Ph.D XXXIX

Supervisor: Alberto Policriti

Phone: 

Room: Rizzi L2-12-BC

Mail: nascimben.francesco@spes.uniud.it

Research Project

Development and implementation of algorithms and data structures for storing and working on large graphs derived from genetic/biological data

As an increasingly widespread means of data representation, gaining further insight on graphs is as crucial now as it has ever been: due to their flexibility and expressiveness, they are commonly used to encode a wide variety of data related to different knowledge domains.

In particular, graphs can also be used to represent and manipulate knowledge stemming from biology and genetics. To name a few examples, de Bruijn graphs are often employed to depict biological sequences; graph-based approaches have been developed to efficiently compute genomic distances; directed graphs can be used to represent expression imbalance between different alleles of the same gene within a population. The relevance of this specific sub-field of graph theory keeps growing stronger, as the amount of data and computational power available to computer scientists and biologists increases.

My research project aims to develop new techniques for efficiently dealing with the above-mentioned data: such techniques may include either the development of new classical algorithms and data structures for already established graph encodings of biological/genetic data or the definition of entirely new representations. Attention will also be paid to machine learning tools, such as neural networks, which have been proved to be extremely effective when tackling huge amounts of data.