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Assistant Professor
Computer Science Department
Universita' degli studi di Milano
Room 3007, III floor
Via Celoria 18
Tel: (+39) 02 503 16295
E-mail: frasca [at] di [dot] unimi [dot] it
(replace - at - with @ )
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- M. Notaro, M. Frasca, A. Petrini, J. Gliozzo, E. Casiraghi, P.N. Robinson and G. Valentini. HEMDAG: a family of modular and scalable hierarchical ensemble methods to improve
Gene Ontology term prediction. Bioinformatics, 2021, 1–8. In press. doi:10.1093/bioinformatics/btab485.
- Esposito A, Casiraghi E, Chiaraviglio F, Scarabelli A, Stellato E, Plensich G, Lastella G, Di Meglio L, Fusco S, Avola E, Jachetti A, Giannitto C, Malchiodi D, Frasca M, Beheshti A, Robinson PN, Valentini G, Forzenigo L, Carrafiello G. Artificial intelligence in predicting clinical outcome in COVID-19 patients from clinical, biochemical and a qualitative chest X-ray scoring system. Reports in Medical Imaging. 2021;14:27-39. https://doi.org/10.2147/RMI.S292314.
- C. Cava, M. Pisati, M. Frasca, I. Castiglioni. Identification of breast cancer subtype-specific biomarkers by integrating copy number alterations and gene expression profiles. Medicina (Lithuania), 2021, 57(3), 261.
- 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,10:3612, 2020.
- A. Petrini, M. Mesiti, M. Schubach, M. Frasca, D. Danis, M. Re, G. Grossi, L. Cappelletti, T. Castrignanò, P.N. Robinson, G. Valentini. parSMURF, a high-performance computing tool for the genome-wide detection of pathogenic variants, GigaScience, 9:5, 2020.
- P. Perlasca, M. Frasca, C.T. Ba, J. Gliozzo, M. Notaro, M. Pennacchioni, G. Valentini, M. Mesiti. Multi-resolution
visualization and analysis of biomolecular networks through
hierarchical community detection and web-based graphical tools. PLoS ONE 15(12): e0244241. 2020
- E. Casiraghi, D. Malchiodi, G. Trucco, M. Frasca, L. Cappelletti, T. Fontana, A. Esposito, E. Avola, A. Jachetti, J. Reese, A. Rizzi, P. Robinson, G.Valentini. Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments, IEEE Access vol. 8, pp. 196299-196325, 2020
- N.
Zhou, et al. The CAFA challenge reports improved protein function
prediction and new functional annotations for hundreds of genes through
experimental screens. Genome Biol 20(244), 2019. doi:10.1186/s13059-019-1835-8
- P.
Perlasca et al. UNIPred-Web: A web tool for the integration and
visualization of biomolecular networks for protein function prediction.
BMC Bioinformatics 20(422) 2019. doi:10.1186/s12859-019-2959-2
- P.
Boldi, M. Frasca, D. Malchiodi. Evaluating the impact of topological
protein features on the negative examples selection. BMC Bioinformatics
19(417), 2018. doi:10.1186/s12859-018-2385-x.
- M.
Frasca, G. Grossi, G. Gliozzo et al. A GPU-based algorithm for fast
node label learning in large and unbalanced biomolecular networks. BMC
Bioinformatics 19(353), 2018. doi:10.1186/s12859-018-2301-4
- E.
Casiraghi, V. Huber, M. Frasca et al. A novel computational method for
automatic segmentation, quantification and comparative analysis of
immunohistochemically labeled tissue sections. BMC Bioinformatics
19(357), 2018. doi:10.1186/s12859-018-2302-3
- S.
Vascon, M. Frasca, R. Tripodi, G. Valentini, M. Pelillo. Protein
Function Prediction as a Graph-Transduction Game. Pattern Recognition
Letters, 2018 (In press). doi:10.1016/j.patrec.2018.04.002
- M. Frasca. Gene2DisCo:Gene to disease using disease commonalities. Artificial Intelligence in Medicine, 82:34-46, 2017. doi:10.1016/j.artmed.2017.08.001
- M.
Frasca and N. Cesa Bianchi. Multitask protein function prediction
through task dissimilarity. IEEE/ACM Transactions on Computational
Biology and Bioinformatics, 16(5), 2017. doi:10.1109/TCBB.2017.2684127
- M.
