History: Fiction or Science? (Chronology, No. 1)

Book Description

Recorded history is a finely-woven magic fabric of intricate lies about events predating the sixteenth century. There is not a single piece of evidence that can be reliably and independently traced back earlier than the eleventh century. This book details events that are substantiated by hard facts and logic, and validated by new astronomical research and statistical analysis of ancient sources.

Customer Reviews:

Calculations are only as good as your numbers.......2007-08-03

Yes, we can all agree that mainstream history is nearly 100% BS due to politics, economics, ego, problems with dating techniques, and various conspiracies. Agreed. But, I've been researching the distinct possibility that human history (in terms of civilizations) are much more ancient than we've been told, so coming across this book was very interesting to me. I wondered how Fomenko could be wrong (if at all) because he is very persuasive in his presentations. Then it dawned on me. If at previous times in prehistory, due to the various catastrophies that are well documented (comets, asteroids, planetary disruptions, plasma discharge, pole reversals, etc) the Earth was in a different position in relation to the sun, different tilt on its axis, different orbit, different rotation (in terms of velocity and DIRECTION), and the continents were in different positions, then would this not cause the ancients to see the sky (constellations) differently? In other words, is Fomenko making erronious assumptions about the physics of the Earth in pre-history, which then corrupt his data with regards to dating the relevant astrology? The last event to seriously disrupt our planet occured roughly 3500 years ago, according to other good researchers, so is it possible Fomenko has been confused by this? The vastly different physics of our planet in the not so distant past may explain this confusion, which is not to say the "mainstream" version of history is correct; on the contrary. I am not an expert in these fields, but wanted to see if this idea could spark discussion.

Pants on fire?.......2007-07-19

Will people ever read before spamming? Yes, Jesuits could not rewrite world history alone, they had help. Anyway, Dr Prof Acad A.Fomenko does not point to jesuits as the driving force of world wide history manipulation in published volumes 1,2,3;, actually he barely mentions the poor devils. Check it with 'Search inside' feature, please. China is rarely mentioned either, in fact, Dr Fomenko is completely eurocentric. Right, his theory contradicts all mainstream schools of history, because in their actual state they are all built on blatantly erroneus chronology. You don't need a mysterious cabal (conspiracy) to falsify history, the falsification is its modus operandi. It is inherent to history(ians) to falsify (distort) events, as it is inherent to humans to boast as it is inherent to power (authority) to legimize itself by referrring to glorious past made to its own order. Dr Prof Fomenko and team have identified scores of instances of such manipulation in Russian, European, etc.. history, and delivered valid statistical proof thereof. His own 'reconstruction' is completely another story. Forget c14 as a valid method of dating. W.Libby has initially discovered a brilliant method of INDEPENDENT dating. Too bad, c14 method has become a joke after a forced marrige with dendrochronology with consensual chronological scale inbuilt. Radiocarbon method can't stand blind tests, but is so very productive as a rubberstamp.

Accepted History & Chronology Must Be Changed. .......2007-04-09

There is no doubt that history as most know it is a sham, & institution's version of History both University & Church is fradulent & inaccurate. Everything was established with an agenda, The real "Dark Ages" are now when we have access to incredible amounts of information past authorities & more important 'common folk' didn't have but our institutions & educators are slow to evolve because of what has ignorantly & arrogantly been taught for too long. This is on many subjects not just Chronology.

For anyone to question "Why would a Mathematician have anything credible to say of History?" The answer is from Dr. Fomenko's preface in the book: "It would be worthwhile to remind the reader that in the XVI-XVII century Chronology was considered to be a subdivision of Mathematics." These volumes could possibly be some of the most important works to date & should be read by everyone with an interest in History, especially professors & educators who have a duty to the public. I have read both books & must say that 'Chronology 1' has some very eye opening & revolutionary information. Even if these volumes are part true the implications are profound & opens the doors to further investigations & questions which must be done. I speak several different lanquages & must say the logic Dr. Fomenko uses with "inflection" of words & words being read from left to right in one region & right to left in another then written backwards, the removal of vowels & get down to basics of words, or different cities & locations having the same name etc. is correct. Vowel usage has always been optional & varied, actually complicating linquistics & study. The first thing one has to understand is that words never had a fixed spelling in history like we do now, the spelling of words was mutable & regional, as well as names & titles of people were vast, varied & changed, NOTHING WAS FIXED or understood linear. Matters of Life & Death as well as financial profiteering yesterday & today were & are made with ignorant, illogical & conspiratorial views of history & reality, it's time people get closer to the Truth & society collectively grow up.

