Marco Mesiti

Associate Professor

PhD: 2003; PhD in Computer Science; University of Genova

MSc: 1998; M. Sc. in Computer Science; University of Genova

Email: marco (dot) mesiti (at) unimi (dot) it

Main interests: data management, big data visualization, data integration

Marco Mesiti is associate professor at the Department of Computer Science of the University of Milan, Italy. He received a Laurea degree (cum laude) and a PhD in Computer Science at the University of Genova, Italy. He has been visiting researcher at the applied research center of Telcordia Technologies, Morristown, New Jersey. His research activity has been carried out in the database area, and has focused on semistructured and XML data handling, with specific interests in access control models, issues concerning data integration, approximate retrieval, query processing and schema evolution. Nowadays he is working on approaches for facing the heterogeneity of data in different contexts like IoT data management, credit worthiness and bioinformatics.

Publications

A. Petrini, M. Schubach, M. Re, M. Frasca, M. Mesiti, G. Grossi, T. Castrignanò, P. Robinson and G. Valentini. Parameters tuning boosts HyperSMURF predictions of rare deleterious non-coding genetic variants. PeerJ Preprints, PeerJ Inc. San Francisco, USA 5, 2017.

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.

G. Valentini, G. Armano, M. Frasca, J. Lin, M. Mesiti and M. Re. RANKS: a flexible tool for node label ranking and classification in biological networks. Bioinformatics, Oxford University Press 32(18), 2016.

M. Re, M. Mesiti and G. Valentini. An automated pipeline for multi-species protein function prediction from the UniProt Knowledgebase. Automated Function Prediction SIG-ISMB, 2014.

M. Re, M. Mesiti and G. Valentini. A fast ranking algorithm for predicting gene functions in biomolecular networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), IEEE Computer Society Press 9(6), 2012.

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.

J. Lin, M. Mesiti, M. Re and G. Valentini. Within network learning on big graphs using secondary memory-based random walk kernels. International Workshop on Complex Networks and their Applications, 2016.

M. Mesiti, M. Re and G. Valentini. Think globally and solve locally: secondary memory-based network learning for automated multi-species function prediction. GigaScience, BioMed Central 3(1), 2014.

M. Mesiti, M. Re and G. Valentini. Scalable network-based learning methods for automated function prediction based on the neo4j graph-database. Proceedings of the Automated Function Prediction SIG 2013—ISMB Special Interest Group Meeting, 2013.

N. Zhou, Y. Jiang, [...], M. Frasca, M. Notaro, G. Grossi, A. Petrini, M. Re, G. Valentini, M. Mesiti and others. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens. Genome Biology 20(1), 2019.

M. Notaro, M. Schubach, M. Frasca, M. Mesiti, P. Robinson and G. Valentini. Ensembling descendant term classifiers to improve gene-abnormal phenotype predictions. International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, 2017.

M. Re, M. Mesiti and G. Valentini. Comparison of early and late omics data integration for cancer modules gene ranking. NETTAB: Integrated Bio-Search, 2012.

M. Mesiti, M. Notaro, D. Malchiodi and M. Andrade-Navarro. Disease--Genes Must Guide Data Source Integration in the Gene Prioritization Process. Computational Intelligence Methods for Bioinformatics and Biostatistics: 14th International Meeting, CIBB 2017, Cagliari, Italy, September 7-9, 2017, Revised Selected Papers 10834, 2019.

M. Re, M. Mesiti and G. Valentini. Drug repositioning through pharmacological spaces integration based on networks projections. EMBnet. journal 18(A), 2012.

H. Vierci, A. Romero, S. Heron, H. Yang, M. Frasca, M. Mesiti, G. Valentini and A. Paccanaro. GOssTo \& GOssToWeb: user-friendly tools for calculating semantic simi-larities on the Gene Ontology. Bio-Ontologies SIG 2013-ISMB 2013, 2013.

H. Caniza, A. Romero, S. Heron, H. Yang, A. Devoto, M. Frasca, M. Mesiti, G. Valentini and A. Paccanaro. GOssTo: a stand-alone application and a web tool for calculating semantic similarities on the Gene Ontology. Bioinformatics, Oxford University Press 30(15), 2014.

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.

S. Bonfitto, F. Hachem, E. Belay, S. Valtolina and M. Mesiti. On the Bulk Ingestion of IoT Devices from Heterogeneous IoT Brokers. 2019 IEEE International Congress on Internet of Things (ICIOT), 2019.

S. Valtolina, F. Hachem, B. Barricelli, E. Belay, S. Bonfitto and M. Mesiti. Facilitating the Development of IoT Applications in Smart City Platforms. International Symposium on End User Development, 2019.

M. Frasca, J. Fontaine, G. Valentini, M. Mesiti, M. Notaro, D. Malchiodi and M. Andrade-Navarro. Disease--Genes must Guide Data Source Integration in the Gene Prioritization Process. International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, 2017.

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.

A. Petrini, M. Mesiti, M. Schubach, M. Frasca, D. Danis, M. Re, G. Grossi, L. Cappelletti, T. Castrignanò, P. Robinson and Others. parSMURF, a high-performance computing tool for the genome-wide detection of pathogenic variants. GigaScience, Oxford University Press 9(5), 2020.