| 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 | 309 | 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 | 163 | 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 | 145 | 2005 |
| kFOIL: Learning simple relational kernels N Landwehr, A Passerini, L De Raedt, P Frasconi Aaai 6, 389-394, 2006 | 123 | 2006 |
| 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 | 121 | 2010 |
| 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), 220, 2012 | 93 | 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 | 91 | 2006 |
| Predicting zinc binding at the proteome level A Passerini, C Andreini, S Menchetti, A Rosato, P Frasconi BMC bioinformatics 8 (1), 39, 2007 | 84 | 2007 |
| Probabilistic inference in hybrid domains by weighted model integration V Belle, A Passerini, G Van den Broeck Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015 | 58 | 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 | 53 | 2008 |
| 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 | 47 | 2002 |
| 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 | 45 | 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 | 43 | 2015 |
| 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 | 39 | 2011 |
| Automatic classification of provisions in legislative texts E Francesconi, A Passerini Artificial Intelligence and Law 15 (1), 1-17, 2007 | 39 | 2007 |
| Fast learning of relational kernels N Landwehr, A Passerini, L De Raedt, P Frasconi Machine learning 78 (3), 305-342, 2010 | 38 | 2010 |
| Kernels on prolog proof trees: Statistical learning in the ILP setting A Passerini, P Frasconi, LD Raedt Journal of Machine Learning Research 7 (Feb), 307-342, 2006 | 37 | 2006 |
| 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 | 34 | 2003 |
| Predicting the geometry of metal binding sites from protein sequence P Frasconi, A Passerini Advances in Neural Information Processing Systems, 465-472, 2009 | 32 | 2009 |
| Structured learning modulo theories S Teso, R Sebastiani, A Passerini Artificial Intelligence 244, 166-187, 2017 | 29 | 2017 |
Paolo FrasconiProfessor of Computer Science, Università di FirenzeEmail verificata su dsi.unifi.it