Very Interesting.......2007-03-07

It is a good proposal and I believe it will mature into something even better in the future. I think it deserves to be read.

History as Science Fiction.......2007-01-10

Anatoly Fomenko has written a very intriguing book, full of pictures, charts, and computer 'proof' of his thesis: backwards of AD900 we don't really know what happened or when. Between AD900 and AD1600 there is more certainty, but there is still a lot of fuzzy ground, and things don't get reliable until we get past the 1600's where the printing press made it very difficult for the perpetrators of this timeline manipulation to change anything that had been committed to print. The Dark Ages did not happen. Books were burned for a reason. One organization has doubled the actual length of its existence by expanding the real chronology. Read why.

I had always wondered why Christ died about AD33 and yet men waited until the 11th century to form the Knights Templar, the Cathars, etc and go after the Holy Land by force. Why the 1000 year gap? Turns out there wasn't more than a 10-12 year gap and he proves it using astronomy. This also implies that the planet is not as old as we have been told, and current Christian and other creationist scientists are already championing that idea without being aware of Fomenko's book. The two groups, creationist scientists and the Russian mathematical analysts corroborate each other. Fascinating.

Of course, all this flies in the face of what we have been told traditionally is the 'proper' chronology of western civilization, and most readers will experience 'cognitive dissonance' in reading this book. It means that our history going backwards from AD1600 becomes progressively more incorrect and unreliable until it cannot be trusted at all... in the space of 700-800 years.

Naturally, the curious, open-minded reader will want to know WHO did this, WHY, and did any of the events we think of as really ancient ever happen?
Dr. Fomenko is a respected scientist/mathematician at Moscow State University who has already answered these questions to the satisfaction of his initially skeptical colleagues. Most of them are now believers, a few still refuse to believe (the usual diehards), and of course the western press has ignored Fomenko's work -- for obvious reasons when you read the book. The ones who perpetrated this chronology ruse have a lot to answer for. They are still with us. That's why this book is a well-kept secret.

I gave the book a 4-star rating because I was unable to check out some of his claims; those I checked were as he said. But if even 1/3 of his claims are true, this punches a big hole in what we think is our history, the meaning of western civilization, our educational process (for repeating the ruse as gospel), and the trustworthiness of the organization that perpetrated this ruse, well-intentioned or not.

This book relates to current research into a Young Earth paradigm, to John Keel's discoveries about our planet, and Fr Malachi Martin's insights (in his now out-of-print books). We are indeed sheep who are manipulated and kept ignorant -- for a reason. While knowing what these men have to say may be the "booby prize" (as in: 'what can you do with this knowledge?'), it will provide interesting reading. Didn't someone say: "...and the Truth will set you free."?? For you to judge if this book contains the truth.

Biostatistical Genetics and Genetic Epidemiology

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Biostatistical Genetics and Genetic Epidemiology

Manufacturer: Wiley
ProductGroup: Book
Binding: Hardcover

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ASIN: 0471486310

Book Description

Human Genetics concerns the study of genetic forces in man. By studying our genetic make-up we are able to understand more about our heritage and evolution. Some of the original, and most significant research in genetics centred around the study of the genetics of complex diseases - genetic epidemiology.

This is the third in a highly successful series of books based on articles from the Encyclopedia of Biostatistics. This volume will be a timely and comprehensive reference, for a subject that has seen a recent explosion of interest following the completion of the first draft of the Human Genome Mapping Project. The editors have updated the articles from the Human Genetics section of the EoB, have adpated other articles to give them a genetic feel, and have included a number of newly commissioned articles to ensure the work is comprehensive and provides a self-contained reference.

Book Description

Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are following closely behind the advances in data generation methods. The statistical methods required by bioinformatics present many new and difficult problems for the research community.

This book provides an introduction to some of these new methods. The main biological topics treated include sequence analysis, BLAST, microarray analysis, gene finding, and the analysis of evolutionary processes. The main statistical techniques covered include hypothesis testing and estimation, Poisson processes, Markov models and Hidden Markov models, and multiple testing methods.

