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Books : Science : Biological Sciences : Bioinformatics
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R is an Open Source implementation of the S language. It works on multiple computing platforms and can be freely downloaded. R is now in widespread use for teaching at many levels as well as for practical data analysis and methodological development.
This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. A supplementary R package can be downloaded and contains the data sets.
The statistical methodology includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one- and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last six chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, survival analysis, Poisson regression, and nonlinear regression.
In the second edition, the text and code have been updated to R version 2.6.2. The last two methodological chapters are new, as is a chapter on advanced data handling. The introductory chapter has been extended and reorganized as two chapters. Exercises have been revised and answers are now provided in an Appendix.
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During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for ``wide'' data (p bigger than n), including multiple testing and false discovery rates.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.
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Were you always curious about biology but were afraid to sit through long hours of dense reading? Did you like the subject when you were in high school but had other plans after you graduated? Now you can explore the human genome and analyze DNA without ever leaving your desktop!Bioinformatics For Dummies is packed with valuable information that introduces you to this exciting new discipline. This easy-to-follow guide leads you step by step through every bioinformatics task that can be done over the Internet. Forget long equations, computer-geek gibberish, and installing bulky programs that slow down your computer. You’ll be amazed at all the things you can accomplish just by logging on and following these trusty directions. You get the tools you need to:
- Analyze all types of sequences
- Use all types of databases
- Work with DNA and protein sequences
- Conduct similarity searches
- Build a multiple sequence alignment
- Edit and publish alignments
- Visualize protein 3-D structures
- Construct phylogenetic trees
This up-to-date second edition includes newly created and popular databases and Internet programs as well as multiple new genomes. It provides tips for using servers and places to seek resources to find out about what’s going on in the bioinformatics world. Bioinformatics For Dummies will show you how to get the most out of your PC and the right Web tools so you’ll be searching databases and analyzing sequences like a pro!
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Probablistic models are becoming increasingly important in analyzing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analyzing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it is accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time presents the state of the art in this new and important field.
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The practice of modern medicine requires sophisticated information technologies with which to manage patient information, plan diagnostic procedures, interpret laboratory results, and conduct research. This book, inspired by a Stanford University training program developed to introduce health professionals to computer applications in modern medical care, fills the need for a high quality text in computers and medicine, and meets the growing demand by practitioners, researchers, and students for a comprehensive introduction to key topics in the field. The work is designed for a broad audience interested in the intersection of computer science and medicine.
Completely revised and expanded, the Third Edition (previously titled "Medical Informatics") includes several new chapters filled with brand new material. This book will provide both a conceptual framework and a practical approach for the implementation and management of IT used to improve the delivery of health care. Designed for use by professors and students of medical informatics and for practicing professionals, this book will focus on the role of computers in the provision of medical services. Biomedial Informatics, Third Edition, provides the conceptual base needed to comprehend and utilize medical informatics through easy to understand examples that demonstrate how computers assist in the delivery of health care. This text also includes pointers to additional literature, chapter summaries, and concise definition of recurring terms for self-study or classroom use.
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Bioconductor software has become a standard tool for the analysis and comprehension of data from high-throughput genomics experiments. Its application spans a broad field of technologies used in contemporary molecular biology. In this volume, the authors present a collection of cases to apply Bioconductor tools in the analysis of microarray gene expression data. Topics covered include
* import and preprocessing of data from various sources
* statistical modeling of differential gene expression
* biological metadata
* application of graphs and graph rendering
* machine learning for clustering and classification problems
* gene set enrichment analysis
Each chapter of this book describes an analysis of real data using hands-on example driven approaches. Short exercises help in the learning process and invite more advanced considerations of key topics. The book is a dynamic document. All the code shown can be executed on a local computer, and readers are able to reproduce every computation, figure, and table.
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This introductory text offers a clear exposition of the algorithmic principles driving advances in bioinformatics. Accessible to students in both biology and computer science, it strikes a unique balance between rigorous mathematics and practical techniques, emphasizing the ideas underlying algorithms rather than offering a collection of apparently unrelated problems.
