Davide Chicco

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Giuseppe JurmanFondazione Bruno Kessler, Trento, ItalyVerified email at fbk.eu
Marco MasseroliPolitecnico di MilanoVerified email at polimi.it
Matthijs J. WarrensGION, University of GroningenVerified email at rug.nl
Luca OnetoDIBRIS - University of GenoaVerified email at unige.it
Peter C AustinICES (Institute for Clinical Evaluative Sciences)Verified email at ices.on.ca
Douglas LeeUniversity of TorontoVerified email at ices.on.ca
Michael M. HoffmanChair and Senior Scientist, University Health Network/Associate Professor, University of TorontoVerified email at utoronto.ca
Pierre BaldiProfessor, University of California, IrvineVerified email at ics.uci.edu
Peter SadowskiAssociate Professor, University of Hawai’i at MānoaVerified email at hawaii.edu
Jaime CardosoFull Professor at Faculty of Engineering, University of Porto; Researcher at INESC TECVerified email at fe.up.pt
Kelwin FernandesNILG.AIVerified email at nilg.ai
V. Starovoitov (ВВ Старовойтов, V. St...ОИПИ НАН Беларуси, National Academy of Sciences of Belarus United Institute of Informatics ProblemsVerified email at newman.bas-net.by
Abbas AlameerKuwait UniversityVerified email at ku.edu.kw
Erica TavazziUniversità degli Studi di PadovaVerified email at dei.unipd.it
Eleonora CiceriGucciVerified email at eleonoraciceri.it
Angelo FacchianoIstituto di Scienze dell'Alimentazione, CNR - Institute of Food Sciences, National Research CouncilVerified email at isa.cnr.it
Giuseppe AgapitoVerified email at unicz.it
Jüri ReimandAssociate professor, Ontario Institute for Cancer Research, University of TorontoVerified email at utoronto.ca
Davide CangelosiSenior Investigator, IRCCS Giannina Gaslini InstituteVerified email at gaslini.org
Dominik HeiderProfessor, University of DüsseldorfVerified email at hhu.de

Davide Chicco

Università di Milano-Bicocca & University of Toronto

Verified email at utoronto.ca - Homepage

applied machine learning bioinformatics biomedical informatics Matthews correlation coefficient


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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	3455	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	1368	2021
Ten quick tips for machine learning in computational biology D Chicco BioData Mining 10 (35), 1-17, 2017	892	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	706	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	497	2021
Siamese neural networks: an overview D Chicco Artificial Neural Networks (3rd edition), Methods in Molecular Biology 2190 …, 2020	483	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	425	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	249	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	246	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	168	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	89	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	85	2018
Computational prediction of diagnosis and feature selection on mesothelioma patient health records D Chicco, C Rovelli PLOS One 14 (1), e0208737, 2019	60	2019
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	54	2019
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	53	2021
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	50	2012
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	44	2017
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	42	2014
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	36	2023
Machine learning compared to conventional statistical models for predicting myocardial infarction readmission and mortality: a systematic review SM Cho, PC Austin, HJ Ross, H Abdel-Qadir, D Chicco, G Tomlinson, ... Canadian Journal of Cardiology, 2021	33	2021

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