Davide Chicco
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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
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
Ten quick tips for machine learning in computational biology
D Chicco
BioData Mining 10 (35), 1-17, 2017
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
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
Siamese neural networks: an overview
D Chicco
Artificial Neural Networks (3rd edition), Methods in Molecular Biology 2190 …, 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
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
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
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
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
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
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
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
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
Computational prediction of diagnosis and feature selection on mesothelioma patient health records
D Chicco, C Rovelli
PLOS One 14 (1), e0208737, 2019
Probabilistic latent semantic analysis for prediction of Gene Ontology annotations
M Masseroli, D Chicco, P Pinoli
Proceedings of IJCNN 2012 – the 2012 International Joint Conference on …, 2012
Latent Dirichlet Allocation based on Gibbs Sampling for gene function prediction
P Pinoli, D Chicco, M Masseroli
Proceedings of IEEE CIBCB 2014 – the IEEE 2014 Conference on Computational …, 2014
An ensemble learning approach for enhanced classification of patients with hepatitis and cirrhosis
D Chicco, G Jurman
IEEE Access 9, 24485-24498, 2021
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
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