The book introduces biological and algorithmic ideas together, linking issues in computer science to biology and thus capturing the interest of students in both subjects. It demonstrates that relatively few design techniques can be used to solve a large number of practical problems in biology, and presents this material intuitively.
An Introduction to Bioinformatics Algorithms is one of the first books on bioinformatics that can be used by students at an undergraduate level. It includes a dual table of contents, organized by algorithmic idea and biological idea; discussions of biologically relevant problems, including a detailed problem formulation and one or more solutions for each; and brief biographical sketches of leading figures in the field. These interesting vignettes offer students a glimpse of the inspirations and motivations for real work in bioinformatics, making the concepts presented in the text more concrete and the techniques more approachable.
PowerPoint presentations, practical bioinformatics problems, sample code, diagrams, demonstrations, and other materials can be found at the Authors' website.
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From the co-developer of R and lead founder of the Bioconductor ProjectThanks to its data handling and modeling capabilities and its flexibility, R is becoming the most widely used software in bioinformatics. R Programming for Bioinformatics builds the programming skills needed to use R for solving bioinformatics and computational biology problems.
Drawing on the author’s experiences as an R expert, the book begins with coverage on the general properties of the R language, several unique programming aspects of R, and object-oriented programming in R. It presents methods for data input and output as well as database interactions. The author also examines different facets of string handling and manipulations, discusses the interfacing of R with other languages, and describes how to write software packages. He concludes with a discussion on the debugging and profiling of R code.
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Remarkable advances in computation and data storage and the ready availability of huge data sets have been the keys to the growth of the new disciplines of data mining and machine learning, while the enormous success of the Human Genome Project has opened up the field of bioinformatics.
These exciting developments, which led to the introduction of many innovative statistical tools for high-dimensional data analysis, are described here in detail. The author takes a broad perspective; for the first time in a book on multivariate analysis, nonlinear methods are discussed in detail as well as linear methods. Techniques covered range from traditional multivariate methods, such as multiple regression, principal components, canonical variates, linear discriminant analysis, factor analysis, clustering, multidimensional scaling, and correspondence analysis, to the newer methods of density estimation, projection pursuit, neural networks, multivariate reduced-rank regression, nonlinear manifold learning, bagging, boosting, random forests, independent component analysis, support vector machines, and classification and regression trees. Another unique feature of this book is the discussion of database management systems.
This book is appropriate for advanced undergraduate students, graduate students, and researchers in statistics, computer science, artificial intelligence, psychology, cognitive sciences, business, medicine, bioinformatics, and engineering. Familiarity with multivariable calculus, linear algebra, and probability and statistics is required. The book presents a carefully-integrated mixture of theory and applications, and of classical and modern multivariate statistical techniques, including Bayesian methods. There are over 60 interesting data sets used as examples in the book, over 200 exercises, and many color illustrations and photographs.
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Suitable for advanced undergraduates and postgraduates, Understanding Bioinformatics provides a definitive guide to this vibrant and evolving discipline. The book takes a conceptual approach. It guides the reader from first principles through to an understanding of the computational techniques and the key algorithms. Understanding Bioinformatics is an invaluable companion for students from their first encounter with the subject through to more advanced studies.
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Genome sequences are now available that enable us to determine the biological components that make up a cell or an organism. The new discipline of systems biology examines how these components interact and form networks, and how the networks generate whole cell functions corresponding to observable phenotypes. This textbook describes how to model networks, determine their properties, and relate these to phenotypic functions. Some knowledge of linear algebra and biochemistry is required, since the book reflects the irreversible trend of increasing mathematical content in biology education.
