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Davide Chicco
Davide Chicco
Università di Milano-Bicocca & University of Toronto
Bestätigte E-Mail-Adresse bei utoronto.ca - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
52632020
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
33512021
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
12182018
Ten quick tips for machine learning in computational biology
D Chicco
BioData Mining 10 (35), 1-17, 2017
10782017
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
8062021
Siamese neural networks: an overview
D Chicco
Artificial Neural Networks (3rd edition), Methods in Molecular Biology 2190 …, 2020
7782020
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
6292020
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
3242019
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
3232021
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
2732023
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
2612014
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
1442020
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
1092018
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
962021
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
822021
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
752019
Computational prediction of diagnosis and feature selection on mesothelioma patient health records
D Chicco, C Rovelli
PLOS One 14 (1), e0208737, 2019
722019
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
562020
Nine quick tips for pathway enrichment analysis
D Chicco, G Agapito
PLOS Computational Biology 18 (8), e1010348, 2022
552022
An ensemble learning approach for enhanced classification of patients with hepatitis and cirrhosis
D Chicco, G Jurman
IEEE Access 9, 24485-24498, 2021
552021
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