International peer reviewed journals

[1] M. Mesiti, M. Re, G. Valentini, Think globally and solve locally: secondary memory-based network learning for automated multi-species function prediction, GigaScience, 3:5, 2014 (link).

[2] G. Valentini, A. Paccanaro, H. Caniza, A. Romero, M. Re, An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods, Artificial Intelligence in Medicine, Volume 61, Issue 2, pages 63-78, June 2014 (link).

[3] M. Re and G. Valentini, Network-based Drug Ranking and Repositioning with respect to DrugBank Therapeutic Categories, IEEE ACM Transactions on Computational Biology and Bioinformatics, IEEE ACM Transactions on Computational Biology and Bioinformatics 10(6), pp. 1359-1371, Nov-Dec 2013 (link).

[4] M. Frasca, A. Bertoni, M. Re and G. Valentini, A neural algorithm for semi-supervised node label learning from unbalanced data. Neural Networks, 43, pp. 84-98, 2013.

[5] 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, (2012) 9(6) pp. 1812-1818, 2012 (PDF).

[6] M. Re and G. Valentini, Cancer module genes ranking using kernelized score functions. BMC Bioinformatics 2012, 13(Suppl 14):S3 doi:10.1186/1471-2105-13-S14-S3 (LINK) .

[7] M. Re, M. Mesiti and G. Valentini, Drug repositioning through pharmacological spaces integration based on networks projection. EMBnet journal, 18:30-31, 2012. ISSN:2266-6089.

[8] A. Beghini, F. Corlazzoli, L. Del Giacco, M. Re, F. Lazzaroni, M. Brioschi, G. Valentini, F. Ferrazzi, A. Ghilardi, M. Righi, M. Turrini, M. Mignardi, C. Cesana, V. Bronte, M. Nilsson, E. Morra and R. Cairoli. Regeneration-associated wnt signaling is activated in long-term reconstituting ac133bright acute myeloid leukemia cells. Neoplasia, 14:1236-1248, 2012. ISSN: 1522-8002.(LINK)

[9] N. Cesa-Bianchi, M. Re and G. Valentini, Synergy of multi-label hierarchical ensembles, data fusion, and cost-sensitive methods for gene functional inference. Machine Learning, pages 1.33, December 2011. Available at: http://www.springerlink.com/content/h53815582x353p40/ , DOI: 10.1007/s10994-011-5271-6. (PDF)

[10] M. Re and G. Valentini Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction. Journal of Machine Learning Research - Machine Learning in Systems Biology, 8:98.111, 2010. Available at: http://jmlr.csail.mit.edu/proceedings/papers/v8/. (PDF)

[11] M. Re and G. Valentini, Noise tolerance of multiple classifier systems in data integration-based gene function prediction. J. Integrative Bioinformatics, 7(3), 2010. Available at: journal.imbio.de/article.php?aid=139 . (PDF,Supp.Information)

[12] M. Re and G. Valentini, Integration of heterogeneous data sources for gene function prediction using decision templates and ensembles of learning machines. Neurocomputing, 73(7-9):1533.1537, 2010. Available at: http://www.sciencedirect.com/science/article/pii/S0925231210000032 . (preprint PDF)

[13] M. Re. Comparing early and late data fusion methods for gene expression prediction. Soft Comput., 15(8):1497.1504, March 2010. DOI: 10.1007/s00500-010-0599-6, Available at: http://www.springerlink.com/content/d389l75446t17942/ . (PDF)

[14] Re M., Pesole G., and Horner D.S. Accurate discrimination of conserved coding and non-coding regions through multiple indicators of evolutionary dynamics. BMC Bioinformatics, 10:282, 2009. (Open Access link)

[15] M. Re and G. Pavesi, Detecting conserved coding genomic regions through signal processing of nucleotide substitution patterns.
Artificial Intelligence in Medicine, 45(2-3):117.123, 2009. DOI:10.1016/j.artmed.2008.07.015 . (PDF)

