470-6406/02 – Advanced Statistics for Bioinformatics (PSB)

Gurantor departmentDepartment of Applied MathematicsCredits10
Subject guarantorprof. Ing. Radim Briš, CSc.Subject version guarantorprof. Ing. Radim Briš, CSc.
Study levelpostgraduateRequirementChoice-compulsory type B
YearSemesterwinter + summer
Study languageEnglish
Year of introduction2019/2020Year of cancellation
Intended for the facultiesFEIIntended for study typesDoctoral
Instruction secured by
LoginNameTuitorTeacher giving lectures
BRI10 prof. Ing. Radim Briš, CSc.
Extent of instruction for forms of study
Form of studyWay of compl.Extent
Full-time Examination 28+0
Part-time Examination 28+0

Subject aims expressed by acquired skills and competences

The objective of the course is to develop advanced knowledge of statistical tools and procedures, understanding of the advanced theory on which the procedures are based, and facility in the application of statistical tools to enable the student to incorporate sound statistical methodology into his or her own research problems with complex data.

Teaching methods

Individual consultations
Project work
Other activities


The course will emphasize methods of applied statistics and data analysis. Theoretical considerations will be included to the extent that knowledge of theory is necessary for a sound understanding of methods and contributes to the development of data analysis skills and the ability to interpret results of statistical analysis. Topics are included in the syllabus below.

Compulsory literature:

• BRIŠ, Radim. Probability and Statistics for Engineers. Ostrava, 2011. Available at: http://homel.vsb.cz/~bri10/Teaching/Prob%20&%20Stat.pdf • JOHNSON, James L. Probability and statistics for computer science. Hoboken, NJ: Wiley Interscience, 2008. ISBN 978-0470383421. • VAN BELLE, Gerald a Lloyd FISHER. Biostatistics: a methodology for the health sciences. 2nd ed. Hoboken, NJ: John Wiley, 2004. ISBN 0471031852.

Recommended literature:

• HASTIE, Trevor, Robert TIBSHIRANI a J. H FRIEDMAN. The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York, NY: Springer, 2009. ISBN 9780387848570. • JAMES, Gareth, Daniela WITTEN, Trevor HASTIE a Robert TIBSHIRANI. An introduction to statistical learning: with applications in R. New York: Springer, [2013]. Springer texts in statistics, 103. ISBN 978-1-4614-7138-7. • MOORE, Dirk F. Applied survival analysis using R. New York, NY: Springer Science+Business Media, 2016. ISBN 978-3319312439. • TUTZ, Gerhard. Regression for categorical data. New York: Cambridge University Press, 2012. ISBN 9781107009653. • HOSMER, David W a Stanley LEMESHOW. Applied logistic regression. 2nd ed. New York: Wiley, 2000. ISBN 978-0471-35632-8. • MÜLLER, Peter, Fernando Andres QUINTANA, Alejandro JARA a Tim HANSON. Bayesian Nonparametric Data Analysis. Springer, 2015. ISBN 978-3319189673.

Way of continuous check of knowledge in the course of semester

Continuous monitoring of study activities and assigned tasks during regular consultations. If some publication activity will be a part of the student's tasks, the relevant article would be presented in the course.



Other requirements

The student prepares and presents the work on a given topic.


Subject has no prerequisities.


Subject has no co-requisities.

Subject syllabus:

• Biostatistical Design of Medical Study (Various Types of Studies, Steps Necessary to Perform a Study, Ethics, Data Collection) • Software Tools for Statistical Computing • Exploratory Data Analysis (Types of Variables, Summarization and Visualization of Distributions) • Rudiments of Probability Theory (Working with Probability, Medical Tests and Bayes Theorem, Random Variables and Probability Distribution, Characteristics of Random Variable – Expected Value, Dispersion, …) • Discrete and Continuous Data Models • Population and Sample, Sampling Distribution • Theory of Estimation (Point and Interval Estimation, Maximum Likelihood Estimation Method, Bayesian Inference) • Hypothesis Testing (Basic of Hypothesis Testing, Type I and Type II Error, p-value, One- and Two-Sample Parametric Tests, Paired Tests, Sample Size Determination) • One-Way Analysis of Variance (ANOVA, Validity of ANOVA Models, Kruskal-Wallis test, Multiple Comparisons) • Linear Regression Models with One Predictor Variable • Linear Regression Models with Multiple Predictor Variables • Logistic Regression • Basics of Survival Analysis (Kaplan-Meier Estimate of the Survival Curve, Log-Rank Test, Cox Proportional Hazard Regression Model) • Stochastic processes (Markov chains, Markov models)

Conditions for subject completion

Full-time form (validity from: 2019/2020 Winter semester)
Task nameType of taskMax. number of points
(act. for subtasks)
Min. number of points
Examination Examination  
Mandatory attendence parzicipation:

Show history

Occurrence in study plans

Academic yearProgrammeField of studySpec.ZaměřeníFormStudy language Tut. centreYearWSType of duty
2021/2022 (P0588D140004) Bioinformatics and Computational Biology P English Ostrava Choice-compulsory type B study plan
2021/2022 (P0588D140004) Bioinformatics and Computational Biology K English Ostrava Choice-compulsory type B study plan
2020/2021 (P0588D140004) Bioinformatics and Computational Biology P English Ostrava Choice-compulsory type B study plan
2020/2021 (P0588D140004) Bioinformatics and Computational Biology K English Ostrava Choice-compulsory type B study plan

Occurrence in special blocks

Block nameAcademic yearForm of studyStudy language YearWSType of blockBlock owner