Applications of Biostatistics – Bioinformatics
Coordinator: G. Sakellaropoulos
This topic aims at the familiarization of graduate students with the application of methodologies of Biostatistics and Bioinformatics. This is achieved through the exposition of methodologies, discussion on the appropriateness of the methodologies for the data analysis of specific research fields and use of software (SPSS, Excel, web-based applications) for solving real-world problems.
This topic consists of two parts. The first part includes methodologies of Biostatistics; methodologies of Bioinformatics are presented in the second part.
- Descriptive statistics (measures of central tendency and dispersion, data presentation in tables and graphs)
- Elements of probability theory (Conditional probability, sensitivity & specificity of a test, Bayes rule, predictive value, probability distributions)
- Statistical sampling (standard error of the mean, central limit theorem)
- Statistical inference (statement of null hypotheses, comparison of mean values of different samples, types of error, power of a statistical test, contingency tables and χ2 test)
- Linear regression & correlation (conceptual difference, use of linear regression for prediction, confidence interval of linear regression line, correlation coefficient)
- Biological sequence databases
- Methods for searching in Databases
- Software for the analysis of nucleotide and aminoacid sequences (finding protein topology, structure analysis of protein functional domains, protein sequence motifs, analysis of physicochemical parameters, post-translational modifications, transmembrane regions of proteins, protein structure prediction and visualization, inferring phylogenetic relationships from sequence data)
- Microarrays and analysis of microarray data