Product Description
An introduction to biological networks and methods for their analysis

Analysis of Biological Networks is the first book of its kind to provide readers with a comprehensive introduction to the structural analysis of biological networks at the interface of biology and computer science. The book begins with a brief overview of biological networks and graph theory/graph algorithms and goes on to explore: global network properties, network centralities, network motifs, network clustering, Petri nets, signal transduction and gene regulation networks, protein interaction networks, metabolic networks, phylogenetic networks, ecological networks, and correlation networks.

Analysis of Biological Networks is a self-contained introduction to this important research topic, assumes no expert knowledge in computer science or biology, and is accessible to professionals and students alike. Each chapter concludes with a summary of main points and with exercises for readers to test their understanding of the material presented. Additionally, an FTP site with links to author-provided data for the book is available for deeper study.

This book is suitable as a resource for researchers in computer science, biology, bioinformatics, advanced biochemistry, and the life sciences, and also serves as an ideal reference text for graduate-level courses in bioinformatics and biological research. ... Read more

Product Description
This first introductory book designed to train novice programmers is based on a student course taught by the author, and has been optimized for biology students without previous experience in programming.
By interspersing theory chapters with numerous small and large programming exercises, the author quickly shows readers how to do their own programming, and throughout uses anecdotes and real-life examples from the biosciences to 'spice up' the text.
This practical book thus teaches essential programming skills for life scientists who want -- or need -- to write their own bioinformatics software tools. ... Read more

Customer Reviews (5)

incomplete book,inpracticle for programming, lack of real stuffs
This book is a mess. It seems to be an unfinished book with many chapters absent. The organization of this book is poor. It has too much verbose and useless opinions without hitting the points. In a word, this book doesn't have many real contents. It's not worthy to buy this book.

Better book than reviews suggest
OK. So, this book is not a typical how-to guide or a standard reference book, in spite of the title. And this is kind of refreshing. In fact, the book does a good job of presenting useful information in an intellectually engaging and entertaining way.

The author points out in the introduction that his main topic is neither Python nor Bioinformatics. His central purpose seems to be to offer a useful conceptual framework -- with skill-building exercises -- for students (and scientists without a lot of programming experience) to use when they approach a problem whose solution requires a tool that isn't ready-made but might be custom made (or at least designed) by one with a rather basic programming skill set.

You can't take the sub-title literally or you'll end up disappointed with the book--as other reviewers seem to have been. In my mind, the practical application of the book's content is teaching solution-oriented thinking, with bioinformatics and Python as a case-study.

The book is not going to fit the needs of a person who would prefer the "For Dummies" format for speed of learning or a more technical, detail-driven book that leads to a specific outcome. It is probably most useful for one who, at the present moment, needs mainly to grasp the scope of what might be possible using a custom built solution.

This book is not for beginners
This book is not for beginners.
It reads more like an overview of programming languages in the context of bioinformatics.
It is also a bit biased with respect to preferences in languages.
Do not buy this book if you are trying to learn python unless you are an excellent programmer with broad knowledge of multiple programming languages.

Don't waste time or money on this book
If you are looking for a practical guide to learning python and bioinformatics this book is definitely not for you.It is filled with obscure latin phrasings and contains little to no useful information.It is of absolutely no practical value for anyone trying to gain an understanding of python.Please pass on this book.

: pass # No value here
On page 6, I immediately became suspicious of this book when the author states "Speaking for myself, it took me about two hours to learn PYTHON...."

That may well be true, but after reading the bulk of the book I started to wonder if this meant the author spent 2 hours getting a "Hello World" program to work and this is what "to learn PYTHON" referred to.

The book itself comes across as an attempt to take thematic elements of "Zen and the Art of Archery", "Write Great Code, Volume 1: Understanding the Machine", "Programming Language Pragmatics", and scatter about some quotes in Latin, philosophical snippets from Eastern and Western tradition, label chapters by pretentious names such "Chapter 3: Propedeutics", introduce a mascot for the book (a Cobra named Anna the hannah, which sits atop "catchy" and colloquial captions such as "Ready dude - beat me , break me!", and "Error in operator: add beer...") add two catchy words (Bioinformatics and Python) and purport to convey some useful knowledge in an overpriced volume that fails in just about every aspect except to show, what I see as, thinly disguised hubris.

