Book Description
Biology is in the midst of a era yielding many significant discoveries and promising many more. Unique to this era is the exponential growth in the size of information-packed databases. Inspired by a pressing need to analyze that data, Introduction to Computational Biology explores a new area of expertise that emerged from this fertile field- the combination of biological and information sciences. This introduction describes the mathematical structure of biological data, especially from sequences and chromosomes. After a brief survey of molecular biology, it studies restriction maps of DNA, rough landmark maps of the underlying sequences, and clones and clone maps. It examines problems associated with reading DNA sequences and comparing sequences to finding common patterns. The author then considers that statistics of pattern counts in sequences, RNA secondary structure, and the inference of evolutionary history of related sequences. Introduction to Computational Biology exposes the reader to the fascinating structure of biological data and explains how to treat related combinatorial and statistical problems. Written to describe mathematical formulation and development, this book helps set the stage for even more, truly interdisciplinary work in biology.
Customer Reviews:
A modern classic.......2003-10-15
The first name people learn in bioinformatics is the Smith-Waterman algorithm. Some people never learn anything else. This is by that Waterman. Although written in 1995, it still has some of the best discussion I've seen on the topics it addresses.
The first few chapters deal with the "digest problem," reconstructing a DNA or protein sequence from the fragment sizes of enzyme digests. The technique is not used as much now as it was then, but it's always good to know the background of modern techniques.
The digest problem doesn't stand alone, though. It introduces concepts - islands, anchors, etc. - that still matter. The problems in reconstructing molecules from digests yield the same kinds of intermediate results and the same ambiguities that arise in modern sequencing. As Waterman advances the discussion, shotgun sequencing appears as a logical extension, at least mathematically, of digest assembly.
Sequence assembly involve end matching, perhaps in the presence of sequencing errors. That introduces the topic for which Waterman's name is famous, approximate string matching. The next few chapter progress through dynamic programming and multiple alignments. The logical connections between the techniques shown are so tight that chapter boundaries are almost artificial. It was a real pleasure to see the computational and practical relationships laid out.
The final topics, RNA structure and phylogenetic trees, lack the continuity that characterized the first dozen chapters. The RNA structure may be the weakest chapter in the book, but still a very competent introduction.
Throughout, Waterman emphasizes mathematical rigor without insisting on uninformative theorems. Every topic is presented in rich detail, with special attention to scoring and background models. Perhaps there are newer discussions of some topics. I don't know of any clearer discussions, though. Best, I think, is how Waterman prepares the reader to ask all the right questions in any future discussion: what are the elements of the computation, how can elements be recombined, how good is a result, and how does the result stand out from the statistical background.
The final chapter is what a bibliography should be. It doesn't just list authors, titles, and dates of publication. It actually discusses the contribution that each source made to this book. Rather than leave the reader to wander aimlessly among obscure titles, Waterman shows which sources are most informative on which topics. I wish more authors took the time for such commentary.
This is a book worth having. It covers topics that I haven't seen elsewhere, and shows how many different topics relate to each other. It is rigorous without giving distracting detail. Most of all, it keeps the biology in sight of all calculations. Some authors seem to forget that anything exists but the arithmetic; Waterman puts the math clearly in the service of its subject. I enjoyed it immensely, and look forward to applying its content in my own research.
