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 | 4268 | 2020 |
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 | 2202 | 2021 |
A promoter-level mammalian expression atlas Nature 507 (7493), 462-470, 2014 | 1726 | 2014 |
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 | 655 | 2009 |
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation D Chicco, N Tötsch, G Jurman BioData mining 14, 1-22, 2021 | 636 | 2021 |
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 | 533 | 2014 |
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 | 523 | 2020 |
A comparison of MCC and CEN error measures in multi-class prediction G Jurman, S Riccadonna, C Furlanello PloS one 7 (8), e41882, 2012 | 412 | 2012 |
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 | 243 | 2021 |
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 | 219 | 2012 |
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 | 187 | 2003 |
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 | 179* | 2010 |
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 | 143 | 2014 |
Canberra distance on ranked lists G Jurman, S Riccadonna, R Visintainer, C Furlanello Proceedings of advances in ranking NIPS 09 workshop, 22-27, 2009 | 135 | 2009 |
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 | 125 | 2008 |
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 | 124 | 2023 |
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 | 106 | 2012 |
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 | 105 | 2020 |
mlpy: Machine learning Python D Albanese, R Visintainer, S Merler, S Riccadonna, G Jurman, ... arXiv preprint arXiv:1202.6548, 2012 | 105 | 2012 |
PD-L1 is a therapeutic target of the bromodomain inhibitor JQ1 and, combined with HLA class I, a promising prognostic biomarker in neuroblastoma O Melaiu, M Mina, M Chierici, R Boldrini, G Jurman, P Romania, ... Clinical Cancer Research 23 (15), 4462-4472, 2017 | 100 | 2017 |