If I were to approach Bioinformatics and Python, with the assumption that you are comfortable working in the life sciences, I would recommend "Python Scripting for Computational Science" and "Programming in Python 3: A Complete Introduction to the Python Language", and getting very familiar with NumPy.

Python is a great language, but depending on your application needs (i.e. for Bioinformatics don't hesitate to explore other less popular (at least in name recognition) but suitable languages such as Haskell and Lua or any other language that you find meets your needs. ... Read more

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

Product Description
The goal of this book is to help readers understand state-of-the-art techniques in biological data mining & data management & includes topics such as: * preprocessing tasks such as data cleaning & data integration as applied to biological data * classification & clustering techniques for microarrays * comparison of RNA structures based on string properties & energetics * discovery of the sequence characteristics of different parts of the genome * mining of haplotypes to find disease markers * sequencing of events leading to the folding of a protein * inference of the subcellular location of protein activity * classification of chemical compounds based on structure * special purpose metrics & index structures for phylogenetic applications * a new query language for protein searching based on the shape of proteins * very fast indexing schemes for sequences & pathways Aimed at computer scientists, necessary biology is explained. ... Read more

Product Description

One of the most pressing tasks in biotechnology today is to unlock the function of each of the thousands of new genes identified every day. Scientists do this by analyzing and interpreting proteins, which are considered the task force of a gene. This single source reference covers all aspects of proteins, explaining fundamentals, synthesizing the latest literature, and demonstrating the most important bioinformatics tools available today for protein analysis, interpretation and prediction. Students and researchers of biotechnology, bioinformatics, proteomics, protein engineering, biophysics, computational biology, molecular modeling, and drug design will find this a ready reference for staying current and productive in this fast evolving interdisciplinary field.

Product Description
Mathematics of Bioinformatics: Theory, Methods, and Applications provides a comprehensive format for connecting and integrating information derived from mathematical methods and applying it to the understanding of biological sequences, structures, and networks. Each chapter is divided into a number of sections based on the bioinformatics topics and related mathematical theory and methods. Each topic of the section is comprised of the following three parts: an introduction to the biological problems in bioinformatics; a presentation of relevant topics of mathematical theory and methods to the bioinformatics problems introduced in the first part; an integrative overview that draws the connections and interfaces between bioinformatics problems/issues and mathematical theory/methods/applications. ... Read more

Product Description
This book is written by an experienced author team representing the many areas from which the new discipline of Bioinformatics is emerging. Their common sense approach and carefully detailed presentations in Bioinformatics: A Computing Perspective blends computing and biological sciences in an engaging and unique way.

Customer Reviews (2)

Decent introduction, but needs work
Overall, this book serves as a decent springboard into further exploration of topics in bioinformatics. It gives a broad sampling of the terms and basic techniques in the field, making the reader conversant enough to research the details elsewhere. This level of involvement is probably the best approach for an emerging field like bioinformatics, where the rules of the game are hardly written down before they change again.

On the other hand, I had several issues with this book. For one, it seemed like a rough draft in many places. In addition to some copyediting issues and stylistic inconsistencies, a few of the illustrations were poorly constructed and unclear.

The book aims (according to the back cover) to be a "standalone text" focusing on the computing side of bioinformatics, but with relevant background in biology and mathematics. Though it handled computing, math, and statistics fairly well, I felt that the biology side of the material was lacking, and sometimes even off the mark. The authors have a tendency to make analogies to computing whenever they try to explain a biological process. This is not necessarily a bad idea, but they often spend too much time on the analogies rather than the actual explanation, confusing the issue by bogging down the reader in the parts that did not correspond. My recommendation: more (and better) illustrations, and fewer analogies.

Even the computing part had some problems. The exercises were all over the place. Some were trivial and had tenuous relevance ("decode this Morse code message using a table"), while others were large programming projects with latent, difficult graph theory problems. Meanwhile, a few code listings demonstrated horrible programming practices (a state machine implemented with recursive function calls, anyone?). Moreover, they were all written in Java, which is fine in itself--choose a language and stick with it--but the text sometimes referred to Java idiosyncrasies that made discussions inapplicable to other languages.

