The lectures deal with computational methods and techniques for Computationl Biology and Bioinformatics, covering both programming languages for Bioinformatics and Machine Learning Methods for Computational Biology.
At the end of the course the student should acquire: - Basic knowledge of machine learning algorithms - Ability to apply Machine Learning algorithms to the analysis of complex biomolecular data - Programming skills to realize software applications in bioinformatics.
1. The R programming language. - Main data structures in R: vectors, factors, matrices, arrays, lists and environments. - Control of execution flow: blocks, conditional statements, loops. - Functions and scripts - I/O functions and operators; R data import/export - R graphics - Object-oriented programming in R - Packages and R "extensions"
2. Machine Learning and Computational Biology. - Learning from data: supervised, unsupervised and semi-supervised machine learning methods. - Some examples of Computational Biology applications of Machine Learning: - Automated functional annotation of proteins - Systems Biology approaches to disease gene prioritization and to the analysis of biological networks. - Outcome and abnormal phenotype prediction from multiple sources of omics data. - Prediction of genetic variants and mutations associated with genetic diseases and cancer.
Prerequisiti e modalità di esame
Oral discussion of the contents of the course and/or development/application of a R program to a bioinformatics problem.
Lectures and lab exercises, where each student will have a personal computer at his/her disposal.