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.
Computational Genomics
Learning Goals
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
ECTS
Semester
Lecturers
Giulio Caravagna