular evolution, and re-assess old ones, such as the role of adaptation in species diversification.

This book integrates a wide variety of data analysis methods into a single and flexible interface: the R language. This open source language is available for a wide range of computer systems and has been adopted as a computational environment by many authors of statistical software. Adopting R as a main tool for phylogenetic analyses will ease the workflow in biologists' data analyses, ensure greater scientific repeatability, and enhance the exchange of ideas and methodological developments.

Graduate students and researchers in evolutionary biology can use this book as a reference for data analyses, whereas researchers in bioinformatics interested in evolutionary analyses will learn how to implement these methods in R. The book starts with a presentation of different R packages and gives a short introduction to R for phylogeneticists unfamiliar with this language. The basic phylogenetic topics are covered: manipulation of phylogenetic data, phylogeny estimation, tree drawing, phylogenetic comparative methods, and estimation of ancestral characters. The chapter on tree drawing uses R's powerful graphical environment. A section deals with the analysis of diversification with phylogenies, one of the author's favorite research topics. The last chapter is devoted to the development of phylogenetic methods with R and interfaces with other languages (C and C++). Some exercises conclude these chapters.

Average customer rating:

Misleading title!
Great all-around review of probability
Disappointing overview
Pretty good overview
guide into the right direction

Statistical Methods in Bioinformatics
Warren J. Ewens , and Gregory R. Grant
Manufacturer: Springer
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An Introduction to Bioinformatics Algorithims (COMPUTATIONAL MOLECULAR BIOLOGY)

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Academic Scientists at Work
The Science Of Cooking
Nanobiotechnology of Biomimetic Membranes (Fundamental Biomedical Technologies)

ASIN: 0387952292

Book Description

Advances in computers and biotechnology have had an immense impact on the biomedical fields, with broad consequences for humanity. Correspondingly, new areas of probability and statistics are being developed specifically to meet the needs of this area. There is now a necessity for a text that introduces probability and statistics in the bioinformatics context. This book also describes some of the main statistical applications in the field, including BLAST, gene finding, and evolutionary inference, much of which has not yet been summarized in an introductory textbook format. This book grew out of a need to teach bioinformatics to graduate students at the University of Pennsylvania. At the same time however, it is organized to appeal to a wider audience. In particular it should appeal to any biologist or computer scientist who wants to know more about the statistical methods of the field, as well as to a trained statistician who wishes to become involved in bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level, and will be accessible to students who have only had introductory calculus and linear algebra. Later chapters are immediately accessible to the trained statistician. Only a basic understanding of biological concepts is assumed, and all concepts are explained when used or can be understood from the context. Several chapters contain material independent of that in other chapters, so that the reader interested in certain areas can proceed directly to those areas.

Warren Ewens is Professor of Biology at the University of Pennsylvania. He is the author of two books, Population Genetics and Mathematical Population Genetics, and has served on the editorial boards of Theoretical Population Biology, GENETICS, Proceeding of the Royal Society B and SIAM Journal in Mathematical Biology. He was recently awarded the Gold Medal of the Australian Statistical Society and elected as Fellow of the Royal Society. His research interests are in evolutionary population genetics, linkage analysis for human diseases, and bioinformatics.

Gregory Grant is a bioinformatics researcher at the University of Pennsylvania in the Computational Biology and Informatics Laboratory (CBIL), where he has been since 1998. In 1995 he received a Ph.D. in Mathematics from the University of Maryland and in 1999 a Masters in Computer Science from the University of Pennsylvania. His research interests are in bioinformatics in general and in particular in the statistical analysis of gene expression data and significance testing methods for IBD-mapping.

Customer Reviews:

Misleading title!.......2004-12-12

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

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

Great all-around review of probability .......2004-08-17

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

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

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

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

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

//wiredweird

Disappointing overview.......2003-11-12

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

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

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

Pretty good overview.......2002-09-19

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

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

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

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

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

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

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

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

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

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

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

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

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

guide into the right direction.......2001-09-06

This is one of the books I have been waiting for. For a population geneticist who wants to learn bioinformatics, most texts are unacceptable: They present heuristic methods in a cookbook fashion, with little reference to what is going on biologically as well as mathematically.

This book is the first exception I know of. It builds, and rests on, solid foundations of genetic stochastic processes and still goes all the way to real-life problems. Let me illustrate this by means of an example, rather than enumerating all the topics in the book.

