Marco Frasca

Assistant Professor

PhD: 2011; PhD in Computer Science; University of Milan

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

Main interests: Neural Networks, Hopfield Networks, Compression of Neural Networks

Marco Frasca is assistant professor at the Department of Computer Science, University of Milan, Italy. He is a member of the AnacletoLab, whose research activities regard the field of Machine Learning applied in Biology and Medicine, with numerous collaborations with international research groups, including the Institute for Medical and Human Genetics Charite of Berlin, the Jackson Laboratory for Genomic Medicine of Farmington, USA, and the Department of Computer Science of the Royal Holloway University of London. He got his PhD in Computer Science in 2011 from the University of Milan, and his post-doc from the Departments of Biosciences and Computer Science of the same university. He has been invited research visitor at several universities, including the Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, the Institute of Molecular Biology of the Johannes Gutenberg University of Mainz. His research activity mainly focused on the design and analysis of new machine learning methods, with applications in bioinformatics and in computational biology and medicine. He contributed to consolidate the application of Hopfield networks to classification and ranking problems with the development of nonel single- and multi-task parametric Hopfield models.

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.

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 and G. Valentini. Multitask Hopfield Networks. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, , Wurzburg (Germany), 2019.

M. Frasca and G. Valentini. COSNet: An R package for label prediction in unbalanced biological networks. Neurocomputing, Elsevier 237, 2017.

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

M. Frasca, A. Bertoni and G. Valentini. UNIPred: Unbalance-aware Network Integration and Prediction of protein functions. Journal of Computational Biology, Mary Ann Liebert, Inc. 140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA 22(12), 2015.

M. Frasca. Automated gene function prediction through gene multifunctionality in biological networks. Neurocomputing, Elsevier 162, 2015.

M. Frasca, A. Bertoni, M. Re and G. Valentini. A neural network algorithm for semi-supervised node label learning from unbalanced data. Neural Networks, Elsevier 43, 2013.

M. Frasca, A. Bertoni and G. Valentini. An unbalance-aware network integration method for gene function prediction. MLSB 2013-Machine Learning for Systems Biology, 2013.

A. Bertoni, M. Frasca and G. Valentini. COSNet: a cost sensitive neural network for semi-supervised learning in graphs. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, 2011.

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.

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.

M. Frasca, S. Bassis and G. Valentini. Learning node labels with multi-category hopfield networks. Neural Computing and Applications, Springer 27(6), 2016.

M. Frasca, A. Bertoni and G. Valentini. A cost-sensitive neural algorithm to predict gene functions using large biological networks. Network Biology SIG: On the Analysis and Visualization of Networks in Biology, 2011.

M. Muselli, A. Bertoni, M. Frasca, A. Beghini, F. Ruffino and G. Valentini. A mathematical model for the validation of gene selection methods. IEEE/ACM transactions on computational biology and bioinformatics, IEEE 8(5), 2010.

M. Frasca and D. Malchiodi. Exploiting negative sample selection for prioritizing candidate disease genes. Genomics and Computational Biology 3(3), 2017.

M. Frasca. Gene2DisCo: gene to disease using disease commonalities. Artificial intelligence in medicine, Elsevier 82, 2017.

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. Frasca and N. Cesa-Bianchi. Multitask protein function prediction through task dissimilarity. IEEE/ACM transactions on computational biology and bioinformatics, IEEE, 2017.

S. Vascon, M. Frasca, R. Tripodi, G. Valentini and M. Pelillo. Protein function prediction as a graph-transduction game. Pattern Recognition Letters, Elsevier, 2018.

M. Frasca, A. Bertoni and G. Valentini. Regularized network-based algorithm for predicting gene functions with high-imbalanced data. EMBnet. journal 18(A), 2012.

M. Frasca and D. Malchiodi. Selection of Negatives in Hopfield Networks. International Workshop on Dynamics of Multi-Level Systems (DYMULT), 2015.

E. Casiraghi, V. Huber, M. Frasca, M. Cossa, M. Tozzi, L. Rivoltini, B. Leone, A. Villa and B. Vergani. A novel computational method for automatic segmentation, quantification and comparative analysis of immunohistochemically labeled tissue sections. BMC Bioinformatics, BioMed Central 19(10), 2018.

P. Boldi, M. Frasca and D. Malchiodi. Evaluating the impact of topological protein features on the negative examples selection. BMC Bioinformatics, BioMed Central 19(14), 2018.

M. Frasca, F. Lipreri and D. Malchiodi. Analysis of informative features for negative selection in protein function prediction. International Conference on Bioinformatics and Biomedical Engineering, 2017.

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.

S. Vascon, M. Frasca, R. Tripodi, G. Valentini and M. Pelillo. Protein function prediction as a graph-transduction game. Pattern Recognition Letters, Elsevier 134, 2020.

M. Frasca, M. Sepehri, A. Petrini, G. Grossi and G. Valentini. Committee-Based Active Learning to Select Negative Examples for Predicting Protein Functions. International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, 2018.