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Sequence similarity is a powerful tool for discovering biological function. Just as the ancient Greeks used comparative anatomy to understand the human body and linguists used the Rosetta stone to decipher Egyptian hieroglyphs, today we can use comparative sequence analysis to understand genomes. BLAST (Basic Local Alignment Search Tool), is a sophisticated software package for rapid searching of nucleotide and protein databases. It is one of the most important software packages used in sequence analysis and bioinformatics. Most users of BLAST, however, seldom move beyond the program's default parameters, and never take advantage of its full power. BLAST is the only book completely devoted to this popular suite of tools. It offers biologists, computational biology students, and bioinformatics professionals a clear understanding of BLAST as well as the science it supports. This book shows you how to move beyond the default parameters, get specific answers using BLAST, and how to interpret your results. The book also contains tutorial and reference sections covering NCBI-BLAST and WU-BLAST, background material to help you understand the statistics behind BLAST, Perl scripts to help you prepare your data and analyze your results, and a wealth of tips and tricks for configuring BLAST to meet your own research needs. Some of the topics covered include:- BLAST basics and the NCBI web interface
- How to select appropriate search parameters
- BLAST programs: BLASTN, BLASTP, BLASTX, TBLASTN, TBLASTX, PHI-BLAST, and PSI BLAST
- Detailed BLAST references, including NCBI-BLAST and WU-BLAST
- Understanding biological sequences
- Sequence similarity, homology, scoring matrices, scores, and evolution
- Sequence Alignment
- Calculating BLAST statistics
- Industrial-strength BLAST, including developing applications with Perl and BLAST
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Historically, programming hasn't been considered a critical skill for biologists. But now, with access to vast amounts of biological data contained in public databases, programming skills are increasingly in strong demand in biology research and development. Perl, with its highly developed capacities in string handling, text processing, networking, and rapid prototyping, has emerged as the programming language of choice for biological data analysis."Mastering Perl for Bioinformatics" covers the core Perl language and many of its module extensions, presenting them in the context of biological data and problems of pressing interest to the biological community. This book, along with "Beginning Perl for Bioinformatics," forms a basic course in Perl programming. This second volume finishes the basic Perl tutorial material (references, complex data structures, object-oriented programming, use of modules--all presented in a biological context) and presents some advanced topics of considerable interest in bioinformatics.
The range of topics covered in "Mastering Perl for Bioinformatics" prepares the reader for enduring and emerging developments in critical areas of bioinformatics programming such as:
Gene finding
String alignment
Methods of data storage and retrieval (SML and databases)
Modeling of networks (graphs and Petri nets)
Graphics (Tk)
Parallelization
Interfacing with other programming languages
Statistics (PDL)
Protein structure determination
Biological models of computation (DNA Computers)
Biologists and computer scientists who have conquered the basics of Perl and are ready to move even further in their mastery of this versatile languagewill appreciate the author's well-balanced approach to applying Perl's analytical abilities to the field of bioinformatics. Full of practical examples and real-world biological problem solving, this book is a must for any reader wanting to move beyond beginner level Perl in bioinformatics.
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This book is the first complete guide to valuation in life sciences for industry professionals, investors, and academics. Boris Bogdan and Ralph Villiger introduce the characteristics of drug and medical device development, explain how to translate these into the valuation, and provide valuable industry data. After guiding the reader through the theory of valuation, including discounted cash-flow and real options, the authors demonstrate how to value projects, patents, licences, firms and stocks on real-life examples, even treating complex licence and company structures. Special emphasis is put on the practicability of the proposed methods by including many hands-on examples, without compromising on realistic results. Ralph Villiger and Boris Bogdan have written what is sure to become the industry standard reference for valuation of pharmaceutical and biotechnology projects and companies. At a time when the healthcare industry is placing increasing emphasis on licensing and M&A as a core strategy this book provides a firm understanding of the way in which products and businesses can be valued at all stages of their development.
Dr. Martin Buckland, Chief Business Officer, Astex Therapeutics, Cambridge, UK
The book presents a number of innovative ideas, illustrated with practical examples that should improve decision-making in the drug development process, intellectual property evaluation, licensing and sublicensing. The authors make a persuasive case for the use of advanced techniques and the section on worked-examples should be particularly appealing to practitioners.
Dr. Martin Grossmann, Novartis Pharmaceuticals
There is no doubt that this book will become an essential reference tool for professionals in technology transfer, business developers and biotech companies, as well as the pharmaceutical industry and Life Science investors.