The second edition features new chapters on microarray analysis and on statistical inference, including a discussion of ANOVA, and discussions of the statistical theory of motifs and methods based on the hypergeometric distribution. Much material has been clarified and reorganized.

The book is written so as to appeal to biologists and computer scientists who wish to know more about the statistical methods of the field, as well as to trained statisticians who wish to become involved with bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level, but with an emphasis on material relevant to later chapters and often not covered in standard introductory texts. Later chapters should be immediately accessible to the trained statistician. Sufficient mathematical background consists of introductory courses in calculus and linear algebra. The basic biological concepts that are used are explained, or can be understood from the context, and standard mathematical concepts are summarized in an Appendix. Problems are provided at the end of each chapter allowing the reader to develop aspects of the theory outlined in the main text.

Warren J. Ewens holds the Christopher H. Brown Distinguished Professorship at the University of Pennsylvania. He is the author of two books, Population Genetics and Mathematical Population Genetics. He is a senior editor of Annals of Human Genetics and has served on the editorial boards of Theoretical Population Biology, GENETICS, Proceedings of the Royal Society B and SIAM Journal in Mathematical Biology. He is a fellow of the Royal Society and the Australian Academy of Science.

Gregory R. Grant is a senior bioinformatics researcher in the University of Pennsylvania Computational Biology and Informatics Laboratory. He obtained his Ph.D. in number theory from the University of Maryland in 1995 and his Masters in Computer Science from the University of Pennsylvania in 1999.

Comments on the First Edition. "This book would be an ideal text for a postgraduate course…[and] is equally well suited to individual study…. I would recommend the book highly" (Biometrics). "Ewens and Grant have given us a very welcome introduction to what is behind those pretty [graphical user] interfaces" (Naturwissenschaften.). "The authors do an excellent job of presenting the essence of the material without getting bogged down in mathematical details" (Journal. American Staistical. Association). "The authors have restructured classical material to a great extent and the new organization of the different topics is one of the outstanding services of the book" (Metrika).

Customer Reviews:

Most Elegant Account of Bioinformatics.......2004-11-27

I was impressed with the 1st edition of this book for its most comprehensive and elegant of statistical techniques in bioinformatics. The book is slightly below the level of the now classic M S Waterman (1995)book:Introduction to Computational Biology: Maps, Sequences and Genomes. But this book is more update in some areas and has much more background materials on probability and statistics, which should provide a solid basis for understanding bioinformatics. Its pedagorical sense is unparalleled. It would make a very good choice for a stat/math oriented introduction to bioinformatics (as opposed to algorithimc/database oriented approach in cs).

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Get a solid foundation for microarray data analysis.
a great book to read about microarray data analysis
Simple Great
Excellent book. Highly recommended!
Introduction to Statistical Data Analysis of Microarrays

Data Analysis Tools for DNA Microarrays
Sorin Draghici
Manufacturer: Chapman & Hall/CRC
ProductGroup: Book
Binding: Hardcover

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Similar Items:

Microarray Bioinformatics
Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health)
Statistics for Microarrays: Design, Analysis and Inference
Microarray Gene Expression Data Analysis: A Beginner's Guide
Design and Analysis of DNA Microarray Investigations

ASIN: 1584883154

Book Description

Technology today allows the collection of biological information at an unprecedented level of detail and in increasingly vast quantities. To reap real knowledge from the mountains of data produced, however, requires interdisciplinary skills-a background not only in biology but also in computer science and the tools and techniques of data analysis. To help meet the challenges of DNA research, Data Analysis Tools for DNA Microarrays builds the foundation in the statistics and data analysis tools needed by biologists and provides the overview of microarrays needed by computer scientists. It first presents the basics of microarray technology and more importantly, the specific problems the technology poses from the data analysis perspective. It then introduces the fundamentals of statistics and the details of the techniques most commonly used to analyze microarray data. The final chapter focuses on commercial applications with sections exploring various software packages from BioDiscovery, Insightful, SAS, and Spotfire. The book is richly illustrated with more than 230 figures in full color and comes with a CD-ROM containing full-feature trial versions of software for image analysis (ImaGene, BioDiscovery Inc.) and data analysis (GeneSight, BioDiscovery Inc. and S-Plus Array Analyzer, Insightful Inc.). Written in simple language and illustrated in full color, Data Analysis Tools for DNA Microarrays lowers the communication barrier between life scientists and analytical scientists. It prepares those charged with analyzing microarray data to make informed choices about the techniques to use in a given situation and contribute to further advances in the field.