I do not want to make this book sound worthless. It had many good qualities, and it was certainly valuable as a starting point. Still, I think the authors and the publisher need to consider some major revisions to many of its sections. I hope that there will be further editions that address some of these issues: the book has lots of potential that is mired by its rough edges.

nice delivery!
MY first review. For this newly published book, there is not much to say. If you are familiar with the logrithms and biochemistry, it's a good text book. What I want to say is that this is a very nice shopper, unbilievable fast delivery, and cheapest price for this book in nice condition. ... Read more

Product Description

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

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

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

Product Description

For decades, bioinformatics textbooks have primarily served gene-hunters and biologists constructing family trees showing tidy lines of descent. Written to make the ‘new’ information-based bioinformatics intelligible to both the ‘bio’ and the ‘info’ audiences, this book identifies the types of information that genomes transmit, shows how competition between different types is resolved in the genomes of different organisms, and identifies the evolutionary forces involved. Early chapters relate the form of information with which we are most familiar, namely written texts, to the DNA text that is our genome. Providing a pathway for introducing historical aspects dating back to the nineteenth century.

Product Description

Proteins lie at the heart of almost all biological processes and have an incredibly wide range of activities. Central to the function of all proteins is their ability to adopt, stably or sometimes transiently, structures that allow for interaction with other molecules. An understanding of the structure of a protein can therefore lead us to a much improved picture of its molecular function. This realisation has been a prime motivation of recent Structural Genomics projects, involving large-scale experimental determination of protein structures, often those of proteins about which little is known of function. These initiatives have, in turn, stimulated the massive development of novel methods for prediction of protein function from structure. Since model structures may also take advantage of new function prediction algorithms, the first part of the book deals with the various ways in which protein structures may be predicted or inferred, including specific treatment of membrane and intrinsically disordered proteins. A detailed consideration of current structure-based function prediction methodologies forms the second part of this book, which concludes with two chapters, focusing specifically on case studies, designed to illustrate the real-world application of these methods. With bang up-to-date texts from world experts, and abundant links to publicly available resources, this book will be invaluable to anyone who studies proteins and the endlessly fascinating relationship between their structure and function.

Product Description
The purpose of this book is to give a thorough and systematicintroduction to probabilistic modeling in bioinformatics. The bookcontains a mathematically strict and extensive presentation of thekind of probabilistic models that have turned out to be useful ingenome analysis. Questions of parametric inference, selection betweenmodel families, and various architectures are treated. Severalexamples are given of known architectures (e.g., profile HMM) used ingenome analysis. Audience: This book will be of interest to advancedundergraduate and graduate students with a fairly limited backgroundin probability theory, but otherwise well trained in mathematics andalready familiar with at least some of the techniques of algorithmicsequence analysis. ... Read more

Customer Reviews (4)

An excellent, rigorous exploration of HMMs as used in bioinformatics
"Hidden Markov Models of Bioinformatics" is an excellent exploration of the subject matter: appropriate coverage, well written, and engaging. Hidden Markov Models are a rather broad class of probabilistic models useful for sequential processes. Their use in the modeling and abstraction of motifs in, for example, gene and protein families is a specialization that bears a thorough description, and this book does so very well. This is a book for understanding the theory and core ideas underlying profile HMMs, and if the term Expectation Maximization doesn't sound familiar or interesting to you, this is probably not the book you're looking for. Personally I found it clearer in some ways than the standard reference by Durbin, Eddy, Krogh, and Mitchison, but actually the two complement each other very nicely. If you are interested in constructing an HMM for your favorite protein family you probably want to look at the HMMER or SAM documentation instead; if you want to understand where HMMs come from or how you might architect one, there's probably no better book than this one.

Written by a mathematician for mathematicians
The intended audience of this book are mathematicians. To understand this book, you should have prior coursework experience in at least several upper division undergraduate courses in mathematical statistics and probability theory. The structure of this book is also that of a typical math book; full of proposition, corollary, lemma, etc, and very limited use of illustrations (e.g., there is no single figure up to chapter 6).

I wanted a book with a mathematical sophistication simliar to Durbin's book, but this book is way more than that. On the other hand, I showed this book to a mathematics graduate student and she said this book is perfect for her. So I guess this book is written by a mathematician only for mathematicians.

