| DISULFIND: a disulfide bonding state and cysteine connectivity prediction server A Ceroni, A Passerini, A Vullo, P Frasconi Nucleic acids research 34 (suppl_2), W177-W181, 2006 | 356 | 2006 |
| New results on error correcting output codes of kernel machines A Passerini, M Pontil, P Frasconi IEEE transactions on neural networks 15 (1), 45-54, 2004 | 181 | 2004 |
| Automatic semantics extraction in law documents C Biagioli, E Francesconi, A Passerini, S Montemagni, C Soria Proceedings of the 10th international conference on Artificial intelligence …, 2005 | 161 | 2005 |
| Brain–computer evolutionary multiobjective optimization: a genetic algorithm adapting to the decision maker R Battiti, A Passerini IEEE Transactions on Evolutionary Computation 14 (5), 671-687, 2010 | 133 | 2010 |
| kFOIL: Learning simple relational kernels N Landwehr, A Passerini, L De Raedt, P Frasconi Aaai 6, 389-394, 2006 | 126 | 2006 |
| Widespread uncoupling between transcriptome and translatome variations after a stimulus in mammalian cells T Tebaldi, A Re, G Viero, I Pegoretti, A Passerini, E Blanzieri, A Quattrone BMC genomics 13 (1), 1-15, 2012 | 113 | 2012 |
| Identifying cysteines and histidines in transition‐metal‐binding sites using support vector machines and neural networks A Passerini, M Punta, A Ceroni, B Rost, P Frasconi Proteins: Structure, Function, and Bioinformatics 65 (2), 305-316, 2006 | 99 | 2006 |
| Predicting zinc binding at the proteome level A Passerini, C Andreini, S Menchetti, A Rosato, P Frasconi BMC bioinformatics 8 (1), 1-13, 2007 | 95 | 2007 |
| Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound S Roy, W Menapace, S Oei, B Luijten, E Fini, C Saltori, I Huijben, ... IEEE Transactions on Medical Imaging 39 (8), 2676-2687, 2020 | 94 | 2020 |
| Probabilistic inference in hybrid domains by weighted model integration V Belle, A Passerini, G Van den Broeck Proceedings of 24th International Joint Conference on Artificial …, 2015 | 82 | 2015 |
| MetalDetector v2. 0: predicting the geometry of metal binding sites from protein sequence A Passerini, M Lippi, P Frasconi Nucleic acids research 39 (suppl_2), W288-W292, 2011 | 58 | 2011 |
| Hashing-based approximate probabilistic inference in hybrid domains V Belle, G Van den Broeck, A Passerini Proceedings of the 31st Conference on Uncertainty in Artificial Intelligence …, 2015 | 52 | 2015 |
| MetalDetector: a web server for predicting metal-binding sites and disulfide bridges in proteins from sequence M Lippi, A Passerini, M Punta, B Rost, P Frasconi Bioinformatics 24 (18), 2094-2095, 2008 | 51 | 2008 |
| Automatic classification of provisions in legislative texts E Francesconi, A Passerini Artificial Intelligence and Law 15 (1), 1-17, 2007 | 48 | 2007 |
| A two-stage SVM architecture for predicting the disulfide bonding state of cysteines P Frasconi, A Passerini, A Vullo Proceedings of the 12th IEEE Workshop on Neural Networks for Signal …, 2002 | 48 | 2002 |
| Characterization of metalloproteins by high-throughput X-ray absorption spectroscopy W Shi, M Punta, J Bohon, JM Sauder, R D'Mello, M Sullivan, J Toomey, ... Genome Research 21 (6), 898-907, 2011 | 47 | 2011 |
| Fast learning of relational kernels N Landwehr, A Passerini, L De Raedt, P Frasconi Machine learning 78 (3), 305-342, 2010 | 44 | 2010 |
| Kernels on Prolog Proof Trees: Statistical Learning in the ILP Setting. A Passerini, P Frasconi, L De Raedt, R Olsson, U Schmid Journal of Machine Learning Research 7 (2), 2006 | 40 | 2006 |
| Structured learning modulo theories S Teso, R Sebastiani, A Passerini Artificial Intelligence 244, 166-187, 2017 | 39 | 2017 |
| Predicting the disulfide bonding state of cysteines with combinations of kernel machines A Ceroni, P Frasconi, A Passerini, A Vullo Journal of VLSI signal processing systems for signal, image and video …, 2003 | 37 | 2003 |
Paolo FrasconiProfessor of Computer Science, Università di FirenzeVerified email at dsi.unifi.it