Customer Reviews:

Get a solid foundation for microarray data analysis........2007-02-18

I'm more than 2/3 through the book and I've never encountered a topic that I feel could have been better presented. My definition of a Great book is that I can understand and follow it, and this definitely is a Great book! Thanks to the author for writing such readable text. This text has not made it to my bookshelf at work, it stays on my desk.

a great book to read about microarray data analysis.......2006-08-07

I have entered the area of microarray data analysis three years ago, having an engineering/machine learning background which includes good knowledge of statistics. After reading many journal papers about particular algorithms for microarray data analysis, I felt the need to read a book so that I could get the big picture of the field. At the beginning I was skeptical about reading Draghici's book because it was recommended to me as "excellent" by a biologist. I was pretty sure that given my background I will get bored of it quickly. My intuition failed me in this case because after reading it, I found it too as being far from ordinary, and answering my needs as well.

The book is an easy-to-follow introduction to the area of microarray data analysis covering areas from image analysis and preprocessing, to differential expression, clustering, and high level analysis such as ontological analysis. The book is particularly useful in underlying common pitfalls with microarray data. Examples include failing to correct for multiple testing in microarray experiments and the misuse or overuse of the clustering algorithms. Abounding examples and clear illustration are given to support every single aspect treated in the text. In my opinion, graduate level students in biology, bioinformatics and statistics can greatly benefit from the lecture of this book.

Another positive aspect is the fact that, with the exception of one chapter about the available commercial software, this book was written by just one author. This gives a continuity of ideas and a consistency of notations and terms throughout the entire book. This is usually not found in many other books on this topic as they are sometimes just edited collections of chapters written independently by different authors (see for instance the text by Berrar et. al which has about 40 contributors).

A great incentive for me in writing this review was reading an overzealous critique to this book, written by Eric Wu in this webpage. I found some of his comments to be particularly misleading and out of context. For instance he says "the book only deals with the bare minimum of data analysis". Compared with other books in the field, the topics about data analysis covered in the book are not only more numerous but much more thoroughly explained. This book does not expedite the reader to some references but cares about explaining the things. If this book is the "bare minimum" at 500 pages, how is Mr. Wu going to characterize the other well known books in the field such as Knudsen, Simon, Speed, Baldi, etc. which have at most half as many as this book has. Knudsen, for instance, takes the reader from absolute measurements to and including ANOVA in 17 pages. Draghici covers the same topics in 7 chapters or about 250 pages, and that would be without counting the chapters on the basic statistics or image analysis. Another example of biased assessment is when Mr. Wu says "Exploratory data visualizing and data mining algorithms are not covered thoroughly in this book. For example, principal component analysis (PCA) is presented as a subsection of a chapter." The PCA description in the book is more than just fine to me. The book is not supposed to be an encyclopedia of statistics. What the reader needs to know is how PCA can help with the visualization of these multidimensional data sets and not necessarily give all the details about PCA.
A last example I give of superficial judgment in Mr. Wu's view is the so called "inflation of Type I error rate". Mr. Wu says: "... if the probability of making a correct conclusion excludes the probability of making Type II errors, 1 - p should be stated as the probability of not making Type I errors".. In general, this statement would be true. However, the paragraph from the book to which Mr. Wu is referring to actually starts by saying: "When the t statistic for a gene is more extreme than the threshold..." etc. If the observed statistic is more extreme than the threshold, the statistical reasoning requires us to reject the null hypothesis. In this case type II errors (false negatives) CANNOT occur. Hence, in this case, the probability of drawing the correct conclusion is indeed 1-p, exactly as stated in the book.

Overall, I find that the value you get per dollar spent when buying this book is high, and thereby I would strongly recommend it.

Dr. Adi L. Tarca, Windsor (CANADA)

Simple Great.......2006-05-16

This book is a must to understand fundamental statistical analysis of microarray data. Must have it.

