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Advanced Statistics for Bioinformatics

Type of study Doctoral
Language of instruction Czech
Code 470-6406/01
Abbreviation PSB
Course title Advanced Statistics for Bioinformatics
Credits 10
Coordinating department Department of Applied Mathematics
Course coordinator prof. Ing. Radim Briš, CSc.

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)

E-learning

Basic materials are available on the educator's website:
http://homel.vsb.cz/~bri10,
Teaching,
SMIP_PhD.zip

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.

Advised 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 .


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