Good material, but you really have to want it.
The book gives outstanding coverage of all that goes into building HMMs - one of the most important tools in genome analysis and structure prediction. It covers the field in extreme depth. More depth, in fact, than needed for building useful HMM systems. It not only presents the forward and backward algorithms leading up to Baum-Welch, it presents all the extras - convergence, etc.

This additional depth of coverage may go beyond many readers' needs. It is very helpful, though, for people who need more than the usual algorithms. By giving the background in such detail, a persistent reader can follow to a certain point, then create modifications with a clear idea of where the new algorithm actually comes from.

Regarding the current practice of HMM usage, I found it a bit thin. Widely-known tools based on HMMs are mentioned only occasionally and in passing, and HMM-based alignment is discussed only briefly. Well, this book isn't for the tool user. Perhaps more important, I found scant mention of scoring with respect to some background probability model ("null" model, as it's called here).

My one real complaint, and this is truly minor, is the quality of illustration. The line-drawings look like Word pictures - not necessarily a bad thing, if done well. These aren't particularly professional-looking, though, and oddly stretched or squashed in many cases. Still, they're readable enough and make all the needed points.

A lesser point, and not the author's fault, is the editorial implication that this book introduces probabilitic models in general. It does not. This is strictly about HMMs, not Bayesian nets, bootstrap techniques, or any of the dozens of other probabilistic models used in bioinformatics. That is not a flaw of the book, just a flaw in how it's represented.

If you are dedicated to becoming an expert in HMM construction and application, you must have this book. It's a bit much, though, for people who just want the results that HMMs give.

Primarily for bio-mathematicians
The field of computational biology has expanded greatly in the last decade, mainly due to the increasing role of bioinformatics in the genome sequencing projects. This book outlines a particular set of algorithms called hidden Markov models, that are used frequently in genetic sequence search routines. The book is primarily for mathematicians who want to move into bioinformatics, but it could be read by a biologist who has a strong mathematical background. The book is detailed at some places, sparse in others, and reads like a literature survey at times, but many references are given, and there are very interesting exercises at the end of each chapter section. In fact it is really imperative that the reader work some of these exercises, as the author proves some of the results in the main body of the text via the exercises.

Some of the highlights of the book include: 1. An overview of the probability theory to be used in the book. The material is fairly standard, including a review of continuous and discrete random variables, from the measure-theoretic point of view, i.e the author introduces them via a probability space which is set with its sigma field, and a probability measure on this field. The weight matrix or "profile" as it is sometimes called, is defined, this having many applications in bioinformatics. Bayesian learning is also discussed, and the author introduces what he calls the "missing information principle", and is fundamental to the probabilistic modeling of biological sequences. Applications of probability theory to DNA analysis are discussed, includingshotgun assembly and the distribution of fragment lengths from restriction digests. A collection of interesting exercises is included at the end of the chapter, particularly the one on the null model for pairwise alignments. 2. An introduction to information theory and the relative entropy or "Kullback distance", the latter of which is used to learn sequence models from data. The author defines the mutual information between two probability distributions and the entropy, and calculates the latter for random DNA. He also proves some of the Shannon source coding theorems, one being the convergence to the entropy for independent, identically distributed random variables. The Kullback distance is then defined, as a distance between probability distributions, with the caution that it is not a metric because of lack of symmetry. 3. The overview of probabilistic learning theory, where 'learning from data' is defined as the process of inferring a general principle from observations of instances. 4.The very detailed treatment of the EM algorithm, including the discussion of a model for fragments with motifs. 5. The discussion of alignment and scoring, especially that of global similarity. Local alignment is treated in the exercises. 6. The discussion of the learning of Markov chains via Bayesian modeling applied to a training sequence via a family of Markov models. Frame dependent Markov chains are discussed in the context of Markovian models for DNA sequences. 7. The discussion of influence diagrams and nonstandard hidden Markov models, in particular the excellent diagrams drawn to illustrate the main properties, and excellent discussion is given of an "HMM with duration" in the context of the functional units of a eukaryotic gene. This is important in the GeneMark:hmm software available. 8. The treatment of motif-based HMM, in particular the discussion of the approximate common substring problem. 9. The discussion of the "quasi-stationary" property of some chains and the connection with the "Yaglom limit". 10. The treatment of Derin's formula for the smoothing posterior probability of a standard HMM. The author shows in detail that the probability of a finite length emitted sequence conditioned on a state sequence of the HMM depends only on a subsequence of the state sequence. 11. The treatment of the lumping of Markov chains, i.e. the question as to whether a function of a Markov chain is another Markov chain. 12. The very detailed treatment of the Forward-Backward algorithm and the Viterbi algorithm. 13. The discussion of the learning problem via the quasi-log likelihood function for HMM. 14. The discussion of the limit points for the Baum-Welch algorithm. Since the Baum-Welch algorithm deals with iterations of a map, its convergence can be proved by finding the fixed points of this map. These fixed points are in fact the stationary points of the likelihood function and can be related to the convergence of the algorithm via the Zangwill theory of algorithms. Unfortunately the author does not give the details of the Zangwill theory, but instead delegates it to the references (via an exercise). The Zangwill theory can be discussed in the context of nonlinear programming, with generalizations of it occurring in the field of nonlinear functional analysis. It might be interesting to investigate whether the properties of hidden Markov models, especially their rigorous statistical properties, can all be discussed in the context of nonlinear functional analysis. ... Read more

