Roberto Visintainer
Roberto Visintainer
The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Italy
Verified email at cosbi.eu
Title
Cited by
Cited by
Year
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
6672010
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
3232014
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
1082013
Canberra distance on ranked lists
G Jurman, S Riccadonna, R Visintainer, C Furlanello
Proceedings of advances in ranking NIPS 09 workshop, 22-27, 2009
762009
mlpy: Machine learning python
D Albanese, R Visintainer, S Merler, S Riccadonna, G Jurman, ...
arXiv preprint arXiv:1202.6548, 2012
742012
An introduction to spectral distances in networks
NNWB Apolloni
Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets …, 2011
382011
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, ...
302010
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
262015
Algebraic comparison of partial lists in bioinformatics
G Jurman, S Riccadonna, R Visintainer, C Furlanello
PloS one 7 (5), e36540, 2012
232012
Stability indicators in network reconstruction
M Filosi, R Visintainer, S Riccadonna, G Jurman, C Furlanello
PloS one 9 (2), e89815, 2014
172014
RegnANN: reverse engineering gene networks using artificial neural networks
M Grimaldi, R Visintainer, G Jurman
PloS one 6 (12), e28646, 2011
152011
mlpy: Machine Learning PYThon. 2012
D Albanese, R Visintainer, S Merler, S Riccadonna, G Jurman, ...
arXiv preprint arXiv:1202.6548 4, 2016
142016
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
102011
A glocal distance for network comparison
G Jurman, R Visintainer, S Riccadonna, M Filosi, C Furlanello
arXiv preprint arXiv:1201.2931, 2012
82012
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
72016
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), 521, 2017
62017
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
62014
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
62012
Mlpy machine learning py
D Albanese, S Merler, G Jurman, R Visintainer, C Furlanello
62009
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