Bi7440 Scientific computing in biology and biomedicine

Faculty of Science
Spring 2014
Extent and Intensity
2/0. 2 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Vlad Popovici, PhD (lecturer)
Mgr. et Mgr. Jiří Kalina, Ph.D. (lecturer)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: doc. Ing. Vlad Popovici, PhD
Supplier department: RECETOX – Faculty of Science
Timetable
Wed 16:00–17:50 F01B1/709
Prerequisites
Basics of linear algebra, Matlab and R programming
Course Enrolment Limitations
The course is offered to students of any study field.
Abstract
At the end of the course, students should be able to:
-Understand the basics of numerical methods for linear algebra
-Know and have experience in applying methods in computational statistics
-Gain knowledge and experience of computer-intensive methods for data analysis
-Know how to use parallel computation tools
-Apply the theory in practice for solving problems in biological data analysis, using Matlab and R
Key topics
  • Introduction: data representation; approximations and errors; computing platforms: from desktop to cloud computing
  • Systems of linear equations: triangular systems; Gauss elimination; norms and conditioning.
  • Linear least squares: normal equations; orthogonalizations
  • Eigendecompositions and singular values: eigenvalues, eigenvectors; singular value decomposition
  • Optimization: general topics; one-dimensional; multidimensional Monte Carlo methods: random numbers; simulation, sampling and non-parametric statistics
  • Bootstrapping and resampling: bootstrap as an analytical tool; confidence intervals from bootstrapping
  • Smoothing and local regression techniques: linear smoothing; smoothing and bootstrapping
  • Parallel computing: levels of parallelization; platforms for computational biology; applications in computational biology
Study resources and literature
    recommended literature
  • HŘEBÍČEK, Jiří; Miroslav KUBÁSEK; Lukáš KOHÚT; Luděk MATYSKA; Lucia TOKÁROVÁ and Jaroslav URBÁNEK. Vědecké výpočty v matematické biologii (Scientific computing in mathematical biology). Brno: Akademické nakladatelství CERM, 2012, 117 pp. ISBN 978-80-7204-781-9. info
  • KEPNER, Jeremy. Parallel MATLAB for Multicore and Multinode Computers. 1st ed. SIAM-Society for Industrial and Applied Mathematics, 2009. ISBN 978-0-89871-673-3. info
  • Handbook of computational statistics : concepts and methods. Edited by James E. Gentle - Wolfgang Härdle - Yuichi Mori. Berlin: Springer, 2004, xii, 1070. ISBN 3540404643. info
  • GANDER, Walter and Jiří HŘEBÍČEK. Solving Problems in Scientific Computing Using Maple and MATLAB. čtvrté. Heidelberg: Springer, 2004, 476 pp. Mathematics. ISBN 3-540-21127-6. URL info
  • HEATH, Michael T. Scientific Computing. An introductory survey. 2nd. The McGraw-Hill Companies, Inc., 2002. ISBN 0-07-239910-4. info
Approaches, practices, and methods used in teaching
Lectures, homeworks and practical exercises
Method of verifying learning outcomes and course completion requirements
Weekly lectures complemented by practical exercises and short homeworks. Written and practical exam.
Language of instruction
Czech
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2008 - for the purpose of the accreditation, Spring 2011 - only for the accreditation, Autumn 2005, Autumn 2006, Spring 2008, Spring 2009, Spring 2010, Spring 2011, Spring 2012, spring 2012 - acreditation, Spring 2013.
  • Enrolment Statistics (recent)
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