D. Chicco

发表

The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

D. Chicco, Giuseppe Jurman, 2020, BMC Genomics.

Survival prediction of patients with sepsis from age, sex, and septic episode number alone

D. Chicco, Giuseppe Jurman, 2020, Scientific Reports.

BEHST: genomic set enrichment analysis enhanced through integration of chromatin long-range interactions

D. Chicco, J. Reimand, M. M. Hoffman, 2019, bioRxiv.

Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality

P. Austin, D. Chicco, G. Tomlinson, 2020, ESC heart failure.

Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach

Jung-Hsien Chiang, Devin C. Koestler, Celine Vens, 2018, Scientific Reports.

Ten simple rules for organizing a special session at a scientific conference

D. Chicco, P. Bourne, 2022, PLoS Comput. Biol..

Nine quick tips for pathway enrichment analysis

D. Chicco, Giuseppe Agapito, 2022, PLoS Comput. Biol..

A machine learning analysis of health records of patients with chronic kidney disease at risk of cardiovascular disease

D. Chicco, L. Oneto, C. Lovejoy, 2021, IEEE Access.

The ABC recommendations for validation of supervised machine learning results in biomedical sciences

D. Chicco, Giuseppe Jurman, 2022, Frontiers in Big Data.

A brief survey of tools for genomic regions enrichment analysis

D. Chicco, Giuseppe Jurman, 2022, Frontiers in Bioinformatics.

Genome Informatics 2016

D. Chicco, M. M. Hoffman, 2017, Genome Biology.

Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning

D. Chicco, Giuseppe Jurman, S. Rahmati, 2022, BioData Mining.

Ten quick tips for computational analysis of medical images

D. Chicco, R. Shiradkar, 2023, PLoS Comput. Biol..

The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

Matthijs J. Warrens, Giuseppe Jurman, Davide Chicco, 2021, PeerJ Comput. Sci..

The Benefits of the Matthews Correlation Coefficient (MCC) Over the Diagnostic Odds Ratio (DOR) in Binary Classification Assessment

Giuseppe Jurman, Davide Chicco, Valery Starovoitov, 2021, IEEE Access.

The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation

Niklas Tötsch, Giuseppe Jurman, Davide Chicco, 2021, BioData Mining.

Siamese Neural Networks: An Overview

Davide Chicco, D. Chicco, 2021, Artificial Neural Networks, 3rd Edition.

Computational prediction of diagnosis and feature selection on mesothelioma patient health records

Davide Chicco, D. Chicco, Cristina Rovelli, 2019, PloS one.

The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation

D. Chicco, Giuseppe Jurman, 2020, BMC Genomics.

Deep autoencoder neural networks for gene ontology annotation predictions

Pierre Baldi, Davide Chicco, Peter J. Sadowski, 2014, BCB.

Brief Survey on Machine Learning in Epistasis.

D. Chicco, Trent Faultless, 2021, Methods in molecular biology.

A Survey on Publicly Available Open Datasets Derived From Electronic Health Records (EHRs) of Patients with Neuroblastoma

D. Chicco, D. Cangelosi, Gabriel Cerono, 2022, Data Sci. J..

Ten quick tips for machine learning in computational biology

Davide Chicco, D. Chicco, 2017, BioData Mining.

Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

Giuseppe Jurman, Davide Chicco, D. Chicco, 2020, BMC Medical Informatics and Decision Making.

geneExpressionFromGEO: An R Package to Facilitate Data Reading from Gene Expression Omnibus (GEO).

D. Chicco, 2021, Methods in molecular biology.

An Ensemble Learning Approach for Enhanced Classification of Patients With Hepatitis and Cirrhosis

Giuseppe Jurman, Davide Chicco, D. Chicco, 2021, IEEE Access.

The MCC-F1 curve: a performance evaluation technique for binary classification pdf

Chang Cao, Davide Chicco, Michael M. Hoffman, 2020, ArXiv.

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Amir K. Foroushani, Yuanfang Guan, Jaewoo Kang, 2019, Nature Communications.

Signature literature review reveals AHCY, DPYSL3, and NME1 as the most recurrent prognostic genes for neuroblastoma

D. Chicco, Giuseppe Jurman, T. Sanavia, 2023, BioData Mining.

Eleven quick tips for data cleaning and feature engineering

D. Chicco, L. Oneto, E. Tavazzi, 2022, PLoS Comput. Biol..

An Enhanced Random Forests Approach to Predict Heart Failure From Small Imbalanced Gene Expression Data

Luca Oneto, Davide Chicco, D. Chicco, 2020, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

geoCancerPrognosticDatasetsRetriever: a bioinformatics tool to easily identify cancer prognostic datasets on Gene Expression Omnibus (GEO)

D. Chicco, Abbas Alameer, 2021, Bioinform..

The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen’s Kappa and Brier Score in Binary Classification Assessment

D. Chicco, Giuseppe Jurman, M. Warrens, 2021, IEEE Access.

Ten simple rules for providing bioinformatics support within a hospital

D. Chicco, Giuseppe Jurman, 2023, BioData Mining.

Novelty Indicator for Enhanced Prioritization of Predicted Gene Ontology Annotations

Marco Masseroli, Fernando Palluzzi, Davide Chicco, 2018, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Latent Dirichlet Allocation based on Gibbs Sampling for gene function prediction

Marco Masseroli, Davide Chicco, Pietro Pinoli, 2014, 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology.