Frasca and D. Malchiodi. Exploiting negative sample selection for
prioritizing candidate disease genes. Genomics and Computational
Biology, GCB, 3(3): e47, 2017. doi:10.18547/gcb.2017.vol3.iss3.e47
- 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 32 (18): 2872-2874 first published
online June 2, 2016. doi:10.1093/bioinformatics/btw235
- M.
Frasca, G.Valentini. COSNet: an R package for label prediction in
unbalanced biological networks. Neurocomputing 237: 397-400. doi:10.1016/j.neucom.2015.11.096
- M.
Frasca, A. Bertoni, G. Valentini. UNIPred: Unbalance-aware Network
Integration and Prediction of protein functions,Journal of
Computational Biology 22(12) 1057–1074, 2015. doi:10.1089/cmb.2014.0110
- M.
Frasca, S.Bassis, G. Valentini Learning node labels with multi-category
Hopfield networks, Neural Computing and Applications, Volume 27, Issue
6, 1 August 2016, 27(6): 1677-1692, 2015. doi:10.1007/s00521-015-1965-1
- M.
Frasca. Automated Gene Function Prediction through Gene
Multifunctionality in Biological Networks. Neurocomputing 162(0) (2015)
48 – 56. doi:10.1016/j.neucom.2015.04.007
- S.
Heron, H. C. Vierci, A. E. Romero, H. Yang, M. Frasca, G. Valentini, A.
Paccanaro. GOssTo: a user-friendly stand-alone and web tool for
calculating semantic similarities on the Gene Ontology. Bioinformatics
2014 Aug 1;30(15):2235-6. doi:10.1093/bioinformatics/btu144
- M.
Frasca, A. Bertoni, M. Re, and G. Valentini. A neural network algorithm
for semi-supervised node label learning from unbalanced data, Neural
Networks 43, pp.84-98, July 2013. doi:10.1016/j.neunet.2013.01.021
- M.
Frasca, A. Bertoni, G. Valentini. Regularized network-based algorithm
for predicting gene functions with high-imbalanced data, In:
EMBnet.journal - ISSN 2226-6089. 18 (2012),pp. 41-42.
- 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 2011, vol.8 n.5 pp.1385-1392. doi:10.1109/TCBB.2010.83
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- G. Fumagalli, D. Raimondi, R. Giancarlo, D. Malchiodi and M. Frasca. On the Choice of General Purpose Classifiers in Learned Bloom Filters: An Initial Analysis Within Basic Filters, The International Conference on Pattern Recognition Applications and Methods (ICPRAM) 2022. Accepted.
- G. Marino', G. Ghidoli, M. Frasca, D. Malchiodi. Compression strategies and space-conscious representations for deep neural networks, 2020 25th International Conference on Pattern Recognition (ICPR), 2021, pp. 9835-9842, doi: 10.1109/ICPR48806.2021.9412209.
- G. Marino', G. Ghidoli, M. Frasca, D. Malchiodi.
Reproducing the sparse Huffman Address Map compression for deep neural
networks.Third Workshop on Reproducible Research in Pattern Recognition. RRPR 2021. Lecture Notes in Computer Science, vol 12636. Springer, Cham. https://doi.org/10.1007/978-3-030-76423-4_12
- M. Frasca, M. Sepehri, A. Petrini, G. Grossi and G. Valentini. Multitask Hopfield Networks European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019, Wurzburg16 September 2019.
- M. Frasca, M. Sepehri, A. Petrini, G. Grossi and G. Valentini. Committee-based Active Learning to Select Negative Examples for Predicting Protein Functions. Computational
intelligence methods for Bioinformatics and Biostatistics (CIBB2018),
6-8 September, Caparica, Portugal.
- C.
T. Ba, E. Casiraghi, M. Frasca, J. Gliozzo, G. Grossi, M. Mesiti, M.
Notaro, P. Perlasca, A. Petrini, M. Re and G. Valentini. A
Graphical Tool for the Exploration and Visual Analysis of Biomolecular
Networks. Computational ntelligence methods for Bioinformatics and Biostatistics (CIBB2018), 6-8 September, Caparica, Portugal. A. Petrini, M. Schubach, M.
Re, M. Frasca, M. Mesiti, G. Grossi, T. Castrignano', P.N.
