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Vincenzo Bonnici
Davide Chicco

BioData mining

Showing results (1-10 of 14) with videos related to

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Biodata Mining|September 3, 2024
Seven quick tips for gene-focused computational pangenomic analysisVincenzo Bonnici, Davide Chicco
Biodata Mining|December 14, 2017
Ten quick tips for machine learning in computational biologyDavide Chicco
Biodata Mining|February 4, 2021
Data analytics and clinical feature ranking of medical records of patients with sepsisDavide Chicco, Luca Oneto
Biodata Mining|February 21, 2023
The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classificationDavide Chicco, Giuseppe Jurman
Biodata Mining|February 24, 2023
Ten simple rules for providing bioinformatics support within a hospitalDavide Chicco, Giuseppe Jurman
Biodata 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 Jurman
Biodata Mining|January 9, 2025
The Venus score for the assessment of the quality and trustworthiness of biomedical datasetsDavide Chicco, Alessandro Fabris, Giuseppe Jurman
Biodata Mining|June 12, 2025
DBSCAN and DBCV application to open medical records heterogeneous data for identifying clinically significant clusters of patients with neuroblastomaDavide Chicco, Luca Oneto, Davide Cangelosi
Biodata Mining|February 5, 2021
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluationDavide Chicco, Niklas Tötsch, Giuseppe Jurman
Biodata Mining|March 4, 2023
Signature literature review reveals AHCY, DPYSL3, and NME1 as the most recurrent prognostic genes for neuroblastomaDavide Chicco, Tiziana Sanavia, Giuseppe Jurman
Pageof 2

Showing results (1-10 of 14) with videos related to

Sort By:
Pageof 2
Biodata Mining|September 3, 2024
Seven quick tips for gene-focused computational pangenomic analysisVincenzo Bonnici, Davide Chicco
Biodata Mining|December 14, 2017
Ten quick tips for machine learning in computational biologyDavide Chicco
Biodata Mining|February 4, 2021
Data analytics and clinical feature ranking of medical records of patients with sepsisDavide Chicco, Luca Oneto
Biodata Mining|February 21, 2023
The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classificationDavide Chicco, Giuseppe Jurman
Biodata Mining|February 24, 2023
Ten simple rules for providing bioinformatics support within a hospitalDavide Chicco, Giuseppe Jurman
Biodata 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 Jurman
Biodata Mining|January 9, 2025
The Venus score for the assessment of the quality and trustworthiness of biomedical datasetsDavide Chicco, Alessandro Fabris, Giuseppe Jurman
Biodata Mining|June 12, 2025
DBSCAN and DBCV application to open medical records heterogeneous data for identifying clinically significant clusters of patients with neuroblastomaDavide Chicco, Luca Oneto, Davide Cangelosi
Biodata Mining|February 5, 2021
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluationDavide Chicco, Niklas Tötsch, Giuseppe Jurman
Biodata Mining|March 4, 2023
Signature literature review reveals AHCY, DPYSL3, and NME1 as the most recurrent prognostic genes for neuroblastomaDavide Chicco, Tiziana Sanavia, Giuseppe Jurman
Pageof 2