Bioconda: sustainable and comprehensive software distribution for the life sciences

Yuri Pirola, Anthony Bretaudeau, Wei Shen, 2017, Nature Methods.

Biological and Medical Ontologies: Protein Ontology (PRO)

Marco Masseroli, Davide Chicco, D. Chicco, 2019, Encyclopedia of Bioinformatics and Computational Biology.

MACHINE LEARNING COMPARED TO CONVENTIONAL STATISTICAL MODELS FOR PREDICTING MYOCARDIAL INFARCTION READMISSION AND MORTALITY: A SYSTEMATIC REVIEW.

P. Austin, D. Chicco, G. Tomlinson, 2021, The Canadian journal of cardiology.

Supervised deep learning embeddings for the prediction of cervical cancer diagnosis

Jaime S. Cardoso, Kelwin Fernandes, Davide Chicco, 2018, PeerJ Comput. Sci..

Enhanced probabilistic latent semantic analysis with weighting schemes to predict genomic annotations

Marco Masseroli, Davide Chicco, Pietro Pinoli, 2013, 13th IEEE International Conference on BioInformatics and BioEngineering.

Probabilistic Latent Semantic Analysis for prediction of Gene Ontology annotations

Marco Masseroli, Davide Chicco, Pietro Pinoli, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

Semi-automated genome annotation using epigenomic data and Segway

Anshul Kundaje, Davide Chicco, Rachel Ancar, 2016, bioRxiv.

Computational algorithms to predict Gene Ontology annotations

Marco Masseroli, Davide Chicco, Pietro Pinoli, 2015, BMC Bioinformatics.

Computational intelligence identifies alkaline phosphatase (ALP), alpha-fetoprotein (AFP), and hemoglobin levels as most predictive survival factors for hepatocellular carcinoma

Luca Oneto, Davide Chicco, D. Chicco, 2021, Health Informatics J..

Arterial Disease Computational Prediction and Health Record Feature Ranking Among Patients Diagnosed With Inflammatory Bowel Disease

Giuseppe Jurman, Davide Chicco, D. Chicco, 2021, IEEE Access.

Data analytics and clinical feature ranking of medical records of patients with sepsis

Luca Oneto, Davide Chicco, D. Chicco, 2021, BioData Mining.

Editorial: Artificial Intelligence Bioinformatics: Development and Application of Tools for Omics and Inter-Omics Studies

Dominik Heider, Angelo Facchiano, Davide Chicco, 2020, Frontiers in Genetics.

Ten quick tips for avoiding pitfalls in multi-omics data integration analyses

D. Chicco, C. Angione, Fabio Cumbo, 2023, PLoS Comput. Biol..

Ontology-Based Prediction and Prioritization of Gene Functional Annotations

Marco Masseroli, Davide Chicco, D. Chicco, 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

Software Suite for Gene and Protein Annotation Prediction and Similarity Search

Marco Masseroli, Davide Chicco, D. Chicco, 2015, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification

D. Chicco, Giuseppe Jurman, 2023, BioData Mining.

Genomic Annotation Prediction Based on Integrated Information

Marco Tagliasacchi, Marco Masseroli, Davide Chicco, 2011, CIBB.

Biomolecular annotation prediction through information integration

Marco Tagliasacchi, Marco Masseroli, Davide Chicco, 2011 .

Validation Pipeline for Computational Prediction of Genomics Annotations

Marco Masseroli, Davide Chicco, D. Chicco, 2015, CIBB.

Semantically improved genome-wide prediction of Gene Ontology annotations

Marco Tagliasacchi, Marco Masseroli, Davide Chicco, 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

Computational intelligence analysis of high-risk neuroblastoma patient health records reveals time to maximum response as one of the most relevant factors for outcome prediction.

D. Chicco, P. Uva, R. Luksch, 2023, European journal of cancer.

A statistical comparison between Matthews correlation coefficient (MCC), prevalence threshold, and Fowlkes-Mallows index

D. Chicco, Giuseppe Jurman, 2023, J. Biomed. Informatics.

The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation

D. Chicco, Giuseppe Jurman, Niklas Tötsch, 2021, BioData Mining.

Clinical Feature Ranking Based on Ensemble Machine Learning Reveals Top Survival Factors for Glioblastoma Multiforme

D. Chicco, O. Melaiu, G. Cerono, 2023, J. Heal. Informatics Res..

An Invitation to Greater Use of Matthews Correlation Coefficient in Robotics and Artificial Intelligence

D. Chicco, Giuseppe Jurman, 2022, Frontiers in Robotics and AI.

Ten quick tips for machine learning in computational biology

D. Chicco, 2017, BioData Mining.

An intraday trading model based on Artificial Immune Systems

Marina Resta, Davide Chicco, D. Chicco, 2011, WIRN.

Bioconda: sustainable and comprehensive software distribution for the life sciences

Brent S. Pedersen, James E. Johnson, Alexander S. Rose, 2018, Nature Methods.

Ten quick tips for bioinformatics analyses using an Apache Spark distributed computing environment

D. Chicco, G. Cattaneo, U. F. Petrillo, 2023, PLoS Comput. Biol..

Computational prediction of diagnosis and feature selection on mesothelioma patient health records

D. Chicco, C. Rovelli, 2019, PloS one.

Ten quick tips for fuzzy logic modeling of biomedical systems

D. Chicco, S. Spolaor, M. S. Nobile, 2023, PLoS Comput. Biol..