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BMC Genomics|January 4, 2020
The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluationDavide Chicco, Giuseppe JurmanBiodata Mining|February 21, 2023
The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classificationDavide Chicco, Giuseppe JurmanScientific Reports|October 14, 2020
Survival prediction of patients with sepsis from age, sex, and septic episode number aloneDavide Chicco, Giuseppe JurmanFrontiers in Bioinformatics|November 17, 2022
A brief survey of tools for genomic regions enrichment analysisDavide Chicco, Giuseppe JurmanPlos Computational Biology|December 1, 2025
Comment on "Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper"Davide Chicco, Giuseppe JurmanBMC Medical Informatics and Decision Making|February 5, 2020
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction aloneDavide Chicco, Giuseppe JurmanBiodata Mining|February 24, 2023
Ten simple rules for providing bioinformatics support within a hospitalDavide Chicco, Giuseppe JurmanJournal of Biomedical Informatics|June 23, 2023
A statistical comparison between Matthews correlation coefficient (MCC), prevalence threshold, and Fowlkes-Mallows indexDavide Chicco, Giuseppe JurmanBiodata Mining|January 9, 2025
The Venus score for the assessment of the quality and trustworthiness of biomedical datasetsDavide Chicco, Alessandro Fabris, Giuseppe JurmanBiodata Mining|August 20, 2025
A simple guide to the use of Student's t-test, Mann-Whitney U test, Chi-squared test, and Kruskal-Wallis test in biostatisticsDavide Chicco, Andrea Sichenze, Giuseppe JurmanPageof 13