| The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models MaQC Consortium Nature biotechnology 28 (8), 827, 2010 | 691 | 2010 |
| The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance C Wang, B Gong, PR Bushel, J Thierry-Mieg, D Thierry-Mieg, J Xu, ... Nature biotechnology 32 (9), 926-932, 2014 | 343 | 2014 |
| Minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers D Albanese, M Filosi, R Visintainer, S Riccadonna, G Jurman, ... Bioinformatics 29 (3), 407-408, 2013 | 114 | 2013 |
| mlpy: Machine learning python D Albanese, R Visintainer, S Merler, S Riccadonna, G Jurman, ... arXiv preprint arXiv:1202.6548, 2012 | 83 | 2012 |
| Canberra distance on ranked lists G Jurman, S Riccadonna, R Visintainer, C Furlanello Proceedings of advances in ranking NIPS 09 workshop, 22-27, 2009 | 80 | 2009 |
| An introduction to spectral distances in networks NNWB Apolloni Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets …, 2011 | 40 | 2011 |
| The HIM glocal metric and kernel for network comparison and classification G Jurman, R Visintainer, M Filosi, S Riccadonna, C Furlanello 2015 IEEE International Conference on Data Science and Advanced Analytics …, 2015 | 30 | 2015 |
| The MAQC-II project: a comprehensive study of common practices for the development and validation of microarray-based predictive models. L Shi, G Campbell, W Jones, F Campagne, Z Wen, S Walker, Z Su, T Chu, ... | 30 | 2010 |
| Algebraic comparison of partial lists in bioinformatics G Jurman, S Riccadonna, R Visintainer, C Furlanello PloS one 7 (5), e36540, 2012 | 24 | 2012 |
| Stability indicators in network reconstruction M Filosi, R Visintainer, S Riccadonna, G Jurman, C Furlanello PloS one 9 (2), e89815, 2014 | 18 | 2014 |
| RegnANN: reverse engineering gene networks using artificial neural networks M Grimaldi, R Visintainer, G Jurman PloS one 6 (12), e28646, 2011 | 15 | 2011 |
| DTW-MIC coexpression networks from time-course data S Riccadonna, G Jurman, R Visintainer, M Filosi, C Furlanello arXiv preprint arXiv:1210.3149, 2012 | 14* | 2012 |
| A machine learning pipeline for discriminant pathways identification A Barla, G Jurman, R Visintainer, M Squillario, M Filosi, S Riccadonna, ... International Meeting on Computational Intelligence Methods for …, 2011 | 10 | 2011 |
| The interplay between individual social behavior and clinical symptoms in small clustered groups P Poletti, R Visintainer, B Lepri, S Merler BMC infectious diseases 17 (1), 1-8, 2017 | 8 | 2017 |
| mlpy: Machine Learning PYThon. 2012 D Albanese, R Visintainer, S Merler, S Riccadonna, G Jurman, ... Project homepage at http://mlpy. fbk. eu, 2016 | 8 | 2016 |
| A glocal distance for network comparison G Jurman, R Visintainer, S Riccadonna, M Filosi, C Furlanello arXiv preprint arXiv:1201.2931, 2012 | 8 | 2012 |
| A comprehensive study design reveals treatment-and transcript abundance–dependent concordance between rna-seq and microarray data C Wang, B Gong, PR Bushel, J Thierry-Mieg, D Thierry-Mieg, J Xu, ... Nature biotechnology 32 (9), 926, 2014 | 7 | 2014 |
| cmine, minerva & minepy: a C engine for the MINE suite and its R and Python wrappers D Albanese, M Filosi, R Visintainer, S Riccadonna, G Jurman, ... stat 1050, 21, 2012 | 7 | 2012 |
| DGW: an exploratory data analysis tool for clustering and visualisation of epigenomic marks S Lukauskas, R Visintainer, G Sanguinetti, GB Schweikert BMC bioinformatics 17 (16), 53-63, 2016 | 6 | 2016 |
| Biological network comparison via Ipsen-Mikhailov distance G Jurman, S Riccadonna, R Visintainer, C Furlanello arXiv preprint arXiv:1109.0220, 2011 | 6 | 2011 |