Packed full of good information.......2000-08-13
This book gives a good survey of the different techniques employed by computational biologists. After a brief review of molecular biology in Chapter 1, the author treats the mathematical modeling of restriction maps in Chapter 2 using graph theory. His presentation is somewhat hurried, but he does give references and gives the reader three exercises at the end of the chapter. Multiple maps are treated in Chapter 3, wherein the author first makes use of probability theory, via the Kingman subadditive ergodic theorem. The proof is omitted but the author does a good job of explaining its use in studying the double digest problem (DDP). The best part of this chapter is the author's explanation of the difficulties of using Kingman's results for solving the DDP, and goes on to discuss multiple solutions of the DDP. Graph theory is again used in the discussion. This sets up the discussion in Chapter 4, which outlines algorithms for the DDP. The author gives a very compact introduction to P- and NP-complete problems in the theory of computation, then proves that DDP is NP-complete. The author does a good job of discussing subsequent approximate methods used for the DDP, such as simulated annealing. Markov chains are introduced in the book here for the first time, but due to the shortness of the presentation, the reader should do outside reading as a back-up. The author does a great job of explaining the difficulties if measurement error is introduced in the DDP at the end of the chapter. Cloning is discussed in Chapter 5, with tools from probability theory used to deal with partial digest libraries. The chapter is really short though, and the working the problems at the end of the chapter is essential for the understanding the results of this chapter. The author switches gears in the next chapter, wherein physical maps are discussed. The discussion is fairly detailed and interesting. Sequencing is discussed in the next two chapters, and the treatment is very good. Hashing is introduced here, and psedocode is given throughout. The very important method of dynamic programming is outlined in Chapter 9, which is beautifully written, and again pseudocode abounds throughout. Genetic mapping is left out though, but the this, the longest chapter of the book, is a detailed introduction to this area. The results in this chapter are used to study multiple sequence alignment in Chapter 10, wherein hidden Markov models are introduced for the first time. The discussion of these models is very curt, but there are other books and notes available if the reader needs further guidance. The best chapter of the book follows, which discusses probability and statistics for sequence alignment. The theory of large deviations is brought in, and the author does an excellent job of discussing this important, and powerful theory. The reader's level of mathematical sophistication is assumed to be a lot greater than the rest of the book in this chapter. Knowledge of measure theory and martingales are assumed here. The author uses the very powerful tool of relative entropy, so indispensable in other applications of probability. The problem set at the end of the chapter is challenging but working them through is definitely worth the time involved. The next chapter also uses some heavy guns from probability theory to study sequence patterns. The author returns to matter of a more empirical nature in Chapter 13, which deals with RNA secondary structures. The reader with a background in simple combinatorial theory should find the reading straightforward and informative. Continuous-time Markov chains are introduced in the next chapter to study trees and sequences. The treatment here is rather hurried, so again the reader should work the exercises at the end of the chapter. The book ends with a discussion of the literature and references. All in all a very nice book, worth the price, and worth spending time reading. The only minus might be the total omission of actual source code, but that really was not the intent of the book. Readers with a strong mathematical background will like the book, as well as anyone interested in going into the area of computational biology.
Book Description
- Unique in its survey of the range of topics.
- Contains a strong, interdisciplinary format that will appeal to both students and researchers.
- Features exercises and web links to software and data sets.
Download Description
- Unique in its survey of the range of topics.
- Contains a strong, interdisciplinary format that will appeal to both students and researchers.
- Features exercises and web links to software and data sets.
Customer Reviews:
Great book!!!.......2004-12-07
A must have for anyone interested in otimization! Extremely well written and objective.
Recommended to scholars and graduate students.......2003-09-23
Introduction to Stochastic Search and Optimization provides comprehensive, current information on methods for real-world problem solving, including stochastic gradient and non-gradient techniques, as well as relatively recent innovations such as simulated annealing, genetic algorithms, and MCMC. It is written to be read and understood by graduate students, industrial practitioners, and experienced researchers in the field. Web links to software and data sets, and an extensive list of references of the book allows the reader to explore deeper into certain topic areas. I also found the index to be very comprehensive and carefully done. The appendices are as a refresher and summary of much of the prerequisite material. The book is somewhat unique in providing a balanced discussion of algorithms, including both their strengths and weaknesses. The book is among very few books that have integrated essential parts of statistical fields with optimization and decision making. The book's inclusion of a chapter on optimal experimental design is an example of such integration. The approaches discussed in the book could be used for financial decision making, forecasting, and quality improvement, among many other areas.
Average customer rating:
- An Excellent Introductory Text
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Introduction to Theoretical Population Genetics (Biomathematics)
Thomas Nagylaki
Manufacturer: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
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ASIN: 3540533443 |
Customer Reviews:
An Excellent Introductory Text.......1999-11-25
This is to my knowledge the best text available for an applied mathematician seeking to learn something about theoretical population biology. It is written clearly, well organized, and has a thorough index that makes it well suited as a reference text.
Book Description
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.
An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text.
The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.