Product Description
Utilizing high speed computational methods to extrapolate to the rest of the protein universe, the knowledge accumulated on a subset of examples, protein bioinformatics seeks to accomplish what was impossible before its invention, namely the assignment of functions or functional hypotheses for all known proteins.

The Ten most Wanted Solutions in Protein Bioinformatics considers the ten most significant problems occupying those looking to identify the biological properties and functional roles of proteins.

- Problem One considers the challenge involved with detecting the existence of an evolutionary relationship between proteins.- Two and Three studies the detection of local similarities between protein sequences and analysis in order to determine functional assignment. - Four, Five, and Six look at how the knowledge of the three-dimensional structures of proteins can be experimentally determined or inferred, and then exploited to understand the role of a protein. - Seven and Eight explore how proteins interact with each other and with ligands, both physically and logically.- Nine moves us out of the realm of observation to discuss the possibility of designing completely new proteins tailored to specific tasks. - And lastly, Problem Ten considers ways to modify the functional properties of proteins.

After summarizing each problem, the author looks at and evaluates the current approaches being utilized, before going on to consider some potential approaches.

introbul>Features---------------------Features---------------------· Presents introductory material on protein structure and function, with an evolutionary perspective· Describes ten of the most cogent problems in computational biology· Considers future routes that are likely to improve our understanding of the exquisitely specific and efficient mechanisms of protein function· Includes a suggested reading list for further research at the end of each chapter· ... Read more

Customer Reviews (3)

Useful, but the title doesn't really describe it
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.

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.

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.

Depends on what you want
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.

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.

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.

//wiredweird

Comprehensive but a little dated
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.

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. ... Read more

Product Description
Recent advances in biotechnology, spurred by the Human Genome Project, have resulted in the accumulation of vast amounts of new data. Ontologies—computer-readable, precise formulations of concepts (and the relationship among them) in a given field—are a critical framework for coping with the exponential growth of valuable biological data generated by high-output technologies. This book introduces the key concepts and applications of ontologies and ontology languages in bioinformatics and will be an essential guide for bioinformaticists, computer scientists, and life science researchers.

The three parts of Ontologies for Bioinformatics ask, and answer, three pivotal questions: what ontologies are; how ontologies are used; and what ontologies could be (which focuses on how ontologies could be used for reasoning with uncertainty). The authors first introduce the notion of an ontology, from hierarchically organized ontologies to more general network organizations, and survey the best-known ontologies in biology and medicine. They show how to construct and use ontologies, classifying uses into three categories: querying, viewing, and transforming data to serve diverse purposes. Contrasting deductive, or Boolean, logic with inductive reasoning, they describe the goal of a synthesis that supports both styles of reasoning. They discuss Bayesian networks as a way of expressing uncertainty, describe data fusion, and propose that the World Wide Web can be extended to support reasoning with uncertainty. They call this inductive reasoning web the Bayesian web. ... Read more

Customer Reviews (3)

Very lucid explanations
One of the most clearly written books I have found on the topic. I agree with a previous reviewer that some areas were not covered, but the ones that were are well described.

