MARCO FRASCA
PUBLICATIONS
Assistant Professor 
Computer Science Department
UniversitÓ degli studi di Milano
Room T304, III floor
Via Comelico 39 
Tel: (+39) 02 503 16295/16321
E-mail:  frasca [at] di [dot] unimi [dot] it
(replace - at - with @ )



Teaching

Publications

Software

International peer-reviewed Journals

  1. 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.
  2. 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.
  3. M. Frasca and N. Cesa Bianchi. Multitask protein function prediction through task dissimilarity. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2017. doi:10.1109/TCBB.2017.2684127.
  4. M. Frasca and D. Malchiodi. Exploiting negative sample selection for prioritizing candidate disease genes. Genomics and Computational BiologyGCB, Vol. 3, No. 3 (2017): e47.
  5. 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
  6. M. Frasca, G.Valentini. COSNet: an R package for label prediction in unbalanced biological networks. Neurocomputing 237: 397-400. doi:http://dx.doi.org/10.1016/j.neucom.2015.11.096
  7. M. Frasca, A. Bertoni, G. Valentini. UNIPred: Unbalance-aware Network Integration and Prediction of protein functions,Journal of Computational Biology 22(12) 1057–1074Avalilable on-line at http://online.liebertpub.com/doi/abs/10.1089/cmb.2014.0110  2015.
  8. 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, Pages 1677-1692. DOI: 10.1007/s00521-015-1965-1 2015 (in press).
  9. M. Frasca. Automated Gene Function Prediction through Gene Multifunctionality in Biological Networks. Neurocomputing 162(0) (2015) 48 – 56.
  10. 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.
  11. 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.
  12. 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.
  13. 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.

Proceedings of International and National peer-reviewed Conferences and book chapters

  1. M. Frasca, M. Sepehri, A. Petrini, G. Grossi and G. Valentini. Committee-based Active Learning to Select Negative Examples for Predicting Protein FunctionsComputational  ntelligence methods for Bioinformatics and Biostatistics (CIBB2018), 6-8 September, Caparica, Portugal.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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
  7. 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
  8. 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.
  9. 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.
  10. P. Robinson, M. Frasca, S. K÷hler, 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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
  15. 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.
  16. 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.
  17. A. Bertoni, M. Frasca, G.Valentini. An efficient supervised method to integrate multiple biological networks, BITS 2011, Bioinformatics Italian Society Meeting Pisa, 2011.
  18. 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.



Publications without proceedings



  • M. Frasca: Selection of Negatives in Hopfield Networks. International Workshop on Dynamics of Multi-Level Systems (DYMULT) 2015, Max Planck Institute for the Physics of Complex Systems, Dresden. Poster contribution.