Customer Reviews:
Good Theoretical GA Textbook.......2005-05-06
This book primarily deals with the theoretical side of genetic algorithms. If you are looking for practical knowledge of how to implement a GA you should look elsewhere. For all intents and purposes this is a textbook. It's heavy on theory and proofs, but doesn't always explain everything in depth (that's what class time is for). There are problems at the end of each chapter that can be assigned to students.
There are case studies of many academic projects that seem to drone on forever and aren't really that useful in helping you learn how to write your own GA. Chapter 1 gives an overview and provides all of the appropriate terminology. Chapter 5 gives an high-level overview of how to implement a GA. Those are the 2 must-read chapters, all of the others can be used as torture for CS students.
To recap, if you're teaching a class in artificial intelligence this book is good. If you're trying to figure out how to implement a GA to solve a practical problem not so good. That evens out to 3 stars for my rating. I recommend searching the web, there are a few good sites on GA programming.
Not for beginners.......2004-02-04
I have an engineering degree, and I found this to be a little tough to follow for two reasons:
1. Not enough step by step prodecure especially at the beginning. Mitchell is too quick to start with the math formulas. It turns out that Genetic Algorithms are fairly straight forward and easy to follow, but you have to read this book twice before you "get it" because Mitchell clouds the discussion with proofs and mathematical representations of systems. It is tough to follow.
2. Mitchell does a poor job of selecting meaningful examples to illustrate the points. A nice simple set of examples where the average person easily picture the system would have been delightful. Instead this author chooses to illustrate the Genetic Algorithms through uncommon neural networks amoung other exotic applications. I found myself struggling to understand both the example (I didn't know a thing about neural networks!) and the genetic algorithm.
When buying an Introduction type book, I expected it to be more 'down to earth'. this book is for advanced minds!
An introduction and much more.......2004-01-26
First it must be said that the book is not an introduction that the non-scientist will easily understand. Some knowledge of computer programming is assumed. It acknowledges this in the last paragraph of the preface. Many of the notations in the book are unfamiliar to business or financial readers. There is no mathematics beyond algebra so the aforementioned prerequisites are the main hills to climb.
Mitchell's book is an overview of genetic algorithm analysis techniques as of 1996. The author gives a history of pre-computer evolutionary strategies and a summary of John Holland's pioneering work. A description of the basic terminology is presented and examples of problems solved using a GA (such as the prisoner's dilemma). The second chapter discusses evolving programs in Lisp and cellular automata. Also included in this chapter is a discussion of predicting dynamical systems. This was the section that has the most interest for me. Also interesting was the summary in this chapter about putting GAs into a neural network so that the ANNs could evolve.
The fifth chapter discusses when to employ a GA for maximum success. I appreciate the clearly thought out discussion of when to choose a GA for a problem. Sometimes authors of these types of books mimic the man with a hammer that thinks everything looks like a nail.
A Great Introduction to Genetic Algorithms.......2002-12-07
This is a great place to start to learn about genetic algorithms. The writing is clear and not bogged down by jargon. The book is not overly technical; it is written for the layman and has a casual conversational style that is a pleasure to read.
About half of the book is devoted to presenting examples of studies that have used genetic algorithms. These examples are interesting in themselves and also serve to illustrate the variety of genetic approaches that are available. The book also presents conflicting points of view of experts about which algorithms work best and why. This is helpful in combatting the impression that a beginner sometimes gets that everything is simple and all the answers are known.
Good introduction for such a short book.......2002-04-07
Although short, this book gives a good introduction to genetic algorithms for those who are first entering the field and are looking for insight into the underlying mechanisms behind them. It was first published in 1995, and considerable work has been done in genetic algorithms since then, but it could still serve as an adequate introduction. Emphasizing the scientific and machine learning applications of genetic algorithms instead of applications to optimization and engineering, the book could serve well in an actual course on adaptive algorithms. The author includes excellent problem sets at the end of each chapter, these being divided up into "thought exercises" and "computer exercises", and in the latter she includes some challenge problems for the ambitious reader.
