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| By: Anna Tramontano ISBN: 1584884916 Publisher: Chapman & Hall/CRC Release Date: 24 May, 2005 Bioscience book rank: 880784 | The title of this book is misleading; at least, it misled me. Before opening it I thought it would deal with ten unsolved problems in protein bioinformatics that we should like to be able to solve but at present cannot. In other words, I expected that it would be a book for researchers that would challenge them to find solutions to major problems where none are currently available. I was, however, surprised that there should be as many as ten of these. In fact, this is not really a book for researchers, but one for students and others new to protein bioinformatics: these are the things we will want to know when we approach a protein with a bioinformatic approach, these are the sort of methods currently in use, this is the sort of information we can get from them, and these are the respects in which we may hope they will be improved in the future. In short, we are not dealing with questions that at present have no answers, but with ones that we may hope to be able to answer better. In this respect, however, protein bioinformatics is just like any other discipline: few, if any of the methods we use in science are so good that we cannot conceive of anything better.
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<br />As an example, Problem 1 concerns protein sequence alignment: the account begins with a discussion of protein evolution, leading to the distinction between orthology and paralogy and the ideas of protein families, similarity matrices and gap penalties. The chapter then proceeds to a description, at times quite advanced, of methods in current use for comparing and aligning sequences, including multiple alignments. Only in the last of nearly thirty pages discussing this problem does the author turn her attention to the ways in which the methods in use might be improved, but she provides almost no detail.
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<br />The other chapters deal with secondary-structure prediction from sequence information, prediction of biological function, tertiary-structure prediction, and so on, ending with more engineering topics such as the design of artificial proteins and the modification of existing proteins to fulfil novel functions. In all of these the presentation is competent, and the book will be very useful to anyone wanting to learn about protein bioinformatics, in particular about the state of the art today. On the other hand, with none of the problems are we dealing with a "most wanted solution" in the sense of seeking a way ahead when the road appears at present to be completely blocked. Nowhere does the author throw down her gauntlet before her colleagues, saying this is where you have failed, and must provide a solution to this vital problem. This book delivers reasonably well on the promise in its title: it does a good job in stating the most computational interesting problems relating to proteins. It assumes the reader knows a little about biochemistry, biology, and computational techniques, but only a little about each. Given that base, it does a fair job in describing problems related to protein structure, function, analysis, and design. It's not an advanced text, in either its computational or biological sides, but not an elementary introduction, either. Someone a bit above novice level will probably get the most out of it. <br /> <br />A few things left me a bit leery about this text, though. Despite its 2005 copyright date, the author (p.53) cites an estimate of human 50,000 genes. I'm not sure where (or when) that number comes from, because most estimates today are closer to 30,000. There was another a minor annoyance in the discussion of convolution as a tool in protein docking. The failure to distinguish convolution from correlation is minor and forgivable. Saying that one "convolutes" a convolution is like say that one "revolutes" a revolution. Revolve: revolution, convolve: convolution. Also, the Fourier transform step in correlation, especially when docking a small molecule to a protein, is an optimization rather than a requirement. Transform-based correlation gives better performance for asymptotically large models. In some computing environments, for models of realistic sizes, the simplicity of direct correlation gives a performance advantage - and allows non-linear scoring algorithms that would be impossible with the transform approach. <br /> <br />This is a fair introduction to many of the ways people study proteins computationally, and to the kinds of tools required. There is very little computaitonal detail, however. It may help a tool-builder create a conceptual base for studying proteins, but won't help much with the specific calculations. <br /> <br />//wiredweird This is a very useful overview of the very broad subject of bioinformatics, and it provides a good background on a variety of approaches to topics like protein conformation prediction. The translation is excellent - the subject-matter is clear and there are no obvious errors, which is unusual for such a technical subject. The main drawback of the book is that, because this is a field in which progress is being made rapidly, the book is already out of date in places. For example, in the chapter dealing with protein structure prediction, there is scant mention of the most successful approach to date, namely the Rosetta project initiated at the University of Washington in Seattle. <br /> <br />Nevertheless, this is a very useful primer for people coming into the area of bioinformatics and it covers topics that will not age as rapidly, such as certain statistical models. Indeed, the author's exposition of how Hidden Markov Models work is as clear as anything I've read anywhere. | |
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| By: Gil Alterovitz, Marco F. Ramoni ISBN: 1596931248 Publisher: Artech House Publishers Release Date: 28 February, 2007 Bioscience book rank: 857295 | ||
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| By: Ann Finney Batiza Ph.D. ISBN: 0791085171 Publisher: Chelsea House Publications Release Date: September, 2005 Bioscience book rank: 986327 | If you don't know anything about Bioinformaticas, this book is very adeccuacy for your first time | |
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| By: Ingvar Eidhammer, Inge Jonassen, William R. Taylor ISBN: 0470848391 Publisher: Wiley Release Date: 01 March, 2004 Bioscience book rank: 850895 | The book 'Protein Bioinformatics' tries to cover all aspects of proteins, from sequence to structure. This is of course a very wide field and the difficulty of the algorithms involved in this analysis increases from sequence to structure investigations. From the preface of the book one can read, that this is still not enough for the authors because additionally they are trying to write this book for a broad audience, for researchers and students.
