Paolo Perlasca

Assistant Professor

PhD: 2004; PhD in Computer Science; University of Milan

MSc: 1999; M.Sc. in Computer Science; University of Milano

Email: paolo (dot) perlasca (at) unimi (dot) it

Main interests: Data security, integrity and protection, Data representation, analysis and modeling

Paolo Perlasca is assistant professor at the Department of Computer Science of the University of Milan (Italy). His research was carried out in the data management area and was focused on data security, integrity and protection aspects with specific interests in data integration and propagation from multiple and heterogeneous sources, and in analysis and specification of access control models and security policies. His research has concerned, in addition to the security aspects, aspects of data representation and modeling for specfic areas. He has worked also on the intellectual property management and protection with particular interest in digital rights management systems license specification and management. In recent years, his interest has focused on the following research topics: development of efficient systems for analysis, merging and displayng large heterogeneous biomolecular networks.

Publications

J. Gliozzo, P. Perlasca, M. Mesiti, E. Casiraghi, V. Vallacchi, E. Vergani, M. Frasca, G. Grossi, A. Petrini, M. Re, A. Paccanaro and G. Valentini. Network modeling of patients’ biomolecular profiles for clinical phenotype/outcome prediction. Scientific Reports, Nature Publishing, 2020.

P. Perlasca, M. Frasca, C. Ba, M. Notaro, A. Petrini, E. Casiraghi, G. Grossi, J. Gliozzo, G. Valentini and M. Mesiti. UNIPred-Web: a web tool for the integration and visualization of biomolecular networks for protein function prediction. BMC Bioinformatics, BioMed Central 20(1), 2019.

P. Perlasca, G. Valentini, M. Frasca and M. Mesiti. Multi-species protein function prediction: towards web-based visual analytics.. iiWAS, 2016.

M. Frasca, G. Grossi, J. Gliozzo, M. Mesiti, M. Notaro, P. Perlasca, A. Petrini and G. Valentini. A GPU-based algorithm for fast node label learning in large and unbalanced biomolecular networks. BMC Bioinformatics, BioMed Central 19(10), 2018.

M. Mesiti, E. Jimènez-Ruiz, I. Sanz, R. Berlanga-Llavori, P. Perlasca, G. Valentini and D. Manset. XML-based approaches for the integration of heterogeneous bio-molecular data. BMC Bioinformatics, BioMed Central 10(12), 2009.

C. Ba, E. Casiraghi, M. Frasca, J. Gliozzo, G. Grossi, M. Mesiti, M. Notaro, P. Perlasca, A. Petrini, M. Re and Others. A Graphical Tool for the Exploration and Visual Analysis of Biomolecular Networks. International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, 2018.

J. Gliozzo, P. Perlasca, M. Mesiti, E. Casiraghi, V. Vallacchi, E. Vergani, M. Frasca, G. Grossi, A. Petrini, M. Re and Others. Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction. Scientific reports, Nature Publishing Group 10(1), 2020.