Research Theme:  Community detection in biomolecular networks
PhD Program in Bioengineering and Robotics

Description: Inferring groups of interacting proteins or genes with biologically significance is a main trend of the current bioinformatics research, as this task can help in revealing the functionality and the relevance of specific macromolecular assemblies or even in discovering possible macromolecules affecting a specific biological process. Protein and gene interaction networks can be modeled similarly to social interaction networks, so that these biologically significant groups correspond to communities. Reliable algorithms able to discover such communities may increase knowledge about biological functions at a molecular level, and may support drug discovery and enhance disease treatments even in earlier stages. This project is aimed at the development of effective tools for community detection in biological networks using methods of network and graph theories, machine learning, and computational intelligence. For instance, a significant application goal, important for cancer biomarker research, is a better understanding of the role of miRNAs, a novel class of non-coding RNA able to modulate the expression of their target genes. The available algorithms, mostly based on structural information, are still not able to provide a biological enrichment of their results, that can instead be obtained from the proposed analysis.