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.
Teaching material and methodology
Lectures and lab exercises, where each student will have a personal computer at his/her disposal.
Slides, notes and scientific papers will be available from the course web site:http://homes.di.unimi.it/valentini/MB1617.html
Oral discussion of the contents of the course and/or development/application of a R program to a bioinformatics problem.