Elements of Bioinformatics. 

Course aims: This course provides the basic elements of the statistical analysis and the mathematical representation of data. At the end of the course the student will be able to: - perform the basic statistical analysis of data samples; - handle vectorial and matrix representation of data; - perform the diagonalization of a square matrix. The course is highly recommended as an introductory course to acquire the necessary expertise to proceed in the curriculum.

Course contents: Basics on probability: definitions, probability of the union of events, probability of the conjunction of events, conditional probability, Bayes rule. Probability distributions: mean, median, mode, variance. Binomial distribution. Poisson distribution. Normal distribution. Maxwell-Boltzmann distribution. Extreme value distribution. Statistical analysis of data samples: sample mean, sample variance, biased and unbiased estimates. Introduction to the statistical tests for assessing the significance of a difference between sets of data. Z-test and t-test for the difference between two mean values. ANOVA test for the difference among many mean values. Chi-square test for the difference between two distributions. Chi-square test and F-test for the difference between two contingency tables. Statistical analysis of data pairs: covariance and correlation. Pearson's, Spearman's and Matthews correlation coefficients. Vector and matrix representation of data. Basic operations:  matrix sum and product. Determinants. Inverse matrix. Diagonalization of square matrices: eigenvectors and eigenvalues.

Readings/Bibliography: on line, selected papers and books.

Teaching methods: Lectures,  seminars.

Assessment methods: The test of assessment will be written based on a series of questions to test the knowledge of the student, followed by an oral section.

Teaching tools: Video beam, PC, overhead projector, laboratory activity