Excellent book. Highly recommended!.......2006-04-04

Being a book worm, as soon as I started working with microarrays I bought a bunch of books on the subject. After six months working with this technique and reading chapters on all the books I've bought I can say with certainty that Draghici's is the best introductory book on microarrays. Other books around are better at describing protocols or explaining the math involved in microarray data analysis but Draghici's book does a very good job at explaining how to analyse microarray data for the biologist (and maybe for other publics but statisticians). Everytime some friend ask me for hints on chapters or books to read for learning (or re-learning) statistics I suggest this book. The first chapters are an excellent review of the basics of statistics necessary for day to day practice. The only complain I have is that the shareware software that comes with the book does not work anymore (it's trial period has already expired and therefore it is not possible to install it even if you get a brand new book). I read this book from cover to cover and I think that, considering how readable it is, anyone could do it.

Introduction to Statistical Data Analysis of Microarrays.......2004-09-28

The targeted audience of this book is biologists who are eager to get an understanding of the analysis tools they use for microarrays. The book does an excellent job addressing this tier of audience.

The book has plenty of examples. Almost all the examples, whether fake or real, are microarray-related. Whenever needed, figures or charts are provided to illustrate ideas. A few chapters that introduce basic statistical concepts provide solved problems and exercises. All these efforts are worthwhile making difficult statistical concepts easy to understand in the context of microarrays and making the book especially valuable for biologists who do not have strong background in statistics.

This book has an emphasis on major statistical aspects of microarray data analysis. There are 17 chapters in this book. About 8 of them are directly related to statistics. Especially, there is one whole chapter devoted to multiple hypothesis testing, one chapter for ANOVA, and one chapter for experimental design. The above subjects are presented in a thorough, yet easy-to-follow style. Statistical issues are often not well addressed in published papers using microarrays. This book on microarray data analysis does an excellent job emphasizing this aspect.

The title of the book indicates "data analysis". However, since this is not a clearly defined term, you should be aware that the book only deals with "the bare minimum" of data analysis. That is routines, such as normalization, transformation, statistical testing, and clustering, that have to be carried out each and every time. Exploratory data visualizing and data mining algorithms are not covered thoroughly in this book. For example, principal component analysis (PCA) is presented as a subsection of a chapter. It does not provide explanations on concepts such as loading factors nor scree test. Series data (e.g. time series) are on two pages only and there is no mention of Fourier transformation. Support vector machine (SVM), which is widely used today as a supervised classification method, is not presented at all.

As I mentioned at the beginning, the targeted audience is biologists. If you are a statistician or a bioinformatician who wants to mathematically explore data analysis algorithms, you should look somewhere else. You may be disappointed that many concepts are not rigorously or accurately defined in this book. For example, the book uses capital letters to denote random variables. But the concept of random variables is not rigorously defined in the book. One of the consequences is the weak definition of mathematical expectation. Another example is the inflation of Type I error rate. On page 220, the author claims that the probability of "drawing the correct conclusion" is 1 - p, where p is the calculated probability of a statistic versus a parameter. However, if the probability of making a correct conclusion excludes the probability of making Type II errors, 1 - p should be stated as the probability of not making Type I errors.

In summary, this is a good book on microarray analysis tools for biologists using microarrays. However, people who are seeking in-depth descriptions of these algorithms should look somewhere else.

Gene Genealogies, Variation and Evolution: A Primer in Coalescent Theory

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Gene Genealogies, Variation and Evolution: A Primer in Coalescent Theory
Jotun Hein , Mikkel H. Schierup , and Carsten Wiuf
Manufacturer: Oxford University Press, USA
ProductGroup: Book
Binding: Paperback

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Statistical Methods in Molecular Evolution (Statistics for Biology and Health)
Computational Molecular Evolution (Oxford Series in Ecology and Evolution)
Coalescent Theory: An Introduction
Population Genetics and Microevolutionary Theory
Principles of Population Genetics, Fourth Edition

ASIN: 0198529961

Book Description

Coalescent theory tells us what gene genealogies are expected to look like if populations have different demographic histories - i.e, population size, structure, etc. The aim of this book is provide an accessible introduction to Coalescent Theory with a view towards data analysis. Coalescent
Theory has in the last two decades moved from being an obscure technique that appealed to mathematical population geneticists to a central tool in data analysis of DNA sequences. The completion of the sequencing of the human genome and accompanying determination of SNPs and haplotypes will increase
its importance even further. This textbook, rich in examples and illustrations, is suitable for a graduate course in statistics, population-, molecular-, and medical genetics.

