Bioinformatics in The Books Store

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.

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

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.

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 wrote much of the statistical modeling software in 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, and projection pursuit.

FROM THE REVIEWS:

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

Departing from O'Reilly's earlier monograph Developing Bioinformatic Computer Skills, Tisdall's text is organized aggressively along didactic lines. Nearly all of the 13 chapters begin with twin bullet lists of Perl programming tools and the bioinformatic methods that require them. Likewise, the chapters end with exercises. String concatenation is illustrated with gene splicing, and regular expressions are taught with gene transcription and motif searching.

Tisdall emphasizes sequence examples throughout, leading up to an introduction to a Perl interface for the NIH GenBank biological database and the widely used BLAST sequence alignment tool. After a brief discussion of three-dimensional protein structure, he returns to sequence extraction and secondary structure prediction.

Tisdall's goal is to boost the beginning programmer into a domain of self-learning. He imparts essential etiquette for the success of programming newbies: use the wealth or resources available, from user documentation to Web site surveys to FAQs to How-To's to news groups and finally to direct personal appeals for help from a senior colleague. A well-plugged-in bioinformatics Perl student will soon discover Bioperl, an open-source effort to bring research-grade bioinformatic tools to the Perl community. Bioperl is described briefly at the end of Tisdall's book and will reportedly be a forthcoming title of its own in the O'Reilly bioinformatics series.

Although he introduces bioinformatics as an academic discipline, Tisdall treats it as a trade throughout his book. He indicates that open questions and computational hard problems exist, but does not describe what they are or how they are being tackled. Ultimately, Tisdall presents bioinformatics as another arrow in a bench scientist's quiver, very much like HPLC, 2D-PAGE, and the various spectroscopies.

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.

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.

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.