Chapter 1 is an overview of the main properties of genetic algorithms, along with a brief discussion of their history. The role of fitness landscapes and fitness functions is clearly outlined, and the author defines genetic algorithms as methods for searching fitness landscapes for highly fit strings. An elementary example of a genetic algorithm is given, and the author compares genetic algorithms with more traditional search methods. The author emphasizes the unique features of genetic algorithms that distinguish them from other search algorithms, namely the roles of parallel population-based search with stochastic selection of individuals, and crossover and mutation. A list of applications is given, and two explicit examples of applications are given that deal with the Prisoner's Dilemna and sorting networks. The author also gives a brief discussion as to how genetic algorithms work from a more mathematical standpoint, emphasizing the role of Holland schemas. The reader more prepared in mathematics can consult the references for more in-depth discussion.
The next chapter stresses the role of genetic algorithms in problem solving, beginning with a discussion of genetic programming. Automatic programming has long been a goal of computer scientists, and the author discusses the role of genetic programming in this area, particularly the work of John Koza on evolving LISP programs. In addition, she discusses the current work on evolving cellular automata and its role in automatic programming. The latter discussion is more detailed, this resulting from the author's personal involvement in artificial life research. Those interested in time series prediction tools will appreciate the discussion on the use of genetic algorithms to predict the behavior of dynamical systems, with an example given on predicting the behavior of the (chaotic) Mackey-Glass dynamical system. The author also gives applications of genetic algorithms in predicting protein structure, an area of application that has exploded in recent years, due to the importance of the proteome projects. The area of neural networks has also been influenced by genetic algorithms, and the author discusses how they have replaced the familiar back-propagation algorithm as a method to find the optimal weights.
Chapter 3 is more in line with what the author intended in the book, namely a discussion of the relevance of genetic algorithms to study the mechanisms behind natural selection. She discusses the "Baldwin effect", which gives a connection between what an organism has learned (a small time-scale process) to the evolutionary history of the Earth (a long time-scale process). A simple model of the Baldwin effect is given using a genetic algorithm, along with a discussion of the Ackley-Littman evolutionary reinforcement learning model, which involves the use of neural networks, and which is another computational demonstration of the Baldwin effect. In addition, the author discusses models for sexual selection and ecosystems based on genetic algorithms. These are the "artificial life" models that the author has been involved in, and she gives a very understandable overview of their properties.
Chapter 4 should suit the curiosity of the mathematician or computer scientist who wants to understand the theoretical justification behind the use of genetic algorithms. Again employing the Holland notion of schemas and adaptation as a "tension between exploration and exploitation", the author formulates a mathematical model, called the Two-Armed Bandit Problem, of how genetic algorithms are used to study the tradeoffs in this tension. The level of mathematics used here is very elementary with the emphasis placed on the intuition behind this model, with only a sketch of the model's solution given. To address the role of crossover in genetic algorithms, the author discusses in detail a class of fitness landscapes, called "Royal Road functions" that she and others have developed. The performance of the genetic algorithm employed is then compared against the three different hill-climbing methods. Formal mathematical models of genetic algorithms are also discussed, one of which involves dynamical systems, another using Markov chains, and one using the tools of statistical mechanics. The latter is very interesting from a physics standpoint but is only briefly sketched. The interested physicist reader can consult the references given by the author for further details.
Practical use of genetic algorithms demands an understanding of how to implement them, and the author does so in the last chapter of the book. She outlines some ideas on just when genetic algorithms should be used, and this is useful since a newcomer to the field may be tempted to view a genetic algorithm as merely a fancy Monte Carlo simulation. The most difficult part of using a genetic algorithm is how to encode the population, and the author discusses various ways to do this. She also details various "exotic" approaches to improving the performance of genetic algorithms, such as the "messy" genetic algorithms. One must also choose a selection method when employing genetic algorithms, and the author shows how to do this using various techniques, such as roulette wheel and stochastic universal sampling. In addition, genetic operators must also be chosen in implementing genetic algorithms, and the author emphasizes crossover and mutation for this purpose. Lastly, the values of the parameters of the genetic algorithm, such as population size, crossover rate, and mutation rate must be chosen. The author discusses various approaches to this. Although brief, she does give a large set of references for further reading.
Average customer rating:
- Don't start with this book
- Unsuitable for its stated purpose.