Introduction to Computational Biology: Maps, Sequences and Genomes (Interdisciplinary Statistics)

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A modern classic
Packed full of good information

Introduction to Computational Biology: Maps, Sequences and Genomes (Interdisciplinary Statistics)
Michael S. Waterman
Manufacturer: Chapman & Hall/CRC
ProductGroup: Book
Binding: Hardcover

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Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology
Computational Genome Analysis: An Introduction (Statistics for Biology & Health)
An Introduction to Bioinformatics Algorithms (Computational Molecular Biology)
Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids
Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health)

ASIN: 0412993910

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.

Introduction to Stochastic Search and Optimization

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Great book!!!
Recommended to scholars and graduate students

Introduction to Stochastic Search and Optimization
James C. Spall
Manufacturer: Wiley-Interscience
ProductGroup: Book
Binding: Hardcover

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Monte Carlo Statistical Methods (Springer Texts in Statistics)
Stochastic Optimization Methods
Practical Methods of Optimization
Introduction to Stochastic Programming (Springer Series in Operations Research and Financial Engineering)
Numerical Optimization (Springer Series in Operations Research and Financial Engineering)

ASIN: 0471330523

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.

Statistical Methods in Molecular Evolution (Statistics for Biology and Health)

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Excelent Reference Book on Molecular Evolution

Statistical Methods in Molecular Evolution (Statistics for Biology and Health)

Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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Similar Items:

Gene Genealogies, Variation and Evolution: A Primer in Coalescent Theory
Computational Molecular Evolution (Oxford Series in Ecology and Evolution)
Inferring Phylogenies
Population Genetics and Microevolutionary Theory
Phylogenetic Trees Made Easy: A How-to Manual, Third Edition

Accessories:

Evolutionary Bioinformatics
Introductory Statistics with R
Fundamentals of Data Mining in Genomics and Proteomics

ASIN: 0387223339

Book Description

In the field of molecular evolution, inferences about past evolutionary events are made using molecular data from currently living species. With the availability of genomic data from multiple related species, molecular evolution has become one of the most active and fastest growing fields of study in genomics and bioinformatics.

Most studies in molecular evolution rely heavily on statistical procedures based on stochastic process modelling and advanced computational methods including high-dimensional numerical optimization and Markov Chain Monte Carlo. This book provides an overview of the statistical theory and methods used in studies of molecular evolution. It includes an introductory section suitable for readers that are new to the field, a section discussing practical methods for data analysis, and more specialized sections discussing specific models and addressing statistical issues relating to estimation and model choice. The chapters are written by the leaders of field and they will take the reader from basic introductory material to the state-of-the-art statistical methods.

This book is suitable for statisticians seeking to learn more about applications in molecular evolution and molecular evolutionary biologists with an interest in learning more about the theory behind the statistical methods applied in the field. The chapters of the book assume no advanced mathematical skills beyond basic calculus, although familiarity with basic probability theory will help the reader. Most relevant statistical concepts are introduced in the book in the context of their application in molecular evolution, and the book should be accessible for most biology graduate students with an interest in quantitative methods and theory.

Rasmus Nielsen received his Ph.D. form the University of California at Berkeley in 1998 and after a postdoc at Harvard University, he assumed a faculty position in Statistical Genomics at Cornell University. He is currently an Ole Rømer Fellow at the University of Copenhagen and holds a Sloan Research Fellowship. His is an associate editor of the Journal of Molecular Evolution and has published more than fifty original papers in peer-reviewed journals on the topic of this book.

