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Giuseppe Jurman
Giuseppe Jurman
Verified email at fbk.eu - Homepage
Title
Cited by
Cited by
Year
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
D Chicco, G Jurman
BMC genomics 21, 1-13, 2020
42242020
The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation
D Chicco, MJ Warrens, G Jurman
Peerj computer science 7, e623, 2021
21132021
A promoter-level mammalian expression atlas
Nature 507 (7493), 462-470, 2014
17122014
The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models
Nature biotechnology 28 (8), 827-838, 2010
9032010
Repeatability of published microarray gene expression analyses
JPA Ioannidis, DB Allison, CA Ball, I Coulibaly, X Cui, AC Culhane, ...
Nature genetics 41 (2), 149-155, 2009
6532009
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation
D Chicco, N T÷tsch, G Jurman
BioData mining 14, 1-22, 2021
6232021
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
5312014
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone
D Chicco, G Jurman
BMC medical informatics and decision making 20, 1-16, 2020
5232020
A comparison of MCC and CEN error measures in multi-class prediction
G Jurman, S Riccadonna, C Furlanello
PloS one 7 (8), e41882, 2012
4142012
The Matthews correlation coefficient (MCC) is more informative than Cohen’s Kappa and Brier score in binary classification assessment
D Chicco, MJ Warrens, G Jurman
Ieee Access 9, 78368-78381, 2021
2402021
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, bts707, 2012
2182012
Entropy-based gene ranking without selection bias for the predictive classification of microarray data
C Furlanello, M Serafini, S Merler, G Jurman
BMC bioinformatics 4, 1-20, 2003
1872003
Differential roles of epigenetic changes and Foxp3 expression in regulatory T cell-specific transcriptional regulation
H Morikawa, N Ohkura, A Vandenbon, M Itoh, S Nagao-Sato, H Kawaji, ...
Proceedings of the National Academy of Sciences 111 (14), 5289-5294, 2014
1432014
Canberra distance on ranked lists
G Jurman, S Riccadonna, R Visintainer, C Furlanello
Proceedings of advances in ranking NIPS 09 workshop, 22-27, 2009
1352009
Algebraic stability indicators for ranked lists in molecular profiling
G Jurman, S Merler, A Barla, S Paoli, A Galea, C Furlanello
Bioinformatics 24 (2), 258-264, 2008
1252008
The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification
D Chicco, G Jurman
BioData Mining 16 (1), 4, 2023
1162023
mlpy: Machine learning Python
D Albanese, R Visintainer, S Merler, S Riccadonna, G Jurman, ...
arXiv preprint arXiv:1202.6548, 2012
1072012
Clinical value of prognosis gene expression signatures in colorectal cancer: a systematic review
R Sanz-Pamplona, A Berenguer, D Cordero, S Riccadonna, X Sole, ...
PloS one 7 (11), e48877, 2012
1052012
Cellular and gene signatures of tumor-infiltrating dendritic cells and natural-killer cells predict prognosis of neuroblastoma
O Melaiu, M Chierici, V Lucarini, G Jurman, LA Conti, R De Vito, ...
Nature Communications 11 (1), 5992, 2020
1032020
An accelerated procedure for recursive feature ranking on microarray data
C Furlanello, M Serafini, S Merler, G Jurman
Neural Networks 16 (5-6), 641-648, 2003
1022003
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Articles 1–20