<br />After reading this book I think it could be used by undergraduate students in Bioinformatics or related fields or as reference. It does not give deep and clear explanations but rather provides short summaries of articles. The good thing is after reading this book you know of the existence of these articles and can consult them to understand the working mechanism of the algorithms in detail.
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<br />There is certainly a lack in good books about proteins and especially about protein structure analysis which can partly filled by this book. This book gives good, basic coverage of the concepts important in understanding protein sequence and structure. <br /> <br />There are three major sections in this book: sequence, structure, and the relatinship between the two. The sequence section covers all the basics: dynamic programming for string matching, scoring matrices, trees and classification, and profiles of various sorts. The sequence discussion is a bit shorter, but goes over substructures, similarity searching and scoring, and kinds of structures and domains. The third section is even shorter and unites the two areas: predicting structure from sequence, with a good introduction to threading. <br /> <br />The book's strength is its breadth. It sacrifices depth to get that breadth, though. A few analytic techniques are sketched in the text or presented in psuedocode. Most often, however, a programmer will have a hard time gleaning enough detail from this to implement any of the algorithms described. <br /> <br />The authors aim at readers who already understand the significance of protein structure and who are comfortable with ideas like hydrogen bonding. Lots of programmers will have a hard time understanding why problems are important or what the driving phenomena are. Biologists won't be put off by an excessively mathematical treatment, but won't get a detailed understanding of the algorithms or mathematical foundations either. This book comes close to under-serving both kinds of reader. <br /> <br />This book is good for conceptual understanding of the algorithms, where implementable details don't matter, and gives good coverage to protein-specific issues. It's decidedly for someone who wants more than just the how-to of running BLAST or strucuture analysis tools. I think this book will help most if you want more understanding of what goes on inside the tools, or if you want an easy start to a deep and complex topic. Advanced readers may not like it, though - detail and real understanding just aren't there. I give this one four stars, but I had to round up to four. <br /> <br />//wiredwerid | |
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| By: Glyn Moody ISBN: 0471327883 Publisher: Wiley Release Date: 03 February, 2004 Bioscience book rank: 817841 | As the other reviewer notes, this book does what it's meant to do: give you an overview of the myriad developments in bioinformatics since its inception. It's fairly engaging, though, for me, that's mostly due to the subject matter itself and not the writer's abilities. This is why I gave it four stars instead of five.
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<br />The scientific explanations are usually not that great, even if the concepts aren't that difficult to understand given some understanding of the underlying biological concepts. I had to quit reading and go in Wikipedia to understand some of these concepts because I felt the author's explanations were just unnecessarily confusing. And the author often decides to jump from the narrative and devote a page to the science, which isn't a horrible thing to do, but I feel maybe the science could have either been explained more succinctly or integrated with the narrative better. So that, along with the worst proofreading I've encountered in a published book (multiple instances of missing words [like 'a' or 'the'], missing punctuation [periods, parentheses], inconsistent punctuation, etc.), prompts me to give a four-star rating.
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<br />That said, it's certainly worth reading; and, from what I've surmised, it's the only published book on the history of bioinformatics (that isn't solely concerned with the Human Genome Project), so it's not like there are (m)any alternatives.
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<br />I'll also note that even though this was published a few years ago, it feels slightly outdated already due to the perpetual advances in throughput and methodology in the field. A new edition (at least with an afterword explaining recent advances) would be nice. It's been a while since I read this book. So I try to get back on my impressions. <br />The book has a good sketch on the key developments of modern genomics and bioinformatics, full of gossips and vivid stories. It is a very difficult job to write a history or something close to that for a fast evolving field. And there are limited accounts on the business side too. To her credit, the author has done an excellent job. <br />I had concerns over the accuracy and coverage of some contents and opinions. But given the breadth of the book, probably this is how good it can get. Other than that, it was a very interesting reading. I recommended it to a friend right the way. | |
| By: Martin Gollery ISBN: 1584886846 Publisher: Chapman & Hall/CRC Release Date: 03 June, 2008 Bioscience book rank: 928335 | ||
| By: Asad Umar, Izet Kapetanovic, Javed Khan, APPLICATIONS OF BIOINFORMATICS IN CANCER ISBN: 1573315109 Publisher: New York Academy of Sciences Release Date: June, 2004 Bioscience book rank: 937224 | ||
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| By: Bryan Bergeron ISBN: 0131008250 Publisher: Prentice Hall PTR Release Date: 29 November, 2002 Bioscience book rank: 763051 | While the book does an adequate job of explaining the purpose to bioinformatics, it wasn't very technical. I had it as a text for a graduate course, and many of us whose background was in computing found a need to find outside references. It's not a bad book for some high level coverage, but it never seems to get to the meat of a subject in much depth or detail. It is more for someone interested in existing tools and databases, but not for a developer who wants to get started in this field. If you're in that category you may want to look at some other text books such as "Bioinformatics in the Post Genomic Era" by Augen or "Fundamental Concepts of Bioinformatics" by Krane and Raymer. Another potential source is Lesk's "Introduction to Bioinformatics" (a bit older, but it does talk about specific computational skills). Bergeron