From the reviews:

"...Overall this is a very useful book in an area of increasing importance." Journal of the Royal Statistical Society

"I find Statistical Methods in Molecular Evolution very interesting and useful. It delves into problems that were considered very difficult just several years ago...the book is likely to stimulate the interest of statisticians that are unaware of this exciting field of applications. It is my hope that it will also help the 'wet lab' molecular evolutionist to better understand mathematical and statistical methods." Marek Kimmel for the Journal of the American Statistical Association, September 2006

"Who should read this book? We suggest that anyone who deals with molecular data (who does not?) and anyone who asks evolutionary questions (who should not?) ought to consult the relevant chapters in this book." Dan Graur and Dror Berel for Biometrics, September 2006

"Coalescence theory facilitates the merger of population genetics theory with phylogenetic approaches, but still, there are mostly two camps: phylogeneticists and population geneticists. Only a few people are moving freely between them. Rasmus Nielsen is certainly one of these researchers, and his work so far has merged many population genetic and phylogenetic aspects of biological research under the umbrella of molecular evolution. Although Nielsen did not contribute a chapter to his book, his work permeates all its chapters. This book gives an overview of his interests and current achievements in molecular evolution. In short, this book should be on your bookshelf." Peter Beerli for Evolution, 60(2), 2006

Customer Reviews:

Excelent Reference Book on Molecular Evolution.......2005-10-14

For those biologist and mathematicians willing to expand their knowledge in Molecular Evolution, this book is the right source. Chapters were written by leading researchers in their fields and are divided in sections from basic concepts to more advanced methods of molecular analysis. Language is clear, topics well organized. I recommend this book to any grad student, post doc or researcher interested in clear, informative reviews in several areas of Molecular Evolution.

Average customer rating:

Using the free software on internet sites to help your research
Good
Yes, It's Really Written at the For Dummies Level.
Great introductory text
great beginner

Bioinformatics for Dummies
Jean-Michel Ph. D. Claverie , Cedric Ph.D. Notredame , Jean-Michel Claverie , and Cedric Notredame
Manufacturer: For Dummies
ProductGroup: Book
Binding: Paperback

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Similar Items:

Genetics For Dummies (For Dummies (Math & Science))
Developing Bioinformatics Computer Skills
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Discovering Genomics, Proteomics and Bioinformatics (2nd Edition) (The Genetics Place Series)

ASIN: 0764516965

Book Description

Bioinformatics – the process of searching biological databases, comparing sequences, examining protein structures, and researching biological questions with a computer – is one of the marvels of modern technology that can save you months of lab work. And the most amazing part is that, if you know how, you can use highly sophisticated programs over the Internet without paying a dime and sometimes, without installing anything new on your own computer. All you need to know is how to use these technological miracles.

That's where Bioinformatics For Dummies comes in. If you want to know what bioinformatics is all about and how to use it without wading through pages of computer gibberish or taking a course full of theory, this book has the answers in plain English. You'll find out how to

Use Internet resources
Understand bioinformatics jargon
Research biological databases
Locate the sequences you need
Perform specific tasks, step by step

Written by two experts who helped develop the science, Bioinformatics For Dummies is all about getting things done. If you're just getting your feet wet, start at the beginning with a quick review of those necessary parts of microbiology and an overview of the tools available. If you already know what you want to do, you can go directly to a chapter that shows you how. Get the lowdown on

Researching and analyzing DNA and protein sequences
Gathering information from all published sources
Searching databases for similar sequences and acquiring information about gene functions through sequence comparisons
Producing and editing multiple sequence comparisons for presentation
Predicting protein structures and RNA structures
Doing phylogenetic analysis

With an Internet connection and Bioinformatics For Dummies, you'll discover how to peruse databases that contain virtually everything known about human biology. It's like having access to the world's largest lab, right from your desk. This book is your lab assistant – one that never takes a day off, never argues when you ask it for help, and won't demand a benefits package.

Customer Reviews:

Using the free software on internet sites to help your research.......2007-03-14

The first chapter is a short review of DNA and RNA sequences, amino acids, and protein. The other chapters teach you to use the free software found on the Internet to work with your research. Information is also given which helps explain some biochemicals. My skills are in Software Development using C++ language, and I need more information on biochemicals to understand the problems and to develop algorithms to solve them.
My only criticism is that I would like the book to give more biochemical theory before taking up the subject of Internet software.
Overall, this is a good beginner's book on biochemistry.

Good.......2007-02-16

I am a couple years into a PhD in bioinformatics, but this is the book I started with. I knew some biology and some computer science, but I still found a lot of the databases, etc. confusing and the field has a decided lack of simplified documentation (though it is getting better).