- Interesting but not very good for beginners
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Computational Molecular Biology: An Introduction
Peter Clote
Manufacturer: John Wiley & Sons
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Bioinformatics: Sequence and Genome Analysis
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Bioinformatics and Functional Genomics
ASIN: 0471872520 |
Book Description
Recently molecular biology has undergone unprecedented development generating vast quantities of data needing sophisticated computational methods for analysis, processing and archiving. This requirement has given birth to the truly interdisciplinary field of computational biology, or bioinformatics, a subject reliant on both theoretical and practical contributions from statistics, mathematics, computer science and biology.
* Provides the background mathematics required to understand why certain algorithms work
* Guides the reader through probability theory, entropy and combinatorial optimization
* In-depth coverage of molecular biology and protein structure prediction
* Includes several less familiar algorithms such as DNA segmentation, quartet puzzling and DNA strand separation prediction
* Includes class tested exercises useful for self-study
* Source code of programs available on a Web site
Primarily aimed at advanced undergraduate and graduate students from bioinformatics, computer science, statistics, mathematics and the biological sciences, this text will also interest researchers from these fields.
Customer Reviews:
Don't start with this book.......2004-02-13
In general I agree with the two previous reviews.
This book is not very good as an introduction. First read some other book such as Setubal and Meidanis, "Introduction to Computational Molecular Biology"; or Krane & Raymer, "Fundamental Concepts of Bioinformatics". These books have more readable narrative and examples.
The writing in this book is obtuse. It is written like an advanced abstract math book, not like an ostensibly applied science book. The notation is unnecessarily intricate. Even though it says "Introduction" in the title, there are very few tutorial examples. This is just for mathematicians/computer scientists: no biologist I have ever known would/could read this and really understand the algorithms.
This book does, however, have one of the more complete detailed descriptions of various algorithms used for sequence matching, etc. If you have read some other books and are looking for more details on algorithms, then this is your book. But I'm still waiting for THE ultimate Computational Biology book!
Unsuitable for its stated purpose........2001-03-21
The book purports to be a "self-contained introduction" to computational biology. It fails on both counts due to its excessive ambition, its opaque pedagogy, and a large number of significant typographical errors, such as entire subroutines missing from pseudocode examples. Undergraduates seeking an accessible survey are advised to look elsewhere.
That said, the mathematical rigor of the text makes it ideal for students who have moved beyond the need for accessible surveys and wish to improve their fundamental understanding of the field.
Interesting but not very good for beginners.......2000-11-23
This is an unusual book. The authors obviously have not been aquinted with biomolecular sequence analysis and fail to give state-of-the-art references to research work in this field. The same comment applies to the description of applications of Shannon communication theory to DNA and protein sequence analysis. The enormous impact of these applications in the 1970s, 1980s and 1990s is not reflected in the book and one could wonder why the authors bother to write of Shannon theory at all. In addition to the above misgivings the authors decided to confuse the reader by including a discussion of quite controversial relationship between Shannon entropy and thermodynamic entropy. Both computational and laboratory biologists will not benefit from this kind of confusion. Mathematicians and computer scientist will probably be mislead by a superficial treatment of this quite intricate topic. Physicists and chemist will probably be able to sort out useful information from over-interpretations but they may wonder why this issue is discussed in a computational biology text.
Despite the above critique I like the book. Organization of this text is interesting and distinctly different form other books in the field. Chapters on sequence alignment and phylogenetic trees are most interesting and original. They should probably be read in conjunction with more systematic textbooks such as Gusfield's "Algorithms on strings, trees and sequences" or Li's "Molecular evolution." Despite many misgivings (see the beginning paragraph of this review) the mathematical primer (chapter 2) is very much worth reading for its originality and compactness. Particularly sections about probability distributions and combinatorial optimization can be useful for non-mathematicians and interesting for those who are mathematically literate. However, care should be exercised (see the beginning paragraph) while reading sections about entropy and about optimality of the genetic code. Chapter 1 about principles of molecular biology is not very good for non-biologists because it is too compact. Chapter about structure prediction is also too compact to be either understandable to non-specialists or enjoyable by the experts. If the authors' ambitious approach was to be sustained, this chapter should probably be expanded to the size of entire book. Exercises at the end of every chapter of the book are interesting and worth the reader's attention. It would probably be good to have access to solutions of all exercises but it is a minor problem.