Chap. 14, entitled `phylogenetic tree estimation' (as opposed to the more common term `phylogenetic tree reconstruction' - not without reason, I presume) builds on, and is firmly interlaced with, Chap. 13 about `evolutionary models', which systematizes the zoo (if not jungle) of substitution models in both discrete and continuous time. On this basis, the overview of tree-building methods makes a lot of sense. Even better, it does not stop here, but presents an application (to real sequence data), followed by a careful analysis of where the various methods agree, and where - and maybe why - they disagree. This way, it clears away some common misconceptions; in particular, it presents a careful analysis of what bootstrap does and what it does not in this context. The chapter closes with a discussion of unresolved problems (like inhomogeneity of substitution rates), and methods and possible pitfalls related to testing of nested and non-nested hypotheses in tree estimation.

The book is written in an informal style without being imprecise, which makes it pleasant reading. It is particularly suitable for teaching at a high level. This is enhanced by realistic (and even real-life) examples that furnish the text, as well as carefully chosen exercises at the end of each chapter.

Book Description

Biometric Technologies and Verification Systems is organized into nine parts composed of 30 chapters, including an extensive glossary of biometric terms and acronyms. It discusses the current state-of-the-art in biometric verification/authentication, identification and system design principles. It also provides a step-by-step discussion of how biometrics works; how biometric data in human beings can be collected and analyzed in a number of ways; how biometrics are currently being used as a method of personal identification in which people are recognized by their own unique corporal or behavioral characteristics; and how to create detailed menus for designing a biometric verification system.

Only biometrics verification/authentication is based on the identification of an intrinsic part of a human being. Tokens, such as smart cards, magnetic stripe cards, and physical keys can be lost, stolen, or duplicated. Passwords can be forgotten, shared, or unintentionally observed by a third party. Forgotten passwords and lost "smart cards" are a nuisance for users and an expensive time-waster for system administrators. Biometric security solutions offer some unique advantages for identifying and verifying/ authenticating human beings over more traditional security methods. This book will serve to identify the various security applications biometrics can play a highly secure and specific role in.

* Contains elements such as Sidebars, Tips, Notes and URL links
* Heavily illustrated with over 150 illustrations, screen captures, and photographs
* Details the various biometric technologies and how they work while providing a discussion of the economics, privacy issues and challenges of implementing biometric security solutions

Customer Reviews:

Security with a 21st century flair.......2007-10-09

The use of biometrics to secure valuable information is a necessity. This book provides insight into the implementation of this incredible methodology.

Great source of security information.......2007-10-08

Biometric Technologies and Verification Systems by John Vacca provides a great source of information on the latest physical recognition systems, committees and emerging standards. The book is rich in easy to comprehend discussions of the various recognition technologies and how they work. The book also describes deployment and implementation strategies along with social, privacy, economic and legal considerations. If you want to know about Biometric Technologies, this is the book to read.

fascinating topic.......2007-10-05

Biometric technologies are a fascinating topic with which everyone should be familiar. It is no longer a sci-fi, double 0 seven special effect. Biometric technologies are real and necessary in this information age.

John Vacca's book explains the different technologies. The variety and depth of his discussions are most interesting. This is a wonderful book.

James Bond technology is now common place........2007-09-25

Biometric technology clearly illustrated and explained in the John Vacca "style" (ie, practical and useful). This book is an excellent reference for the IT Security professional, laymen, or student.

The best book yet on Biometric Technology!.......2007-09-15

Biometric verification systems offer a radical alternative to passports, PIN numbers, ID cards and driving licences. It uses physiological or behavioral characteristics such as fingerprints, hand geometry, and retinas to check a person's identity.

This is the first book to provide an easy-to-read explanation of the technical aspects of biometric systems.

This book tells you how to make biometrics technology work. Several technologies involving scanning and analyzing unique body characteristics and matching them against information stored in a database are covered.

This book is a complete, technical guide that covers the details, principles, methods, and technologies of biometric security systems. It explains the practical applications and covers a number of topics critical for successful biometrics security implementation. These include recognition accuracy, processing speed, intrinsic and system security, privacy and legal requirements, and user acceptance.

This book will be an invaluable read for practitioners, managers and IT personnel - in fact for anyone considering, or involved in, implementing a biometric system. Security and financial administrators, computer science professionals, and biometric systems developers will all benefit from an enhanced understanding of this important technology. If you are considering making biometrics a part of your overall security plan this hands-on guide will be very helpful to you.