Robinson, G. Valentini.
Parameters tuning boosts hyperSMURF predictions of rare
deleterious non-coding genetic variants, PeerJ Preprints 5:e3185v1, 2017
presented at Methods, tools & platforms for
Personalized Medicine in the Big Data Era - NETTAB 2017,
Palermo, Italy.
- M. Frasca, J. F. 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. Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB2017). 7-9 September 2017, Cagliari, Italy.
- M. Notaro, M. Schubach, M. Frasca, M. Mesiti, P. N. Robinson and G. Valentini. Ensembling Descendant Term Classifiers to Improve Gene – Abnormal Phenotype Predictions. Computational Intelligence methods for Bioinformatics and Biostatistics (CIBB2017). 7-9 September 2017, Cagliari, Italy.
- M.
Frasca and D. Malchiodi. Analysis of Informative Features for Negative
Selection in Protein Function Prediction. International Work-Conference
on Bioinformatics and Biomedical Engineering, IWBBIO 2017. DOI: 10.1007/978-3-319-56154-7_25
- G.
Perlasca, P. Valentini, M. Frasca, and M. Mesiti. Multi-species protein
function prediction: Towards web-based visual analytics.
Proceedings of the 18th International Conference on Information
Integration and Web-based Applications and Services (iiWAS2016), 28–30
Nov, Singapore., pages 489-493. ACM 2016.
Doi: 10.1145/3011141.3011222
- M. Frasca, S.Bassis. Gene-disease Prioritization through Cost-Sensitive Graph-based Methodologies.
Lecture Notes in Computer Science (including subseries Lecture Notes in
Artificial Intelligence and Lecture Notes in
Bioinformatics). 9656 739-751. 2016. 4th International
Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO
2016; Granada; Spain; 20 - 22 April 2016.
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M.Frasca and D. Malchiodi. Selection of Negative
Examples for Node Label Prediction through Fuzzy
Clustering Techniques.
Smart Innovation, Systems and Technologies
Volume 54, 2016, Pages 67-76. International Workshop on Neural
Networks, WIRN 2015; Vietri sul Mare; Italy; 20 May
2015 through 22 May 2015.
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P. Robinson, M. Frasca, S. Köhler, M. Notaro, M. Re and G.
Valentini. A
hierarchical ensemble method
for DAG-structured taxonomies. Multiple Classifier
Systems
2015, Volume
9132 of the series Lecture
Notes in Computer Science
pp
15-26.
- M. Frasca and G. Pavesi. A
Neural Network Based Algorithm for Gene Expression Prediction from
Chromatin Structure, The International Joint Conference
on Neural Networks (IJCNN 13), Dallas,
Texas, 2013.
- H. C. Vierci, A. E. Romero, S. Heron, H. Yang, M. Frasca, M.
Mesiti, G. Valentini and A. Paccanaro GOssTo
& GOssToWeb: user-friendly tools for calculating semantic
similarities on the Gene Ontology,
Bio-Ontologies SIG 2013 ISMB, Berlin.
- M.Frasca, A. Bertoni, G. Valentini. An
unbalance-aware network integration method for gene function
prediction, MLSB
2013 - Machine Learning for Systems Biology - Berlin, 2013.
- M. Frasca, A. Bertoni, A. Sion. A
neural procedure for Gene Function Prediction, Neural
Nets and
Surroundings,Smart Innovation, Systems and Technologies. Volume
19, 2013, pp 179-188, WIRN
2012.
- A. Bertoni, M. Frasca, G. Valentini. COSNet:
a Cost Sensitive Neural Network for Semi-supervised Learning
in Graphs, In: "European Conference on
Machine Learning, ECML PKDD, 2011, Athens, Proceedings,
Part I, Lecture Notes on Artificial Intelligence, vol. 6911,
pp.219- 234, Springer.
- M. Frasca, A. Bertoni, 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, ISMB Wien, 2011.
- A. Bertoni, M. Frasca, G.Valentini. An
efficient supervised method to integrate multiple biological networks,
BITS 2011, Bioinformatics Italian Society Meeting Pisa, 2011.
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A. Bertoni, M. Frasca, G. Grossi, G. Valentini: Learning
functional linkage networks with a cost- sensitive
approach, WIRN
2010, IOS Press, pp. 52-61, 2011.
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