Marco Notaro

PostDoc

PhD: 2019; PhD in Computer Science; University of Milano

MSc: 2015; M.Sc. Molecular Biotechnology and Bioinformatics; University of Milano

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

Main interests: Bioinformatics, Biological Networks, Machine Learning

Marco Notaro received a M.Sc. in Molecular Biotechnology and Bioinformatics from the University of Milan in 2015 and a Ph.D. in Computer Science from the University of Milan in 2019. His research interests touch the fields of Bioinformatics, Computational Biology, Biological Network and Machine Learning. More precisely, he focused on the analysis and construction of complex biomolecular networks and in design and implementation of output-structured learning algorithms for gene/protein function prediction. His Ph.D. paper was awarded by International Medical Informatics Association as one of the best five papers of 2017 in the field of Medical Informatics.

Publications

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.

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.

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. Notaro, M. Schubach, P. Robinson and G. Valentini. Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods. BMC Bioinformatics, BioMed Central 18(1), 2017.

P. Robinson, M. Frasca, S. Köhler, M. Notaro, M. Re and G. Valentini. A hierarchical ensemble method for dag-structured taxonomies. International Workshop on Multiple Classifier Systems, 2015.

G. Valentini, S. Köhler, M. Re, M. Notaro and P. Robinson. Prediction of human gene-phenotype associations by exploiting the hierarchical structure of the human phenotype ontology. International Conference on Bioinformatics and Biomedical Engineering, 2015.

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. 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.