Current students

Machine Learning techniques can be crucial to investigate the complexity of cancer and provide answers to unsolved biological and clinical issues, since they allow a more comprehensive analysis of the increasing amount of omics data, becoming available thanks to Next Generation Sequencing (NGS) technologies.

The purpose of this PhD research activity is to explore and apply Machine Learning models to NGS mutational, transcriptional and copy number data of patients suffering from different types of cancer, as to provide clinically relevant stratifications of such patients as well as identify genes, mutations and variants with therapeutic and prognostic value or meaningfully associated with genetic risk and drug sensitivity.

To this aims, both the implementation and careful evaluation of suitable and fully legit Machine Learning workflows are key aspects of the research activity. Furthermore, to obtain noteworthy results in the field of bioinformatics and computational oncogenomics, it is also decisive to take particular care of the analyses needed to validate the achieved results from a clinical and biological perspective.