| From protein sequence to dynamics and disorder with DynaMine E Cilia, R Pancsa, P Tompa, T Lenaerts, WF Vranken Nature communications 4 (1), 2741, 2013 | 176 | 2013 |
| The DynaMine webserver: predicting protein dynamics from sequence E Cilia, R Pancsa, P Tompa, T Lenaerts, WF Vranken Nucleic acids research 42 (W1), W264-W270, 2014 | 162 | 2014 |
| Argot2: a large scale function prediction tool relying on semantic similarity of weighted Gene Ontology terms M Falda, S Toppo, A Pescarolo, E Lavezzo, B Di Camillo, A Facchinetti, ... BMC bioinformatics 13, 1-9, 2012 | 157 | 2012 |
| DIDA: A curated and annotated digenic diseases database AM Gazzo, D Daneels, E Cilia, M Bonduelle, M Abramowicz, ... Nucleic acids research 44 (D1), D900-D907, 2016 | 110 | 2016 |
| Accurate prediction of the dynamical changes within the second PDZ domain of PTP1e E Cilia, GW Vuister, T Lenaerts PLoS computational biology 8 (11), e1002794, 2012 | 35 | 2012 |
| The signature amidase from Sulfolobus solfataricus belongs to the CX3C subgroup of enzymes cleaving both amides and nitriles: Ser195 and Cys145 are … E Cilia, A Fabbri, M Uriani, GG Scialdone, S Ammendola The FEBS Journal 272 (18), 4716-4724, 2005 | 35 | 2005 |
| Early folding events, local interactions, and conservation of protein backbone rigidity R Pancsa, D Raimondi, E Cilia, WF Vranken Biophysical journal 110 (3), 572-583, 2016 | 31 | 2016 |
| Predicting structural and functional sites in proteins by searching for maximum-weight cliques F Mascia, E Cilia, M Brunato, A Passerini Proceedings of the AAAI Conference on Artificial Intelligence 24 (1), 1274-1279, 2010 | 25 | 2010 |
| Automatic prediction of catalytic residues by modeling residue structural neighborhood E Cilia, A Passerini BMC bioinformatics 11, 1-17, 2010 | 23 | 2010 |
| Dynamically coupled residues within the SH2 domain of FYN are key to unlocking its activity R Huculeci, E Cilia, A Lyczek, L Buts, K Houben, MA Seeliger, ... Structure 24 (11), 1947-1959, 2016 | 12 | 2016 |
| Predicting virus mutations through statistical relational learning E Cilia, S Teso, S Ammendola, T Lenaerts, A Passerini BMC bioinformatics 15, 1-12, 2014 | 10 | 2014 |
| Relational feature mining with hierarchical Multitask kFOIL E Cilia, N Landwehr, A Passerini Fundamenta informaticae 113 (2), 151-177, 2011 | 7 | 2011 |
| From binding-induced dynamic effects in SH3 structures to evolutionary conserved sectors A Zafra Ruano, E Cilia, JR Couceiro, J Ruiz Sanz, J Schymkowitz, ... PLoS computational biology 12 (5), e1004938, 2016 | 6 | 2016 |
| From protein sequence to dynamics and disorder with DynaMine. Nat Commun 4, 2741 E Cilia, R Pancsa, P Tompa, T Lenaerts, WF Vranken | 5 | 2013 |
| Advanced tree-based kernels for protein classification E Cilia, A Moschitti Congress of the Italian Association for Artificial Intelligence, 218-229, 2007 | 4 | 2007 |
| Structured kernels for automatic detection of protein active sites E Cilia, A Moschitti, S Ammendola, R Basili MLG 2006, 117, 2006 | 4 | 2006 |
| Identification of the amino acid residues affecting the catalytic pocket of the Sulfolobus solfataricus signature amidase C Elisa, A Sergio Protein and Peptide Letters 17 (2), 146, 2010 | 3 | 2010 |
| Predicting virus mutations through relational learning. E Cilia, S Teso, S Ammendola, T Lenaerts, A Passerini AIMM, 2012 | 2 | 2012 |
| Frankenstein Junior: a relational learning approach toward protein engineering E Cilia, A Passerini Proceedings of the Annotation, Interpretation and Management of Mutations …, 2010 | 1 | 2010 |
| Mining Drug Resistance Relational Features with Hierarchical Multitask kFOIL E Cilia, N Landwehr, A Passerini Proceedings of BioLogical@ AI* IA2009, 2009 | 1 | 2009 |