[16] Avogadri R., Brioschi M., Ferrazzi F., Re M., Beghini A., and Valentini G. A stability-based algorithm to validate hierarchical clusters of genes. IJKESDP, 1(4):318.330, 2009. DOI:10.1504/IJKESDP.2009.028985 . (PDF)

[17] P. D.Onorio De Meo, D. Carrabino, N. Sanna, T. Castrignano, G. Grillo, F. Licciulli, Liuni. S., Re M., F. Mignone, and G. Pesole. A high performance grid-web service framework for the identification of .conserved sequence tags.. Future Generation Comp. Syst., 23(3):371.381, 2007. AVailable at: http://www.sciencedirect.com/science/article/pii/S0167739X06001373 . (PDF, CSTGrid)

[18] Re M., Mignone F., Iacono M., Grillo G., Liuni S., and Pesole G. A new strategy to identify novel genes and gene isoforms: Analysis of human chromosomes 15, 21 and 22. Gene, (365):35 . 40, 2006. Available at: http://dx.doi.org/10.1016/j.gene.2005.09.041 . (PDF)

Books editing

[19] O. Okun, G. Valentini, and M. Re, editors. Ensembles in Machine Learning Applications, volume 373 of Studies in Computational Intelligence. Springer-Verlag Berlin Heidelberg, 2011. ISBN: 978-3-642-22909-1. (LINK)

[20] O. Okun, M. Re, and G. Valentini, editors. Ensembles in Machine Learning Applications. Proceedings of the the Third Workshop on Supervised and Unsupervised Ensemble Methods and Their Applications (SUEMA), European Conference on Machine Learning, Barcelona, Spain. 2010. (PDF)

Review papers in international peer reviewed books/jourals

[21] G. Valentini and M. Re. Ensemble methods: A review. In: Advances in Machine Learning and Data Mining for Astronomy, Chapman & Hall Data Mining and Knowledge Discovery Series, Chap. 26, pp. 563-594, 2012. ISBN: 9781439841730. (link)

International conferences proceedings and peer reviewed book chapters

[22] G. Valentini, S. Kohler, M. Re, M. Notaro, P.N. Robinson, Prediction of human gene-phenotype associations by exploiting the hierarchical structure of the Human Phenotype Ontology, 3rd International Work-Conference on Bioinformatics and Biomedical Engineering - IWBBIO 2015, Granada, Spain Lecture Notes in Bioinformatics, Springer Berlin/Heidelberg. Vol. 9043, pp. 66-77, 2015.(link)

[23] P.N. Robinson, M. Frasca, S. Kohler, M. Notaro, M. Re, G. Valentini. A hierarchical ensemble method for dag-structured taxonomies. in Multiple Classifier Systems: 12th international workshop MCS 2015, Gunzburg, Germany, june 29 - july 1, 2015. Proceedings, vol. 9132 of Lecture Notes in COmputer Science, pages 15-26, Springer Berlin, 2015. ISBN: 978-3-319-20247-1. (link)

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

[25] M. Re, M.Mesiti, G. Valentini, On the Automated Function Prediction of Big Multi-Species Networks, Network Biology SIG 2014 - ISMB 2014, Boston, USA

[26] G. Valentini, A. Paccanaro, H. C. Vierci, A. E. Romero, M. Re, Network integration boosts disease gene prioritization, Network Biology SIG 2013 - ISMB 2013, Berlin

[27] M.Mesiti, M. Re, G. Valentini, Scalable Network-based Learning Methods for Automated Function Prediction based on the Neo4j Graph-database, Automated Function Prediction SIG 2013 - ISMB 2013, Berlin

[28] M. Re, M.Mesiti, G. Valentini, Comparison of early and late omics data integration for cancer modules gene ranking , NETTAB 2012 Workshop on Integrated Bio-Search, Como 14-16 November, 2012.