I would love to see a follow-on that reviews major ontology works in progress and explains when and how to use them and how to effectively map from one domain or specialty to another.

Excellent book of how we apply Ontology into real life application.
I have been working on Model Driven Architecture, Meta-data driven software, Modeling tools and Ontology for over 4 years. I have also implemented a suite of software supporting MDA and Ontologies. The material that this books covered just speaks my words out. Now, I can easily articulate my MDA/Ontology problems out with this book. Life is so easy with the help of a book which is in line of my work. Hooray!

Title can mislead: Greater focus on methods than content of ontologies
I was disappointed in this book in the lack of depth or breadth on a couple of key areas as follows:

Item 1 - It appeared to me that ontologies were not explained in sufficient detail to help "newbies" determine exactly why a specific ontology was created and the specific functions it is designed to support.I'm considering the UMLS to prototype a search engine because it is a compilation of a number of ontologies.However, this provides numerous options for subsetting the UMLS; for those relatively new to these ontologies it is not clear which ontology subsets are the most important (i.e., which play a pivotal or lesser role for my intended use).For example, I can include SNOMED and HL7 subsets, but do they provide redundant concepts for my needs and if so, in which areas?It is not practical for me to learn then all, so I was hoping to obtain this information from this book.Unfortunately I was unable to do so quickly and have to dig more on my own (which was the reason to buy the book.....).

Item 2 - I noticed a couple of areas that may be missing for those more interested in the tools and process of using ontologies.For example, I did not see the Protege and LexGrid tools for visualization, from Stanford and Mayo Clinic respectively, in the index (I had located them in prior web search).In a more general sense, as I've been working with ontologies more and more, I've noticed that platform selection and interoperability seem to be stumbling blocks.This topic was not a major focus in the book.

Due to the breadth and complexity of the platforms and tools needed to effectively use ontologies, and the complexity and size of the ontologies themselves, addressing both of these large subjects in sufficient detail in one book may be very difficult.Perhaps this is the reason that this book did not reach the 5 stars potential.Perhaps it would if it had addressed one or the other, but not both.

Perhaps if the description and title clarified the focus of the book, I would have gotten a better idea of the content before purchasing (and unfortunately, returning).

Keep in mind that I'm relatively new to using ontologies. ... Read more

Product Description

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

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

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

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

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

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

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

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Guiding readers from the elucidation and analysis of a genomic sequence to the prediction of a protein structure and the identification of the molecular function, Introduction to Bioinformatics describes the rationale and limitations of the bioinformatics methods and tools that can help solve biological problems. Requiring only a limited mathematical and statistical background, the book shows how to efficiently apply these approaches to biological data and evaluate the resulting information.

The author, an expert bioinformatics researcher, first addresses the ways of storing and retrieving the enormous amount of biological data produced every day and the methods of decrypting the information encoded by a genome. She then covers the tools that can detect and exploit the evolutionary and functional relationships among biological elements. Subsequent chapters illustrate how to predict the three-dimensional structure of a protein. The book concludes with a discussion of the future of bioinformatics.

Even though the future will undoubtedly offer new tools for tackling problems, most of the fundamental aspects of bioinformatics will not change. This resource provides the essential information to understand bioinformatics methods, ultimately facilitating in the solution of biological problems. ... Read more

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A collection of readily reproducible bioinformatic methods to advance the drug discovery process from gene identification to protein modeling to the identification of specific drug candidates. The authors demonstrate these techniques, including microarray analysis, the analysis of genes as potential drug targets, virtual screening and in silico protein design, and cheminformatics, in a variety of practical situations. Because these technologies are still emergent, each chapter contains an extended introduction that explains the theory and application of the technology and techniques described. ... Read more

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Univ. College, London, UK. Covers both the traditional approaches including gene and protein sequence analysis and structure prediction, and more recent technologies such as datamining to provide insights on cellular mechanisms.Written specifically for advanced level courses for undergraduates. Softcover. ... Read more

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This monograph addresses, in a systematic and pedagogical manner, the mathematical methods and the algorithms required to deal with the molecularly based problems of bioinformatics. Prominent attention is given to pair-wise and multiple sequence alignment algorithms, stochastic models of mutations, modulus structure theory and protein configuration analysis. Strong links to the molecular structures of proteins, DNA and other biomolecules and their analyses are developed.