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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
20222020
Ten quick tips for machine learning in computational biology
D Chicco
BioData Mining 10 (35), 1-17, 2017
7072017
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
5842018
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
4252021
Siamese neural networks: an overview
D Chicco
Artificial Neural Networks (3rd edition), Methods in Molecular Biology 2190 …, 2020
2372020
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
2372020
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
2262014
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
2232021
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
1972019
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
632021
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
632018
Computational prediction of diagnosis and feature selection on mesothelioma patient health records
D Chicco, C Rovelli
PLOS One 14 (1), e0208737, 2019
512019
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
472020
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
462012
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
422019
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
412014
Bioconda: a sustainable and comprehensive software distribution for the life sciences
R Dale, B Grüning, A Sjödin, J Rowe, BA Chapman, CH Tomkins-Tinch, ...
bioRxiv 207092, 1-13, 2017
352017
Computational algorithms to predict Gene Ontology annotations
P Pinoli, D Chicco, M Masseroli
BMC Bioinformatics 16 (Suppl 6), S4, 2015
292015
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
262021
A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction
MP Menden, D Wang, Y Guan, M Mason, B Szalai, KC Bulusu, T Yu, ...
bioRxiv 200451, 1-32, 2018
242018
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