Computational Genomics

Learning Goals

Students will learn Machine Learning methods (unsupervised mixtures, probabilistic graphical models, variational models, etc. in R and Python) can be used to analyse cancer DNA/RNA sequencing data. Students will become used to process modern with genomics and epigenomics data, from a variety of technologies and perspectives. The acquired knowledge will be immediately usable in the context of industrial jobs in a pharma company.

Program in pills

Students will focus on statistical methods for the analysis of cancer data, including DNA and RNA sequencing data from different technologies, evolutionary inferences and population genetics. At the end of the courses students will be proficient with state-of-the-art analyses carried out on modern technologies, and will be proficient to start carrying out research in the field of Data Science applied to genomics.

Area

Multidisciplinary, Ethical-Judicial-Social Knowledge and Applications

Curriculum Foundations
TAF Type

Curriculum Industry
TAF Type

Curriculum Health
TAF Type

Curriculum Economy
TAF Type

SSD

INF/01

ECTS

6

Semester

1

Lecturers

Giulio Caravagna