[29] M. Re and G. Valentini, Random walking on functional interaction networks to rank genes involved in cancer, 2nd Artificial Intelligence Applications in Biomedicine Workshop, in: AIAI 2012 - Artificial Intelligence Applications and Innovations, pp. 66-75, IFIP AICT Series, Springer, 2012

[30] M. Re and G. Valentini, Random Walking on Functional Interaction Networks to Rank Genes Involved in Cancer, In: L. Iliadis et al. (Eds.): AIAI 2012 Workshops, IFIP AICT 382, pp. 66.75, 2012.IFIP International Federation for Information Processing 2012, 2nd Artificial Intelligence Applications in Biomedicine Workshop (AIAB 2012). ISBN 978-3-642-33411-5. (PDF)

[31] M. Re and G. Valentini, Large Scale Ranking and Repositioning of Drugs with Respect to DrugBank Therapeutic Categories, In: L. Bleris et al. (Eds.): International Symposium on Bioinformatics Research and Applications (ISBRA 2012), Dallas, USA, Lecture Notes in Bioinformatics vol.7292, pp. 225.236, Springer, 2012. (PDF)

[32] A. Rozza, G. Lombardi, M. Re, E. Casiraghi, G. Valentini, and P. Campadelli. A novel ensemble technique for protein subcellular location prediction. In O. Okun, G. Valentini, and M. Re, editors, Ensembles in Machine Learning Applications, volume 373 of Studies in Computational Intelligence, pages 151.167. Springer-Verlag Berlin Heidelberg, 2011.

[33] A. Bertoni, M. Re, F. Sacca, and G. Valentini. Identification of promoter regions in genomic sequences by 1-dimensional constraint clustering. In B. Apolloni, S. Bassis, A. Esposito, and C.F. Morabito, editors, Neural Nets WIRN11 - Proceedings of the 21st Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, 2011, Frontiers in Artificial Intelligence and Applications, pages 162.169. IOS Press, 2011.

[33] M. Re and G. Valentini. Genes prioritization with respect to cancer gene modules using functional linkage network data. In R. Bellazzi and P. Romano, editors, 11th International Workshop, NETTAB 2011, Network Tools and Application in Biology, Pavia, Italy, October 12-14, 2011, Proceedings, pages 124.125, 2011.

[34] M. Re and G. Valentini. An experimental comparison of hierarchical bayes and true path rule ensembles for protein function prediction. In N. El Gayar, J. Kittler, and F. Roli, editors, Multiple Classifier Systems, 9th International Workshop, MCS 2010, Cairo, Egypt, April 7-9, 2010. Proceedings, volume 5997 of Lecture Notes in Computer Science, pages 294.303, 2010. (PDF)

[35] N. Cesa-Bianchi, M. Re, and G. Valentini. Functional inference in funcat through the combination of hierarchical ensembles with data fusion methods. In M. Zhang, G. Tsoumakas, and Z. Zhou, editors, ICML/COLT Workshop on learning from Multi-Label Data MLD'10 Working Notes, Jun 25, Haifa, Israel, pages 13.20, 2010. (Working Notes MLD'10)

[36] G. Valentini and M. Re. Weighted true path rule: a multilabel hierarchical algorithm for gene function prediction. In G. Tsoumakas, M. Zhang, and Z. Zhou, editors, MLD-ECML 2009, 1st International Workshop on learning from Multi-Label Data, Sept 7, Bled, Slovenia, pages 132.145, 2009. (MLD'09 proceedings)

[37] M. Re and G. Valentini. Simple ensemble methods are competitive with state-of-the-art data integration methods for gene function prediction. In S. Dzeroski, P. Geurts, and J. Rousu, editors, Machine Learning in Systems Biology, Proceedings of the Third international workshop, Sept 5-6, Ljubljana, Slovenia, pages 95.104, 2009.

[38] M. Re and G. Valentini. Prediction of gene function using ensembles of svms and heterogeneous data sources. In O. Okun and G. Valentini, editors, Applications of Supervised and Unsupervised Ensemble Methods, volume 245 of Studies in Computational Intelligence, pages 79.91. Springer, 2009.