Of course, bioinformatics is a pretty broad topic and no book could possibly cover everything.

If you do not know any biology at all you probably should also get a basic text on genetics/molecular biology (or read thema at the NCBI web site books section for free). You don't need anything in depth to read the dummies book, just at the level of an introductory biology book. Hint: DNA to RNA, RNA to Protein. And you want to know why proteins are similar because proteins with similar amino acid sequences often have similar chemical properties and therefore similar functions, so if you know what one protein does you can guess what a protein like it probably does.
:-)

And despite the name of the book the authors are REAL bioinformaticists (T-Coffee rocks!)

Yes, It's Really Written at the For Dummies Level........2007-01-18

This book kind of blew me away. Bioinformatics is such a big word.

Then in the second chapter they tell you 'How Most People Use Bioinformatics.' And all of a sudden they have you on line to the National Library of Medicine at the National Institute of Health. They have you looking at protein sequences, and you even understand what they are saying.

This is a 'For Dummies' book. It is written in their traditional style, assuming that you know very little -- well to be sure they say they are making the assumption that 'You likely have a background in molecular biology. If you don't - or if you need to brush up on your molecular biology - Chapter 1 gives you a brief overview of the basics.'

I found that the first few chapters went down pretty easily. By part IV it had gone further than I wanted to go, and I quit reading.

BUT if I were going to be taking a course in bioinformatics, or even thinking about taking such a course, or just looking at a degree in biology, I'd spend a week or two getting around this book. It's written a hell of a lot better than any text you're likely to get assigned, and at its price it's quite a deal.

Great introductory text.......2006-09-20

This is an excellent introduction to Bioinformatics. It does assume some very basic knowledge of biology- perhaps a couple of paragraphs could help the total newbies.

Have you noticed that reviews of bioinformatics books tend to follow certain patterns? The Computer Scientists and mathematicians complain that there is not enough detail about the algorithms, the biologists complain that they could not get through the math, and everyone complains that there is not enough detail about their favorite programs! Let us face the fact that this is a very broad field, and most people that want to learn about it will never design algorithms.

great beginner.......2005-12-16

In spite of the title (I don't know many dummies interested in multiple sequence alignments) this reference is written by experts in the field of bioinformatics and is very accessable for the beginner. I purchased this book as a beginning graduate student so that I could learn which programs to use to compare amino acid and nucleic acid sequences as well as prepare them for publication and this book was perfect for this end. This text doesn't delve into the algorithms or much theory (which is learned through practice and other sources), nevertheless, I recommend this for the researcher for a crash course or quick reference. This book really helped me get my feet wet in this area (and recently publish a nice alignment) and will certainly reduce my workload next semester for my bioinformatics course!

Statistical Methods in Genetic Epidemiology

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Statistical Methods in Genetic Epidemiology
Duncan C. Thomas
Manufacturer: Oxford University Press, USA
ProductGroup: Book
Binding: Hardcover

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Similar Items:

A Statistical Approach to Genetic Epidemiology: Concepts and Applications
Human Genome Epidemiology: A Scientific Foundation for Using Genetic Information to Improve Health and Prevent Disease (Monographs in Epidemiology and Biostatistics)
Fundamentals of Genetic Epidemiology
Statistics in Human Genetics
Genetics and Analysis of Quantitative Traits

ASIN: 019515939X

Book Description

This well-organized and clearly written text has a unique focus on methods of identifying the joint effects of genes and environment on disease patterns. It follows the natural sequence of research, taking readers through the study designs and statistical analysis techniques for determining whether a trait runs in families, testing hypotheses about whether a familial tendency is due to genetic or environmental factors or both, estimating the parameters of a genetic model, localizing and ultimately isolating the responsible genes, and finally characterizing their effects in the population. Examples from the literature on the genetic epidemiology of breast and colorectal cancer, among other diseases, illustrate this process. Although the book is oriented primarily towards graduate students in epidemiology, biostatistics and human genetics, it will also serve as a comprehensive reference work for researchers. Introductory chapters on molecular biology, Mendelian genetics, epidemiology, statistics, and population genetics will help make the book accessible to those coming from one of these fields without a background in the others. It strikes a good balance between epidemiologic study designs and statistical methods of data analysis.

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