Davide Chicco
Orcid: 0000-0001-9655-7142Affiliations:
- University of Toronto, Institute of Health Policy Management and Evaluation, ON, Canada
According to our database1,
Davide Chicco
authored at least 52 papers
between 2010 and 2024.
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Bibliography
2024
Clinical Feature Ranking Based on Ensemble Machine Learning Reveals Top Survival Factors for Glioblastoma Multiforme.
J. Heal. Informatics Res., March, 2024
Ensemble machine learning reveals key features for diabetes duration from electronic health records.
PeerJ Comput. Sci., 2024
2023
PLoS Comput. Biol., December, 2023
A statistical comparison between Matthews correlation coefficient (MCC), prevalence threshold, and Fowlkes-Mallows index.
J. Biomed. Informatics, August, 2023
PLoS Comput. Biol., January, 2023
Signature literature review reveals AHCY, DPYSL3, and NME1 as the most recurrent prognostic genes for neuroblastoma.
BioData Min., January, 2023
The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification.
BioData Min., January, 2023
BioData Min., January, 2023
Ten quick tips for bioinformatics analyses using an Apache Spark distributed computing environment.
PLoS Comput. Biol., 2023
PLoS Comput. Biol., 2023
Exploratory analysis of longitudinal data of patients with dementia through unsupervised techniques.
Proceedings of the 4th Italian Workshop on Artificial Intelligence for an Ageing Society co-located with 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023), 2023
2022
PLoS Comput. Biol., December, 2022
PLoS Comput. Biol., 2022
An Invitation to Greater Use of Matthews Correlation Coefficient in Robotics and Artificial Intelligence.
Frontiers Robotics AI, 2022
The ABC recommendations for validation of supervised machine learning results in biomedical sciences.
Frontiers Big Data, 2022
Frontiers Bioinform., 2022
A Survey on Publicly Available Open Datasets Derived From Electronic Health Records (EHRs) of Patients with Neuroblastoma.
Data Sci. J., 2022
geoCancerPrognosticDatasetsRetriever: a bioinformatics tool to easily identify cancer prognostic datasets on Gene Expression Omnibus (GEO).
Bioinform., 2022
Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning.
BioData Min., 2022
2021
An Enhanced Random Forests Approach to Predict Heart Failure From Small Imbalanced Gene Expression Data.
IEEE ACM Trans. Comput. Biol. Bioinform., 2021
The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation.
PeerJ Comput. Sci., 2021
Computational intelligence identifies alkaline phosphatase (ALP), alpha-fetoprotein (AFP), and hemoglobin levels as most predictive survival factors for hepatocellular carcinoma.
Health Informatics J., 2021
The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation.
BioData Min., 2021
Data analytics and clinical feature ranking of medical records of patients with sepsis.
BioData Min., 2021
The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen's Kappa and Brier Score in Binary Classification Assessment.
IEEE Access, 2021
The Benefits of the Matthews Correlation Coefficient (MCC) Over the Diagnostic Odds Ratio (DOR) in Binary Classification Assessment.
IEEE Access, 2021
A Machine Learning Analysis of Health Records of Patients With Chronic Kidney Disease at Risk of Cardiovascular Disease.
IEEE Access, 2021
Arterial Disease Computational Prediction and Health Record Feature Ranking Among Patients Diagnosed With Inflammatory Bowel Disease.
IEEE Access, 2021
An Ensemble Learning Approach for Enhanced Classification of Patients With Hepatitis and Cirrhosis.
IEEE Access, 2021
Proceedings of the Artificial Neural Networks - Third Edition., 2021
2020
Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone.
BMC Medical Informatics Decis. Mak., 2020
CoRR, 2020
2019
Proceedings of the Encyclopedia of Bioinformatics and Computational Biology - Volume 1, 2019
2018
Novelty Indicator for Enhanced Prioritization of Predicted Gene Ontology Annotations.
IEEE ACM Trans. Comput. Biol. Bioinform., 2018
PeerJ Comput. Sci., 2018
2017
2016
IEEE ACM Trans. Comput. Biol. Bioinform., 2016
2015
IEEE ACM Trans. Comput. Biol. Bioinform., 2015
Proceedings of the Computational Intelligence Methods for Bioinformatics and Biostatistics, 2015
2014
Computational Prediction of Gene Functions through Machine Learning methods and Multiple Validation Procedures
PhD thesis, 2014
Proceedings of the IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 2014
Proceedings of the Computational Intelligence Methods for Bioinformatics and Biostatistics, 2014
Proceedings of the 5th ACM Conference on Bioinformatics, 2014
2013
Proceedings of the Computational Intelligence Methods for Bioinformatics and Biostatistics, 2013
Enhanced probabilistic latent semantic analysis with weighting schemes to predict genomic annotations.
Proceedings of the 13th IEEE International Conference on BioInformatics and BioEngineering, 2013
Proceedings of the 13th IEEE International Conference on BioInformatics and BioEngineering, 2013
2012
Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), 2012
2011
Proceedings of the 11th International Conference on Intelligent Systems Design and Applications, 2011
Proceedings of the Computational Intelligence Methods for Bioinformatics and Biostatistics, 2011
2010
Proceedings of the Neural Nets WIRN10, 2010