| The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation D Chicco, G Jurman BMC Genomics 21 (6), 1-13, 2020 | 5308 | 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 | 3397 | 2021 |
| Bioconda: sustainable and comprehensive software distribution for the life sciences B Grüning, R Dale, A Sjödin, BA Chapman, J Rowe, CH Tomkins-Tinch, ... Nature Methods 15 (7), 475, 2018 | 1234 | 2018 |
| Ten quick tips for machine learning in computational biology D Chicco BioData Mining 10 (35), 1-17, 2017 | 1078 | 2017 |
| 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 (13), 1-22, 2021 | 812 | 2021 |
| Siamese neural networks: an overview D Chicco Artificial Neural Networks (3rd edition), Methods in Molecular Biology 2190 …, 2020 | 781 | 2020 |
| 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 (16), 1-16, 2020 | 633 | 2020 |
| 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 | 330 | 2021 |
| Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen MP Menden, D Wang, MJ Mason, B Szalai, KC Bulusu, Y Guan, T Yu, ... Nature Communications 10 (1), 2674, 2019 | 325 | 2019 |
| 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 (4), 1-23, 2023 | 277 | 2023 |
| Deep autoencoder neural networks for Gene Ontology annotation predictions D Chicco, P Sadowski, P Baldi Proceedings of ACM BCB 2014 – the 5th ACM Conference on Bioinformatics …, 2014 | 261 | 2014 |
| Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality S Shin, PC Austin, HJ Ross, H Abdel‐Qadir, C Freitas, G Tomlinson, ... ESC Heart Failure 8 (1), 106-115, 2020 | 145 | 2020 |
| Supervised deep learning embeddings for the prediction of cervical cancer diagnosis K Fernandes, D Chicco, JS Cardoso, J Fernandes PeerJ Computer Science 4 (e154), 2018 | 109 | 2018 |
| The benefits of the Matthews correlation coefficient (MCC) over the diagnostic odds ratio (DOR) in binary classification assessment D Chicco, V Starovoitov, G Jurman IEEE Access 9, 47112-47124, 2021 | 96 | 2021 |
| Warrens, and Giuseppe Jurman.“The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation” D Chicco, J Matthijs PeerJ Computer Science 7, e623, 2021 | 85 | 2021 |
| Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach R Kueffner, N Zach, M Bronfeld, R Norel, N Atassi, V Balagurusamy, ... Scientific Reports 9 (1), 690, 2019 | 76 | 2019 |
| Computational prediction of diagnosis and feature selection on mesothelioma patient health records D Chicco, C Rovelli PLOS One 14 (1), e0208737, 2019 | 73 | 2019 |
| Survival prediction of patients with sepsis from age, sex, and septic episode number alone D Chicco, G Jurman Scientific Reports 10 (17156), 1-12, 2020 | 57 | 2020 |
| Nine quick tips for pathway enrichment analysis D Chicco, G Agapito PLOS Computational Biology 18 (8), e1010348, 2022 | 56 | 2022 |
| An ensemble learning approach for enhanced classification of patients with hepatitis and cirrhosis D Chicco, G Jurman IEEE Access 9, 24485-24498, 2021 | 55 | 2021 |
Giuseppe JurmanHumanitas University, Milan & Fondazione Bruno Kessler, Trento, Italyfbk.eu의 이메일 확인됨