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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
41062020
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
19802021
Ten quick tips for machine learning in computational biology
D Chicco
BioData Mining 10 (35), 1-17, 2017
9682017
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
8122018
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
6092021
Siamese neural networks: an overview
D Chicco
Artificial Neural Networks (3rd edition), Methods in Molecular Biology 2190 …, 2020
5772020
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
5112020
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
2752019
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
2522014
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
2292021
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
1152020
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
972023
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
922018
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
742021
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
672019
Computational prediction of diagnosis and feature selection on mesothelioma patient health records
D Chicco, C Rovelli
PLOS One 14 (1), e0208737, 2019
672019
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
522012
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
432014
An ensemble learning approach for enhanced classification of patients with hepatitis and cirrhosis
D Chicco, G Jurman
IEEE Access 9, 24485-24498, 2021
422021
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
402020
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