Jean-Pierre Saintouil, Director of Technology Transfer Department, Institut Pasteur, Paris, France
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This richly illustrated book describes the use of interactive and dynamic graphics as part of multidimensional data analysis. Chapters include clustering, supervised classification, and working with missing values. A variety of plots and interaction methods are used in each analysis, often starting with brushing linked low-dimensional views and working up to manual manipulation of tours of several variables. The role of graphical methods is shown at each step of the analysis, not only in the early exploratory phase, but in the later stages, too, when comparing and evaluating models.
All examples are based on freely available software: GGobi for interactive graphics and R for static graphics, modeling, and programming. The printed book is augmented by a wealth of material on the web, encouraging readers follow the examples themselves. The web site has all the data and code necessary to reproduce the analyses in the book, along with movies demonstrating the examples.
The book may be used as a text in a class on statistical graphics or exploratory data analysis, for example, or as a guide for the independent learner. Each chapter ends with a set of exercises.
The authors are both Fellows of the American Statistical Association, past chairs of the Section on Statistical Graphics, and co-authors of the GGobi software. Dianne Cook is Professor of Statistics at Iowa State University. Deborah Swayne is a member of the Statistics Research Department at AT&T Labs.
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Bioinformatics--the application of computational and analytical methods to biological problems--is a rapidly evolving scientific discipline. Genome sequencing projects are producing vast amounts of biological data for many different organisms, and, increasingly, storing these data in public databases. Such biological databases are growing exponentially, along with the biological literature. It's impossible for even the most zealous researcher to stay on top of necessary information in the field without the aid of computer-based tools. Bioinformatics is all about building these tools. Developing Bioinformatics Computer Skills is for scientists and students who are learning computational approaches to biology for the first time, as well as for experienced biology researchers who are just starting to use computers to handle their data. The book covers the Unix file system, building tools and databases for bioinformatics, computational approaches to biological problems, an introduction to Perl for bioinformatics, data mining, and data visualization. Written in a clear, engaging style, Developing Bioinformatics Computer Skills will help biologists develop a structured approach to biological data as well as the tools they'll need to analyze the data.
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KEY BENEFIT: Discovering Genomics is the first genomics text that combines web activities and case studies with a problem-solving approach to teach upper-level undergraduates and first-year graduate students the fundamentals of genomic analysis. More of a workbook than a traditional text, Discovering Genomics, Second Edition allows students to work with real genomic data in solving problems and provides the user with an active learning experience. KEY TOPICS: Genomic Medicine Case Study: What’s wrong with my child? Genome Sequence Acquisition and Analysis, Comparative Genomics in Evolution and Medicine, Genome Variations, Genomic Medicine Case Study: Why Can’t I Just Take a Pill to Lose Weight? Basic Research with DNA Microarrays, Applied Research with DNA Microarrays, Proteomics, Genomic Medicine Case Study: Why Can’t We Cure More Diseases? Genomic Circuits in Single Genes, Integrated Genomic Circuits, Modeling Whole-Genome Circuits. MARKET: For all readers interested in genomics.
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The Semantic Web: Semantics for Data and Services on the Web (Data-Centric Systems and Applications)
The Semantic Web is a vision – the idea of having data on the Web defined and linked in such a way that it can be used by machines not just for display purposes but for automation, integration and reuse of data across various applications. Technically, however, there is a widespread misconception that the Semantic Web is primarily a rehash of existing AI and database work focused on encoding knowledge representation formalisms in markup languages such as RDF(S), DAML+OIL or OWL.
Kashyap, Bussler, and Moran seek to dispel this notion by presenting the broad dimensions of this emerging Semantic Web and the multi-disciplinary technological underpinnings like machine learning, information retrieval, service-oriented architectures, and grid computing, thus combining the informational and computational aspects needed to realize the full potential of the Semantic Web vision. Throughout the book, the use-case of a clinical vignette will serve to motivate and explain solutions based on Semantic Web technologies, emphasizing the application aspects related to data integration, knowledge acquisition, change management, semantic web services, and workflow management.
With this textbook, the authors deliver an application-driven state-of-the-art presentation of Semantic Web technologies, ideally suited for academic courses on the Semantic Web and architectures of information systems, and for self-studying professionals engaged in the design and implementation of advanced application systems.
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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).