In summary: it is an interesting book but it should be read in conjunction with other texts. It should not be recommended to the beginners in computational biology. Mathematically seasoned readers will enjoy reading selected parts of this book. It would be nice if the publisher could consider lowering price of this book (already in paperback.)
Book Description
Until now, those interested in the emerging field of computational molecular biology have used surveys and technical articles collected from many sources. Introduction to Computational Molecular Biology brings together major results in the field, in coherent and readable format. Setubal and Meidanis present a representative sample of problems in molecular biology, focusing on the algorithms that have been proposed to solve them. Readers will find background material on molecular biology, definitions of key terms, descriptions of models, and a full sample of algorithmic results. Key theoretical computer science concepts are emphasized, such as the improvement in asymtotic running time with better algorithms, the contrast between heuristics and an algorithm with guarantees, and the difficulty posed by NP-complete problems. Algorithms for sequence comparison, including the popular BLAST and FAST programs, are covered. Introduction to Computational Molecular Biology serves readers from both the mathematical and computing sciences as well as molecular biology. The authors assume a basic chemistry background and some training in college-level discrete mathematics and algorithms.
Customer Reviews:
Detailed broad overview of algorithms.......2005-11-16
We used this book in a bioinformatics class. It can take a whole semester to discuss this little book. The approach here is algorithmic. It explains fundamental bioinformatics algorithms in detail. In comparison to Pevzner's Computational Molecular Biology, it's more practical, and has less mathematical formalisms. This book peaks inside the black box of bioinformatics algorithms.
It's rigourous and for mathematics and computer science folks, and some material is difficult. Biologists may have a hard time with it, because of algorithm analysis and the required familiarity with graph theory. On the other hand, computer science folks shouldn't really take the introductory chapter on biology seriously.
Take a look at its Table of Contents to see what it covers, there's no point repeating it here.
Must have for anyone interested in implementing bioinformatics algorithms. On the other hand, if you're a biologist simply interested in how to use bioinformatics in your work, e.g., BLAST, there's no point reading this book.
Strong on algorithm analysis.......2005-02-03
This is a good book on some of the algortihms used in bioinformatics. That term may not have been in wide use back in `97, leading to the title that suggests a lot more focus on the molecules than is in fact present.
The book starts with two brief chapters on biology and mathematical basics - graph theory and algorithm analysis. I'm sorry to say that these really are too brief. A reader who comes in with little knowledge of either topic will probably leave with about the same level of knowledge.
After that, the authors give coverage of the basics, as `97 writers saw them: approximate string matching, fragment assembly, mapping, trees, and a discussion of the reversals that occur in DNA over evolutionary time. Each topic is presented carefully, in a number of variations, and with formal analysis of the algorithmic complexity. That last won't do much for the biologists in the crowd, but gives programmers a good idea of how each technique will behave as the problems grow larger (and they always do). The presentation generally stick with the most popular algorithms, emphasizing detailed presentation over breadth of coverage. Multiple alignment, in particular, could have used a lot more pages. Also, topics like assembly and restriction digests aren't at the forefront of analysis any more. They're important, but good algorithms exist in widely available tools, and more advanced analyses tend to attract more attention these days. The section on genome rearrangements is quite good, but seems to stand alone - it could have been one input into tree building, but the authors don't draw any clear relationships between the reorderings and any other problems.
The section on structure prediction is definitely showing its age. RNA structure prediction has come a long way since this was written, and protein structure prediction has come even farther. Discussions of the basic ideas are good, but a more recent reader will want a lot more development. The final section, on using DNA as a material for performing computations, is interesting but hardly mainstream. The authors use one or two problems as case studies, but don't present the kinds of tools that can be applied to lots of different problems, just a few point solutions.
This book's value, today, lies mostly in the clarity of its complexity analysis and in the pseudocode that gives a programmer a step in the right direction. It has a few unusual items, like a highly generalized gap model for approximate string matching. On the whole, though, more recent books present most of the same material at least as well, and cover more up-to-date topics.
When it was new, I might have given this book a five star rating. Times have changed, though, and I have to rate this book among the others on the shelves now.