Instant Notes in Bioinformatics (Instant Notes)

Average customer rating:

Concise primer. Not bad.

Instant Notes in Bioinformatics (Instant Notes)
D. R. Westhead
Manufacturer: BIOS Scientific Publishers
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Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins
Developing Bioinformatics Computer Skills
Bioinformatics: Sequence and Genome Analysis
Ethical Issues in Biotechnology
Bioinformatics for Dummies (For Dummies Series)

ASIN: 1859962726

Book Description

Instant Notes titles focus on core information and are designed to help undergraduate students come to grips with a subject quickly and easily.

Instant Notes in Bioinformatics describes what is possible, and the strengths, limitations and potential pitfalls of methods and analyses. The book begins by describing data generation and databases. It also discusses the newer bioinformatics problems associated with structures, expression, proteomics, interactions and pathways. The important areas in bioinformatics are covered to encourage easy learning and brushing-up.

Customer Reviews:

Concise primer. Not bad........2003-12-09

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.

Book Description

Books on bioinformatics which began appearing in the mid 80s primarily served gene-hunters, and biologists who wished to construct family trees showing tidy lines of descent. Given the great pharmaceutical industry interest in genes, this trend has continued in most subsequent texts. These deal extensively with the exciting topic of gene discovery and searching databases, but hardly consider genomes as information channels through which multiple forms and levels of information, including genic information, have passed through the generations. This book identifies the types of information that genomes transmit, shows how competition between different types is resolved in the genomes of different organisms, and identifies the evolutionary forces involved. The early chapters relate the form of information with which we are most familiar, namely written texts, to the DNA text that is our genome. This lends itself well to introducing historical aspects dating back to the nineteenth century.

Advanced Data Mining and Applications: Third International Conference, ADMA 2007, Harbin, China, August 6-8, 2007 Proceedings (Lecture Notes in Computer Science)

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Advanced Data Mining and Applications: Third International Conference, ADMA 2007, Harbin, China, August 6-8, 2007 Proceedings (Lecture Notes in Computer Science)

Manufacturer: Springer
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ASIN: 3540738703

Book Description

This book constitutes the refereed proceedings of the Third International Conference on Advanced Data Mining and Applications, ADMA 2007, held in Harbin, China in August 2007.

The 44 revised full papers and 15 revised short papers presented together with the abstract of 1 invited lecture were carefully reviewed and selected from about 200 submissions. The papers focus on advancements in data mining and peculiarities and challenges of real world applications using data mining. The major theme of the conference encompasses the innovative applications of data mining approaches to real-world problems that involve large data sets, incomplete and noisy data, or demand optimal solutions.

Advances in Intelligent Data Analysis V: 5th International Symposium on Intelligent Data Analysis, IDA 2003, Berlin, Germany, August 28-30, 2003, Proceedings (Lecture Notes in Computer Science)

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Advances in Intelligent Data Analysis V: 5th International Symposium on Intelligent Data Analysis, IDA 2003, Berlin, Germany, August 28-30, 2003, Proceedings (Lecture Notes in Computer Science)

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ASIN: 3540408134

Book Description

This book constitutes the refereed proceedings of the 5th International Conference on Intelligent Data Analysis, IDA 2003, held in Berlin, Germany in August 2003.

The 56 revised papers presented were carefully reviewed and selected from 180 submissions. The papers are organized in topical sections on machine learning, probability and topology, classification and pattern recognition, clustering, applications, modeling, and data processing.

Advances in Intelligent Data Analysis VII: 7th International Symposium on Intelligent Data Analysis, IDA 2007, Ljubljana, Slovenia, September 6-8, 2007, Proceedings (Lecture Notes in Computer Science)

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Advances in Intelligent Data Analysis VII: 7th International Symposium on Intelligent Data Analysis, IDA 2007, Ljubljana, Slovenia, September 6-8, 2007, Proceedings (Lecture Notes in Computer Science)

Manufacturer: Springer
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ASIN: 3540748245

Book Description

This book constitutes the refereed proceedings of the 7th International Conference on Intelligent Data Analysis, IDA 2007, held in Ljubljana, Slovenia, September 6-8, 2007.

The 33 revised papers presented were carefully reviewed and selected from almost 100 submissions. All current aspects of this interdisciplinary field are addressed; the areas covered include statistics, machine learning, data mining, classification and pattern recognition, clustering, applications, modeling, and interactive dynamic data visualization.

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