Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health)

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Three lecture hours and one discussion hour a week for one semester. Prerequisite: A passing score on the College of Natural Sciences mathematics placement examination, and six semester hours of coursework in biology. Students may receive credit for only one of the following: Statistics and Data Sciences or , or Biology M, or Mathematics K. Introduction to the use of statistical or mathematical applications for data analysis.

Statistics for Biology and Health | Mitchell Gail | Springer

Two hours per week for eight weeks. May be repeated for credit when the topics vary. Prerequisites vary with the topic and are given in the Course Schedule. Covers simple and multiple regression, fundamentals of experimental design, and analysis of variance methods. Other topics will be selected from the following: logistic regression, Poisson regression, resampling methods, introduction to Bayesian methods, and probability models. Includes substantial use of statistical software. Three lecture hours and one laboratory hour a week for one semester. Prerequisite: Statistics and Data Sciences , , , , or Mathematics Advanced topics in statistical modeling, including models for categorical and count data; spatial and time-series data; and survival, hazard, and hierarchical models.

Extensive use of statistical software to build on knowledge of introductory probability and statistics, as well as multiple regression. Prerequisite: Upper-division standing; additional prerequisites may vary with the topic and are given in the Course Schedule. An introduction to quantitative analysis using fundamental concepts in statistics and scientific computation.

Statistical Methods in Bioinformatics An Introduction Statistics for Biology and Health

Probability, distributions, sampling, interpolation, iteration, recursion and visualization. Introduction to programming using both the C and Fortran 95, languages, with applications to basic scientific problems. Covers common data types and structures, control structures, algorithms, performance measurement, and interoperability.

Prerequisite: Credit or registration for Mathematics K or C. Matrix representations and properties of matrices; linear equations, eigenvalue problems and their physical interpretation; linear least squares and elementary numerical analysis. Emphasis will be placed on physical interpretation, practical numerical algorithms and proofs of fundamental principles. Iterative solution to linear equations and eigenvalue problems; properties of symmetric and asymmetric matrices, exploitation of parsity and diagonal dominance; introduction to multivariate nonlinear equations; numerical analysis; selected applications and topics in the physical sciences.

Comprehensive introduction to computing techniques and methods applicable to many scientific disciplines and technical applications. Covers computer hardware and operating systems, systems software and tools, code development, numerical methods and math libraries, and basic visualization and data analysis tools. Prerequisite: Mathematics D or M and prior programming experience. Concentrated study in a specific area or areas of application. Areas may include computational biology, computational chemistry, computational applied mathematics, computational economics, computational physics, or computational geology.

Computational-based data sorting, data transformation, and data analysis; programming in Python and R.

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Three lecture hours and one laboratory hour per week. Parallel computing principles, architectures, and technologies. Parallel application development, performance, and scalability.

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Prepares students to formulate and develop parallel algorithms to implement effective applications for parallel computing systems. Distributed and grid computing principles and technologies. Covers common modes of grid computing for scientific applications, developing grid enabled applications, future trends in grid computing.

Scientific visualization principles, practices and technologies, including remote and collaborative visualization. Also introduces statistical analysis, data mining and feature detection. PhD students take another written advanced examination at the beginning of the third year.

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Both examinations will cover material in the areas of probability, inference, data analysis, and bioinformatics and computational biology. After beginning research on a dissertation topic, PhD students take an oral qualifying examination, consisting largely of a presentation of a thesis proposal to a faculty committee, the student's Thesis Committee. Upon completion of the dissertation, doctoral candidates present their work at a public lecture followed by an oral defense of the dissertation before the Thesis Committee. Prior to completing degrees, most students have some publications underway, including some work related to their dissertation research, possibly other methodological work done in collaboration with other members of the faculty, and often some applied papers with scientific researchers in other fields.

In general, the PhD program requires a minimum of four years of study, with five years of study being more common see Timeline for Degree Completion. For a better shopping experience, please upgrade now. Javascript is not enabled in your browser. Enabling JavaScript in your browser will allow you to experience all the features of our site.

Statistical Methods in Bioinformatics: An Introduction / Edition 2

Learn how to enable JavaScript on your browser. 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.

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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.