| Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles G Pollastri, D Przybylski, B Rost, P Baldi Proteins: Structure, Function, and Bioinformatics 47 (2), 228-235, 2002 | 998 | 2002 |
| Exploiting the past and the future in protein secondary structure prediction P Baldi, S Brunak, P Frasconi, G Soda, G Pollastri Bioinformatics 15 (11), 937-946, 1999 | 678 | 1999 |
| Porter: a new, accurate server for protein secondary structure prediction G Pollastri, A McLysaght Bioinformatics 21 (8), 1719-1720, 2005 | 592 | 2005 |
| Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules A Lusci, G Pollastri, P Baldi Journal of chemical information and modeling 53 (7), 1563-1575, 2013 | 589 | 2013 |
| Towards the improved discovery and design of functional peptides: common features of diverse classes permit generalized prediction of bioactivity C Mooney, NJ Haslam, G Pollastri, DC Shields Public Library of Science 7 (10), e45012, 2012 | 383 | 2012 |
| Prediction of coordination number and relative solvent accessibility in proteins G Pollastri, P Baldi, P Fariselli, R Casadio Proteins: Structure, Function, and Bioinformatics 47 (2), 142-153, 2002 | 317 | 2002 |
| The principled design of large-scale recursive neural network architectures--dag-rnns and the protein structure prediction problem P Baldi, G Pollastri The Journal of Machine Learning Research 4, 575-602, 2003 | 265 | 2003 |
| A neural network approach to ordinal regression J Cheng, Z Wang, G Pollastri 2008 IEEE international joint conference on neural networks (IEEE world …, 2008 | 259 | 2008 |
| Deep learning methods in protein structure prediction M Torrisi, G Pollastri, Q Le Computational and Structural Biotechnology Journal 18, 1301-1310, 2020 | 246 | 2020 |
| Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners G Pollastri, P Baldi Bioinformatics 18 (suppl_1), S62-S70, 2002 | 201 | 2002 |
| Spritz: a server for the prediction of intrinsically disordered regions in protein sequences using kernel machines A Vullo, O Bortolami, G Pollastri, SCE Tosatto Nucleic acids research 34 (suppl_2), W164-W168, 2006 | 161 | 2006 |
| Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information G Pollastri, AJM Martin, C Mooney, A Vullo BMC bioinformatics 8, 1-12, 2007 | 158 | 2007 |
| Porter, PaleAle 4.0: high-accuracy prediction of protein secondary structure and relative solvent accessibility C Mirabello, G Pollastri Bioinformatics 29 (16), 2056-2058, 2013 | 151 | 2013 |
| CPPpred: prediction of cell penetrating peptides TA Holton, G Pollastri, DC Shields, C Mooney Bioinformatics 29 (23), 3094-3096, 2013 | 141 | 2013 |
| DOME: Recommendations for supervised machine learning validation in biology I Walsh, D Fishman, D Garcia-Gasulla, T Titma, G Pollastri, J Harrow, ... Nature Methods, 1-6, 2021 | 135* | 2021 |
| A two-stage approach for improved prediction of residue contact maps A Vullo, I Walsh, G Pollastri BMC bioinformatics 7, 1-12, 2006 | 116 | 2006 |
| Distill: a suite of web servers for the prediction of one-, two-and three-dimensional structural features of proteins D Baú, AJM Martin, C Mooney, A Vullo, I Walsh, G Pollastri BMC bioinformatics 7, 1-8, 2006 | 111 | 2006 |
| CSpritz: accurate prediction of protein disorder segments with annotation for homology, secondary structure and linear motifs I Walsh, AJM Martin, T Di Domenico, A Vullo, G Pollastri, SCE Tosatto Nucleic acids research 39 (suppl_2), W190-W196, 2011 | 109 | 2011 |
| Bidirectional dynamics for protein secondary structure prediction P Baldi, S Brunak, P Frasconi, G Pollastri, G Soda Sequence Learning: Paradigms, Algorithms, and Applications, 80-104, 2001 | 93 | 2001 |
| Prediction of short linear protein binding regions C Mooney, G Pollastri, DC Shields, NJ Haslam Journal of molecular biology 415 (1), 193-204, 2012 | 85 | 2012 |
