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Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health)
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).
Author: Warren J. Ewens, Gregory Grant
Hardcover: 588 pages
Company: Springer (2005-09-30)
ISBN: 0387400826
List Price: $94.95
Amazon Price: $58.22
Used Price: $47.48
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An Introduction to Bioinformatics Algorithms (Computational Molecular Biology)
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.
Author: Neil C. Jones, Pavel A. Pevzner
Hardcover: 454 pages
Company: The MIT Press (2004-08-01)
ISBN: 0262101068
List Price: $60.00
Amazon Price: $43.00
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Discovering Genomics, Proteomics and Bioinformatics (2nd Edition) (The Genetics Place Series)
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.
Author: A. Malcolm Campbell, Laurie J. Heyer
Paperback: 464 pages
Company: Benjamin Cummings (2006-03-12)
ISBN: 0805382194
List Price: $101.80
Amazon Price: $60.49
Used Price: $56.49
Essential Bioinformatics is a concise yet comprehensive textbook of bioinformatics, which provides a broad introduction to the entire field. Written specifically for a life science audience, the basics of bioinformatics are explained, followed by discussions of the state-of-the-art computational tools available to solve biological research problems. All key areas of bioinformatics are covered including biological databases, sequence alignment, genes and promoter prediction, molecular phylogenetics, structural bioinformatics, genomics and proteomics. The book emphasizes how computational methods work and compares the strengths and weaknesses of different methods. This balanced yet easily accessible text will be invaluable to students who do not have sophisticated computational backgrounds. Technical details of computational algorithms are explained with a minimum use of mathematical formulae; graphical illustrations are used in their place to aid understanding. The effective synthesis of existing literature as well as in-depth and up-to-date coverage of all key topics in bioinformatics make this an ideal textbook for all bioinformatics courses taken by life science students and for researchers wishing to develop their knowledge of bioinformatics to facilitate their own research.
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.
With its highly developed capacity to detect patterns in data, Perl has become one of the most popular languages for biological data analysis. But if you’re a biologist with little or no programming experience, starting out in Perl can be a challenge. Many biologists have a difficult time learning how to apply the language to bioinformatics. The most popular Perl programming books are often too theoretical and too focused on computer science for a non-programming biologist who needs to solve very specific problems.