wrote this book such that if you have a computer background, you can relate to the topic at hand, and if you have a biology background, you can pick up the material quickly. He uses one to teach the other, and does so rather comprehensively. Major topics and areas of interests in bioinformatics are covered, such as: <br />* Databases <br />* Networks and the Internet <br />* Bioinformatics search engines <br />* Data mining techniques <br />* Statistics <br />* Pattern Matching <br />* Simulation techniques and modeling <br /> <br />Any of these topics deserve a volume of books dedicated to them, but the author gives the readers enough information that can be useful in determining where to go next. Even though the topics are mostly computing related, the author takes a great care at talking about these topics in the context of Bioinformatics. He even lists the specific applications of each topic at the beginning of each chapter to aid the reader in relating to the topic at hand. For example, after reading the chapter on modeling and simulation, you would know that modeling is used to determine the efficacy of drugs and to determine drug side effects during the drug discovery process. <br /> <br />Databases are probably one of the most important and well known tools in Bioinformatics. The enormous amount of data available for analysis requires large and fast databases. In fact, the amount of data in bioinformatics doubles every eighteen months, so databases and database design is an integral part of bioinformatics computing. In addition to the vast amount of raw data (sequence data and protein data for example) that is stores in databases, the analysis such as pattern matching, simulation and visualization of data requires constant access to databases. The author talks about what are know as primary databases, databases that are used to store raw data, and other value added databases, the one's that store analyzed and/or verified data. One thing that reader gets out databases is the realization of what the data life cycle is in the bioinformatics world, and how it affects all the application areas of bioinformatics. <br /> <br />The databases around the world are either somehow integrated together ease the task of data discovery and data mining. Due to the vast amount of information available, various data mining techniques have been developed over the years to assist in finding the data that a researcher is looking for. Tasks such as data enrichment, missing value analysis for sequence data, data characterization and data distribution analysis mark some of the tasks that data mining techniques needs to accomplish. A number of data mining techniques such as hidden Markov Models, Decision Trees, Neural Networks and Genetic Algorithms are talked about and the pro's and con's of each one is discusses in detail. A bioinformatician needs to be at least aware of the various data mining techniques and should have an overview how they work and why they work in general. <br /> <br />After the data has been discovered, a method of visualization that can get the point across, per se, needs to be used. Visualization and simulation techniques are talked about to show the reader what a bioinformatician needs to do with the information found. There are a number of graphical tools available out there, some free and some not, that are used heavily in this business to aid the understanding of the vast amount of information that is available. Various modeling techniques are being used today to aid with the drug discovery process and figuring out the side effect of newly developed drugs. I would say that this area of bioinformatics will see the most growth in the coming years, and the author, Bryan Bergeron, does a great job discussing this topic. <br /> <br />Statistics is another technique used heavily in bioinformatics computing. Even though most of the statistical tools, Matlab and many others, have been used for a number of years, one must know the theory and reason behind using numerous statistical techniques in Bioinformatics. These techniques are integrated into bioinformatics search engines and the software applications for modeling and simulations, but one still needs to know how they work. Bioinformatics is a new field of study, and not by any means been perfected, so there are still a number research track and advancements that are still untapped, thus making the theory behind how some of the available tools work very important in this field. <br /> <br />Bryn Bergeron in Bioinformatics Computing gives the necessary background for anyone interested in the field of bioinformatics. After reading this book, a reader can get a good idea of which area s/he wants to pursue further. The topics are broken into logical units that can aid the reader in realizing what specific field of bioinformatics is more interesting than others. <br /> <br />Even if you don't decide to pickup one of many advanced books in this topic, you should know about an industry that is growing rapidly, and Bergeron's book can aid you to do just that. <br /> <br /> Ok, I'm not done yet with the book but after two chapters, I could already share with people something: this book is a solid introduction to the field of biocomputing. It cover many aspects in 10 differents chapters (database, data mining, collaboration, read the table of contents).<p>The autor is enthousiast about his field of research but he doesn't miss an important thing: criticism!! At the end of chapter, you have a small dose of concerns he have about biocomputing. Where we could make mystakes, what we should do?<p>I'm about to choose if I want to do my master degree in this discipline and this book is great to introduce me with a large perspective to this branch of science.<p>If you working in this industry, this book might be a little bit boring but even for me who work 5 years with computers networks and databases, both chapters about those technologies learned me something interesting so... I'm quite happy about my decision to acquire this book. | |
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| By: Jason T. L. Wang, Mohammed J. Zaki, Hannu T.T. Toivonen, Dennis E. Shasha ISBN: 1852336714 Publisher: Springer Release Date: 17 September, 2004 Bioscience book rank: 917432 | ||
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| By: Shiyi Shen, Jack A. Tuszynski ISBN: 3540748903 Publisher: Springer Release Date: 01 March, 2008 Bioscience book rank: 6377489 | ||
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bioinformatics, computational biology, sequence analysis, protein structure, data mining, biostatistics
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