computational-biology books


	Bioinformatics and Computational Biology Solutions Using R and Bioconductor (Statistics for Biology and Health) publisher: Springer, published: 2005-08-31 ISBN: 0387251464 sales rank: 130718 Product Description Bioconductor is a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology. Bioconductor is rooted in the open source statistical computing environment R. This volume's coverage is broad and ranges across most of the key capabilities of the Bioconductor project, including importation and preprocessing of high-throughput data from microarray, proteomic, and flow cytometry platforms: Curation and delivery of biological metadata for use in statistical modeling and interpretation Statistical analysis of high-throughput data, including machine learning and visualization Modeling and visualization of graphs and networks The developers of the software, who are in many cases leading academic researchers, jointly authored chapters. All methods are illustrated with publicly available data, and a major section of the book is devoted to exposition of fully worked case studies. This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text; it is a dynamic document. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers. Review This book is great for helping you get started analyzing all types of microarrays in R. However, the chapters are written by several different authors which causes the book to be a little disorganized. This is probably the case with many books that have contributed chapters. In the end, the technical information is there, sometimes you just have to visit a couple of different chapters. I find this book is not so good for people without any gene or microarray experiment background. It didn't even give clear definition of the basic concepts. Another problem is that it's not well organized because every chapter is written by different authors who have different interest and preference and use slightly different terms for the same thing. If you're like me, you came upon this book because you decided to use R for analysis of microarray data, but you're mired in its gory and frustrating details. Yes, you need a reference book. But not this one, and certainly not this edition. Better documentation can be found elsewhere (dare I say online?). The code examples given are technically accurate and run as advertised, but they are of the "monkey see, monkey do" variety. They provide little intuition for how to use R for oneself, outside the covers of this text. For example, Chapter 23 discusses linear models for microarray data (using the "limma" package), and several code examples contain the parameter 'adjust = "fdr"'. The reader is never enlightened that this refers to a "false discovery rate" adjustment. In other cases, example code is simply missing. Chapter 21 covers the Rgraphviz graphing library, with a figure showing the three common graphical layouts -- but no example code for producing these graphs is given (I had to find it outside the book). For those trying to use R for computational biology, I recommend getting an overview of the R programming language first (Venables and Ripley's book "Modern Applied Statistics with S" is a great text), and only then wading into references such as this one, if at all.
	Computational Cell Biology publisher: Springer, published: 2005-02-15 ISBN: 0387953698 sales rank: 261359 Product Description This textbook provides an introduction to dynamic modeling in cell biology, emphasizing computational approaches based on realistic molecular mechanisms. It is designed to introduce cell biology and neuroscience students to computational modeling, and applied mathematics students, theoretical biologists, and engineers to many of the problems in dynamical cell biology. This volume was conceived of and begun by Professor Joel Keizer based on his many years of teaching and research together with his colleagues. The project was expanded and finished by his students and friends after his untimely death in 1999. Carefully selected examples are used to motivate the concepts and techniques of computational cell biology, through a progression of increasingly more complex and demanding cases. Illustrative exercises are included with every chapter, and mathematical and computational appendices are provided for reference. This textbook will be useful for advanced undergraduate and graduate theoretical biologists, and for mathematics students and life scientists who wish to learn about modeling in cell biology. Royalties from this book will be donated to the Joel E. Keizer memorial endowment for collaborative interdisciplinary research in the life sciences. Review
	An Introduction to Systems Biology: Design Principles of Biological Circuits (Chapman & Hall/Crc Mathematical and Computational Biology) by: Uri Alon publisher: Chapman & Hall/CRC, published: 2006-07-07 ISBN: 1584886420 sales rank: 62955 Product Description This bookpresents recently discovered design principles that govern the structure and behavior of biological networks such as gene circuits, highlighting simple, recurring circuit elements that make up the network. It provides a quantitative theory for which circuits are found in a given environment and a mathematical framework for understanding and even designing biological circuits. The book requires only basic mathematics and includes a review of the necessary background material. It fills a significant need for a textbook and introduction to the concepts, principles, and mathematical tools that will form the basis of future developments in the field. Review If you have any interest in how life actually works, you should read this book. It weighs in at less than 300 pages, which makes it very approachable. But it manages to pack a wide array of fascinating material into those pages. Life is complicated. And there is no reason to expect it to be readily comprehensible. Yet over the last few decades we have found that biological systems make extensive and repetitive use of certain patterns of functionality, and that these patterns often embody good design principles as practiced by human engineers. Concepts such as modularity, robustness, and even optimality are found to be reflected in biological systems and exploitable to make verifiable predictions about how biological systems operate experimentally. It is worth noting that while this book is deeply fascinating, it is not math free. Indeed the author began his career as a mathematician and the reader will find it helpful to have some knowledge of basic ordinary differential equations, calculus, and elementary algebra. On the other hand, very little if any biochemistry is required. What a great book! If you have any interest in how biological systems function and what principles lay in their most basic designs, you must read it! It's very clearly written. I think almost everyone will find it accessible and interesting, no matter what your education level is or your primary field (but I think engineers will get a particular kick out of it). There is quite a bit of math, but it's very basic and even if you skip all of it, you will still learn a lot from the book. I found the whole subject fascinating and intriguing. Sure, biological systems are not as simple as this book may seem to portray, but there is no doubt in my mind that the ideas presented here are quite relevant to biology and indeed underlay many of the real-world biological phenomena. Awesome book! The author did a great job in presenting the topics in an easy and enjoyable way.
	Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation (Wiley Series in Mathematical & Computational Biology) by: O. Diekmann, J. A. P. Heesterbeek publisher: Wiley, published: 2000-05-25 ISBN: 0471492418 sales rank: 348198 Product Description Provides systematic coverage of the mathematical theory of modelling epidemics in populations, with a clear and coherent discussion of the issues, concepts and phenomena. Mathematical modelling of epidemics is a vast and important area of study and this book helps the reader to translate, model, analyse and interpret, with numerous applications, examples and exercises to aid understanding. Review Great book but unless you are a computational biologist with very advanced mathematical skills-don't bother. This was over my head as a basic epidemiology student. I was searching for a book to elucidate R-zero...this was not it. I purchased this book partially because one review proposed that this text would serve as a good, teach-yourself introduction to mathematical modeling of infectious disease (I.D.). I'm currently in a master's-level class on I.D. modeling which has no specific text requirement, and having only a so-so math background and little knowledge of model construction, I thought a self-teach book would be nice. Simply said, this book is not for those who stumbled through calculus 1. In fact, unless you're quite well-to-do in the math department, you'll find much of this text either very challenging or impenetrable. The worked-out problems are a very nice touch--one that many authors would do well to note--but the high-level math is too much for this budding epidemiologist.
	Computational Molecular Biology: An Algorithmic Approach (Computational Molecular Biology) by: Pavel A. Pevzner publisher: The MIT Press, published: 2000-08-21 ISBN: 0262161974 sales rank: 146588 Product Description In one of the first major texts in the emerging field of computational molecular biology, Pavel Pevzner covers a broad range of algorithmic and combinatorial topics and shows how they are connected to molecular biology and to biotechnology. The book has a substantial "computational biology without formulas" component that presents the biological and computational ideas in a relatively simple manner. This makes the material accessible to computer scientists without biological training, as well as to biologists with limited background in computer science. Computational Molecular Biology series Computer science and mathematics are transforming molecular biology from an informational to a computational science. Drawing on computational, statistical, experimental, and technological methods, the new discipline of computational molecular biology is dramatically increasing the discovery of new technologies and tools for molecular biology. The new MIT Press Computational Molecular Biology series provides a unique venue for the rapid publication of monographs, textbooks, edited collections, reference works, and lecture notes of the highest quality. Review Dr. Pevzner writes with a very lucid and conversational style about very complex and seemingly inscrutable topics. As a biologist who works primarily with computational tools in the field of genomics, this resource has helped to provide me with more than a rudimentary understanding of the algorithms and logic lurking in the methods of sequence analysis. Explaining dynamic programming to a biologist with rudimentary programming skills is a daunting task. However, his description of sequence alignment algorithms (including dynamic programming) in chapter 6 is quite readable and the information is very accessible. I highly recommend this book if you want a comprehensive understanding of the computational biologists toolkit. Pevzner has written a very useful book on bioinformatics algorithms, and one that seems reasonably up to date. The table of contents follows a classic plan: restriction maps, assembly and sequencing, 2- and N- way string comparisons, and analysis of rearrangements. There's a good but brief section on mass spec analysis - unfortunately, that chapter is called "Proteomics" even though the term covers a lot more than MS. Other sections skim the surface of hidden Markov models and Gibbs sampling for finding patterns ("motifs") in DNA. A few chapters have unusual strengths. The "Conway Equation" gives more insight in analysis of motif significance than other introductory books do. The section in sequence comparison pays a lot more attention to BLAST-like algorithms than other books do, also - modern material you'd normally see only in the journals. Also, the section on rearrangements gives some ideas about using rearrangement data for phylogenetic analysis. That really gives the material meaning. Rearrangements aren't just string operations, they're features of evolution, and they can be compared to each other. No matter what the discussion, Pevzner keeps maintains a readable and enjoyably informal tone. The book does have some weaknesses, though. It's a bit advanced for an undergrad intro, but bottoms out before the Baum-Welch algorithm, for example. Discussion of microarrays for sequencing seems dated. Pevnzer describes their use in sequencing, a rarity now, but skips their use in functional gneomics, where they are used most often. Illustration style is erratic and many diagrams are oddly stretched (3.5, 5.7, 8.3, and others, some much worse). Formal analysis of the algorithms is weak, but Pevzner somewhat makes up for that with better statistical analysis than many authors give. Also, even though the book was reprinted in 2001, it still estimates 100K genes in the human genome. This is a good second book, maybe the one to read after Pevzner's newer "Introduction". It covers most of the basics and gives fairly usable pseudocode. Most of all, it always keeps the biology in mind. That, by itself, makes this book stand out. //wiredweird An excellent book for studying computational molecular biology from an algorithmic perspective. (But if you never took mathematics seriously, you are forewarned.)
	Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R (Mathematical and Computational Biology Series) by: Gabriel Valiente publisher: Chapman & Hall/CRC, published: 2009-04-08 ISBN: 142006973X sales rank: 948115 Product Description Emphasizing the search for patterns within and between biological sequences, trees, and graphs, this book shows how combinatorial pattern matching algorithms can solve computational biology problems that arise in the analysis of genomic, transcriptomic, proteomic, metabolomic, and interactomic data. It provides an intuitive presentation of the algorithms, followed by a detailed exposition in pseudo-code. The author offers alternative implementations of the algorithms in Perl and R to enable the testing of algorithms and the building of projects based on the code. He also includes the Perl and R source code for all the algorithms on his website. Review
	Computational Modelling Of Gene Regulatory Networks -- A Primer by: Hamid Bolouri publisher: Imperial College Press, published: 2008-08-13 ISBN: 1848162219 sales rank: 615185 Product Description This book serves as an introduction to the myriad computational approaches to gene regulatory modeling and analysis, and is written specifically with experimental biologists in mind. Mathematical jargon is avoided and explanations are given in intuitive terms. In cases where equations are unavoidable, they are derived from first principles or, at the very least, an intuitive description is provided. Extensive examples and a large number of model descriptions are provided for use in both classroom exercises as well as self-guided exploration and learning. As such, the book is ideal for self-learning and also as the basis of a semester-long course for undergraduate and graduate students in molecular biology, bioengineering, genome sciences, or systems biology. Contents: Introduction; What Is a System, and Why Should We Care?; What Models Can and Cannot Predict; Why Make Computational Models of Gene Regulatory Networks?; Graphical Representations of Gene Regulatory Networks; Implicit Modeling via Interaction Network Maps; The Biochemical Basis of Gene Regulation; A Single-Cell Model of Transcriptional Regulation; Simplified Models: Mass-Action Kinetics; Simplified Models: Boolean and Multi-valued Logic; Simplified Models: Bayesian Networks; The Relationship between Logic and Bayesian Networks; Network Inference in Practice; Searching DNA Sequences for Transcription Factor Binding Sites; Model Selection Theory; Simplified Models -- GRN State Signatures in Data; System Dynamics; Robustness Analysis; GRN Modules and Building Blocks; Notes on Data Processing for GRN Modeling; Applications of Computational GRN Modeling; Quo Vadis. Review
	Kinetic Modelling in Systems Biology (Chapman & Hall/Crc Mathematical and Computational Biology) by: Oleg Demin, Igor Goryanin publisher: Chapman & Hall/CRC, published: 2008-10-24 ISBN: 1584886676 sales rank: 839865 Product Description Focusing on one of the main pillars in the future development of systems biology, this book explores the kinetic modeling approach and how to apply it to solve real-life problems. It covers model creation techniques, model verification and validation, and simulations and interpretation of biological results. The authors describe underlying algorithms for selected reference methods and present several examples of applications, ranging from drug safety mechanisms to multiple target identification analysis to optimization of E. coli amino acid biosynthesis. The accompanying CD-ROM contains pathway diagrams, model examples, EPE software, and DBSolve installation. Review
	The Problem Solving Handbook for Computational Biology and Bioinformatics (Lecture notes in mathematics) publisher: Springer, published: 2009-10 ISBN: 0387097597 sales rank: 7985101 Product Description Bioinformatics is growing by leaps and bounds; theories/algorithms/statistical techniques are constantly evolving. Nevertheless, a core body of algorithmic ideas have emerged and researchers are beginning to adopt a "problem solving" approach to bioinformatics, wherein they use solutions to well-abstracted problems as building blocks to solve larger scope problems. The Problem Solving Handbook for Computational Biology and Bioinformatics is an edited volume contributed by world renowned leaders in this field. This comprehensive handbook with problem solving emphasis, covers all relevant areas of computational biology and bioinformatics. Web resources and related themes are highlighted at every opportunity in this central easy-to-read reference. Designed for advanced-level students, researchers and professors in computer science and bioengineering as a reference or secondary text, this handbook is also suitable for professionals working in this industry. Review
	Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology by: Dan Gusfield publisher: Cambridge University Press, published: 1997-05-28 ISBN: 0521585198 sales rank: 57033 Product Description Traditionally an area of study in computer science, string algorithms have, in recent years, become an increasingly important part of biology, particularly genetics. This volume is a comprehensive look at computer algorithms for string processing. In addition to pure computer science, Gusfield adds extensive discussions on biological problems that are cast as string problems and on methods developed to solve them. This text emphasizes the fundamental ideas and techniques central to today's applications. New approaches to this complex material simplify methods that up to now have been for the specialist alone. With over 400 exercises to reinforce the material and develop additional topics, the book is suitable as a text for graduate or advanced undergraduate students in computer science, computational biology, or bio-informatics. Review If you want to learn about how to make suffix trees and other algorithms this is a great book. The book was like new. The shipping was fast. I am very happy with this purchase!!! This book is absolutely excellent. Gusfield walks the reader from simple concepts in string matching through advanced in a way that I found very easy to follow. Every bioinformatics researcher should have copy of this text.

Books on 'computational-biology'