[39] M. Re and G. Valentini. Predicting gene expression from heterogeneous data. In Computational Intelligence Methods for Bioinformatics and Biostatistics - 6th International Meeting Proceedings, CIBB 2009, Genoa, Italy, October 15-17, 2009, 2009.

[40] M. Re and G. Valentini. Ensemble based data fusion for gene function prediction. In J.A. Benediktsson, J. Kittler, and F. Roli, editors, Multiple Classier Systems, 8th International Workshop, MCS 2009, Reykjavik, Iceland, June 10-12, 2009. Proceedings, volume 5519 of Lecture Notes in Computer Science, pages 448.457. Springer, 2009. (PDF)

[41] M. Re and G. Valentini. Comparing early and late data fusion methods for gene function prediction. In B. Apolloni, S. Bassis, and F.C. Morabito, editors, Neural Nets WIRN09 - Proceedings of the 19th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, May 28-30 2009, Frontiers in Artificial Intelligence and Applications, pages 197.207. IOS Press, 2009.

[42] M. Re and G. Pavesi. Signal processing in comparative genomics. In F. Masulli, S. Mitra, and G. Pasi, editors, Applications of Fuzzy Sets Theory, 7th International Workshop on Fuzzy Logic and Applications, WILF 2007, Camogli, Italy, July 7-10, 2007, Proceedings, volume 4578 of Lecture Notes in Computer Science, pages 544.550. Springer, 2007.

Domestic conferences proceedings

[43] A. Rozza, G. Lombardi, M. Re, E. Casiraghi, G. Valentini, and P. Campadelli. A novel ensemble approach for the subcellular localization of proteins. In BITS 2011, Bioinformatics Italian Society Annual Meeting, Pisa, Italy, 2011. Proceedings, 2011.

[44] D. Malchiodi, M. Re, and G. Valentini. Uso di mathematica per la classificazione di dati di qualit variabile. In Mathematica Italia User Group Meeting - Atti del Convegno 2010. Adalta, 2010.

[45] M. Re and G. Valentini. Data fusion based gene function prediction using ensemble methods. In BITS 2009, Bioinformatics Italian Society Annual Meeting, Genova, Italy, 2009. Proceedings, 2009.

[46] M. Re, C. Nasi, G. Pesole, and D.S. Horner. Efficient detection of conserved coding regions through a comparative genomic approach. In BITS 2007, Bioinformatics Italian Society Annual Meeting, Napoli, Italy, 2007. Proceedings, 2007.

[47] V. Piccolo, M. Re, G. Pesole, and S.D. Horner. Towards an integrated pipeline for the in-silico prediction of plant micrornas and their precursors. In Proceedings Nono Congresso annuale FISV (Federazione Italiana Scienze della Vita), Riva del Garda, 2007.

[48] M. Re, S.D. Horner, C. Nasi, and G. Pesole. Improving the capacity of the cstminer algorithm to correctly classify conserved sequences. In Ottavo Congresso annuale FISV (Federazione Italiana Scienze della Vita), Riva del Garda, 2006.

[49] F. Mignone, M. Re, D.S. Horner, and G. Pesole. A new strategy to identify novel genes and genes isoforms: whole genome comparison of human and mouse. In BITS 2006, Bioinformatics Italian Society Annual Meeting, Bologna, Italy, 2006. Proceedings, 2006.

[50] D.S. Horner, M. Re, C. Nasi, and G. Pesole. Improving the cstminer algorithm to correctly classify conserved sequences. In BITS 2006, Bioinformatics Italian Society Annual Meeting, Bologna, Italy, 2006. Proceedings, 2006.

[51] M. Re, M. Iacono, F. Mignone, T. Castrignano, S. Liuni, G. Grillo, F. Licciulli, D.S. Horner, and G. Pesole. Identification of novel genes and genes isoforms in the human genome using cst miner, a novel algorithm for the differentiazion of coding and non-coding conserved sequence tags. In BITS 2005, Bioinformatics Italian Society Annual Meeting, Milan, Italy, 2005. Proceedings, 2005.