Davide Chicco (0000-0001-9655-7142)

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Davide Chicco obtained his Bachelor of Science and Master of Science degrees in computer science at Università di Genova (Genoa, Italy) respectively in 2007 and 2010. He then started the PhD program in computer engineering at Politecnico di Milano university (Milan, Italy), where he graduated in spring 2014. He also spent a semester as visiting doctoral scholar at University of California Irvine (USA). From September 2014 to September 2018, he has been a post-doctoral researcher at the Princess Margaret Cancer Centre and guest at University of Toronto. From September 2018 to January 2021, he was a scientific researcher first at the Peter Munk Cardiac Centre and then at the Krembil Research Institute (Toronto, Ontario, Canada). He then joined University of Toronto. His research topics focus upon mainly machine learning algorithms applied to biomedical informatics.

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Universita' di Milano Bicocca: Milan, IT

2023-05 to present

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Davide Chicco

https://orcid.org/0000-0001-9655-7142

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Employment (5)

Universita' di Milano Bicocca: Milan, IT

University of Toronto: Toronto, Ontario, CA

Krembil Research Institute: Toronto, Ontario, CA

Peter Munk Cardiac Centre: Toronto, Ontario, CA

Princess Margaret Cancer Centre: Toronto, Ontario, CA

Education and qualifications (4)

University of California Irvine: Irvine, California, US

Politecnico di Milano: Milan, IT

Università di Genova: Genoa, IT

Università di Genova: Genoa, IT

Works (50 of 69)

Eight quick tips for biologically and medically informed machine learning

The Venus score for the assessment of the quality and trustworthiness of biomedical datasets

Gene signatures for cancer research: A 25-year retrospective and future avenues

Seven quick tips for gene-focused computational pangenomic analysis

Ten quick tips for clinical electroencephalographic (EEG) data acquisition and signal processing

Ten quick tips for electrocardiogram (ECG) signal processing

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

Identifying prognostic factors for survival in intensive care unit patients with SIRS or sepsis by machine learning analysis on electronic health records

Ten quick tips for fuzzy logic modeling of biomedical systems

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

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

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

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

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

Ten simple rules for providing bioinformatics support within a hospital

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

Ten quick tips for computational analysis of medical images

Eleven quick tips for data cleaning and feature engineering

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

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

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

Nine quick tips for pathway enrichment analysis

An invitation to greater use of Matthews correlation coefficient in robotics and artificial intelligence

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

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

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

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

Arterial disease computational prediction and health record feature ranking among patients diagnosed with inflammatory bowel disease

The Matthews correlation coefficient (MCC) is more informative than Cohen’s Kappa and Brier score in binary classification assessment

The benefits of the Matthews correlation coefficient (MCC) over the diagnostic odds ratio (DOR) in binary classification assessment

Brief survey on machine learning in epistasis

An ensemble learning approach for enhanced classification of patients with hepatitis and cirrhosis

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

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

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

An enhanced Random Forests approach to predict heart failure from small imbalanced gene expression data

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

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

Siamese neural networks: an overview

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

Editorial: Artificial intelligence bioinformatics: development and application of tools for omics and inter-omics studies

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

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

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

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

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

Bioconda: sustainable and comprehensive software distribution for the life sciences

Supervised deep learning embeddings for the prediction of cervical cancer diagnosis

Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach

A cancer pharmacogenomic screen powering crowd-sourced advancement of drug combination prediction