//wiredweird
Concise and to-the-point.......2002-02-21
This is a really nice introduction to the most commonly used algorithms in bioinformatics. It is not really a general introduction to molecular biology or to computer science, and to make best use of this book a reader probably needs some prior exposure to both. But, for someone who has had a basic course in say genetics, and a basic programming course that covers simple data structures and algorithms etc., this volume provides all they will need to understand what is really happening when they run a BLAST search, for instance. A serious computer-science type person will probably not find the alogorithms described here very interesting, because they aren't meant to be elegant or interesting, just useful. I think a reader would have to have some direct interest in bioinformatics per se in order to enjoy this book. One thing that I find particularly nice is that the length of the chapters is just right so that you can read through a chapter in a single sitting, and because the chapters are largely independent of one another, its a handy book to have around and pick up when one has a little spare time. I recommend it very strongly.
Not a good introduction book.......2001-11-17
I do not think it is a good introduction book for biologists to learn computational biology. The authors should have used more figures and examples to illustrate the concepts. Also, I do not like the norrow margins of this book. I like to write comments and my understanding in the books when I read. There is simply no place to write.
Not a good introduction book.......2001-11-17
I do not think it is a good introduction book. The authors should have used more figures to illustrate the concepts. Also, I do not like the norrow margins of this book. I like to write comments and my understanding in the books. There is simply no place to write!
Average customer rating:
- nice introduction to stat methods in genetic research
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Introduction to Statistical Methods in Modern Genetics (Asian Mathematics Series)
M.C. Yang
Manufacturer: CRC
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ASIN: 9056991345 |
Book Description
Although the basic statistical theory behind modern genetics is not very difficult, most statistical genetics papers are not easy to read for beginners in the field, and formulae quickly become very tedious to fit a particular area of application. Introduction to Statistical Methods in Modern Genetics distinguishes between the necessary and unnecessary complexity in a presentation designed for graduate-level statistics students. The author keeps derivations simple, but does so without losing the mathematical details. He also provides the required background in modern genetics for those looking forward to entering this arena. Along with some of the statistical tools important in genetics applications, students will learn: · How a gene is found · How scientists have separated the genetic and environmental aspects of a person's intelligence · How genetics are used in agriculture to improve crops and domestic animals · What a DNA fingerprint is and why there are controversies about it Although the author assumes students have a foundation in basic statistics, an appendix provides the necessary background beyond the elementary, including multinomial distributions, inference on frequency tables, and discriminant analysis. With clear explanations, a multitude of figures, and exercise sets in each chapter, this text forms an outstanding entrée into the rapidly expanding world of genetic data analysis.
Customer Reviews:
nice introduction to stat methods in genetic research.......2002-03-12
Mark Yang is a University of Florida Professor of Statistics. He has a strong engineering background and good training in biology and biochemistry. This interest led him to a careful understanding of the subject and the modern methods that are employed which rely more and more on statistics.
What is particularly nice about the book is its simplicity. The study of genetics can be very complicated. Also various useful statistical tools including Markov chain Monte Carlo, bootstrap methods, the EM algorithm and multiple comparisons can be complex as well. But Professor Yang is careful to extract only the essentials to make both the statistics and teh genetics understandable to the layperson.
On the statistical side the emphasis is on the Hardy-Weinberg equilibrium and the likeihood equations in pedigree analysis. Rather than go through complicated Markov Chain theory to demonstrate the equilibrium result Professor Yang demonstrates using simple conditional probabilities. This approach works because the convergence occurs exactly after two steps.
For the benefit of the statistician who knows nothing about genetics Chapter 1 is a primer on molecular genetics that gives the essential to understand recombinant methods, recombination fraction, the Haldane distance mapping function, linkage, RFLP and PCR techniques needed for subsequent chapters.
Most of the work in genetic inference involves calcualting the formula for the likelihood equations. This involves basic discrete probability calculations and combinatorics for the main part.
Once we learn the basics we are ready to understand gentic fingerprinting which is used to establish paternity or identify criminals.
The book has interesting examples and exercises and many good references. The only major drawback to the book is that it does not cover microarray analysis.
However, I recently took a short course from Dr. Yang based on this book. In the course he spent a great deal of time on microarray technology by supplementing the text with some notes on microarrays and a recent paper he published on the reliabiity on the technique. The text certainly gives the reader the proper background for microarray analysis or other genetic developments.
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