Davide Chicco
e-mail: davide.chicco-at-unimib.it
DISCo (Department of Informatics, Systems and Communication)
University of Milano-Bicocca
Viale Sarca 336, Building U14
Room 1019, tel: +39 0264487905
BIOGRAPHY
Davide Chicco received his Bachelor of Science and Master of Science degrees in computer science from the Università di Genova (Genoa, Italy) and then a Ph.D. degree in computer engineering from the Politecnico di Milano University (Milan, Italy) in 2014, spending a doctoral semester as a visiting doctoral scholar with the University of California Irvine (California USA). He then moved to Toronto (Ontario, Canada) where he worked as a postdoctoral researcher and as a scientific associate at the Princess Margaret Cancer Centre, the Peter Munk Cardiac Centre, the Krembil Research Institute, and then at the University of Toronto. He joined Università di Milano-Bicocca as an assistant professor in summer 2023.
His research interests include machine learning applied to health informatics (in particular, data from electronic health records), bioinformatics (data of gene expression), and statistical metrics to assess classification and regression, with a particular interest in
the Matthews correlation coefficient.
He has been the general chair of the CIBB 2021 conference, and has served as associate editor for the BMC Bioinformatics and the PeerJ
Computer Science journals.
He’s the main author of the scientific article “The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation.
PUBLICATIONS
2024 Clustering of longitudinal Clinical Dementia Rating data to identify predictors of Alzheimer's disease progression Ribino, P., Paragliola, G., Napoli, C., Mannone, M., Chicco, D., Gasparini, F. (2024). Clustering of longitudinal Clinical Dementia Rating data to identify predictors of Alzheimer's disease progression. In 15th International Conference on Emerging Ubiquitous Systems and Pervasive Networks / 14th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare EUSPN/ICTH 2024 (pp.326-333) [10.1016/j.procs.2024.11.117].2024 Analyzing trajectories of clinical markers in patients with sepsis through multivariate longitudinal clustering Ribino, P., Mannone, M., Di Napoli, C., Paragliola, G., Chicco, D., Gasparini, F. (2024). Analyzing trajectories of clinical markers in patients with sepsis through multivariate longitudinal clustering. In HC@AIxIA 2024 Artificial Intelligence For Healthcare 2024 Proceedings of the 3rd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2024) co-located with the 23rd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2024) (pp.247-256). CEUR-WS.2024 Gene signatures for cancer research: A 25-year retrospective and future avenues Liu, W., He, H., Chicco, D. (2024). Gene signatures for cancer research: A 25-year retrospective and future avenues. PLOS COMPUTATIONAL BIOLOGY, 20(10) [10.1371/journal.pcbi.1012512].2024 Ten quick tips for electrocardiogram (ECG) signal processing Chicco, D., Karaiskou, A., De Vos, M. (2024). Ten quick tips for electrocardiogram (ECG) signal processing. PEERJ. COMPUTER SCIENCE., 10, 1-22 [10.7717/PEERJ-CS.2295].2024 Ten quick tips for clinical electroencephalographic (EEG) data acquisition and signal processing Cisotto, G., Chicco, D. (2024). Ten quick tips for clinical electroencephalographic (EEG) data acquisition and signal processing. PEERJ. COMPUTER SCIENCE., 10, 1-25 [10.7717/PEERJ-CS.2256].2024 Seven quick tips for gene-focused computational pangenomic analysis Bonnici, V., Chicco, D. (2024). Seven quick tips for gene-focused computational pangenomic analysis. BIODATA MINING, 17(1) [10.1186/s13040-024-00380-2].2024 Identifying prognostic factors for survival in intensive care unit patients with SIRS or sepsis by machine learning analysis on electronic health records Mollura, M., Chicco, D., Paglialonga, A., Barbieri, R. (2024). Identifying prognostic factors for survival in intensive care unit patients with SIRS or sepsis by machine learning analysis on electronic health records. PLOS DIGITAL HEALTH, 3(3) [10.1371/journal.pdig.0000459].2024 Ensemble machine learning reveals key features for diabetes duration from electronic health records Cerono, G., Chicco, D. (2024). Ensemble machine learning reveals key features for diabetes duration from electronic health records. PEERJ. COMPUTER SCIENCE., 10 [10.7717/peerj-cs.1896].2024 Clinical Feature Ranking Based on Ensemble Machine Learning Reveals Top Survival Factors for Glioblastoma Multiforme Cerono, G., Melaiu, O., Chicco, D. (2024). Clinical Feature Ranking Based on Ensemble Machine Learning Reveals Top Survival Factors for Glioblastoma Multiforme. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH, 8(1 (March 2024)), 1-18 [10.1007/s41666-023-00138-1].
2023 Exploratory analysis of longitudinal data of patients with dementia through unsupervised techniques Ribino, P., Di Napoli, C., Paragliola, G., Serino, L., Gasparini, F., Chicco, D. (2023). Exploratory analysis of longitudinal data of patients with dementia through unsupervised techniques. In 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) (pp.67-87). CEUR-WS.2023 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 Chicco, D., Haupt, R., Garaventa, A., Uva, P., Luksch, R., Cangelosi, D. (2023). 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. EUROPEAN JOURNAL OF CANCER, 193(November 2023) [10.1016/j.ejca.2023.113291].2023 Ten quick tips for fuzzy logic modeling of biomedical systems Chicco, D., Spolaor, S., Nobile, M. (2023). Ten quick tips for fuzzy logic modeling of biomedical systems. PLOS COMPUTATIONAL BIOLOGY, 19(12) [10.1371/journal.pcbi.1011700].2023 Ten quick tips for bioinformatics analyses using an Apache Spark distributed computing environment Chicco, D., Ferraro Petrillo, U., Cattaneo, G. (2023). Ten quick tips for bioinformatics analyses using an Apache Spark distributed computing environment. PLOS COMPUTATIONAL BIOLOGY, 19(7), 1-16 [10.1371/journal.pcbi.1011272].2023 Ten quick tips for avoiding pitfalls in multi-omics data integration analyses Chicco, D., Cumbo, F., Angione, C. (2023). Ten quick tips for avoiding pitfalls in multi-omics data integration analyses. PLOS COMPUTATIONAL BIOLOGY, 19(7), 1-15 [10.1371/journal.pcbi.1011224].2023 A statistical comparison between Matthews correlation coefficient (MCC), prevalence threshold, and Fowlkes-Mallows index Chicco, D., Jurman, G. (2023). A statistical comparison between Matthews correlation coefficient (MCC), prevalence threshold, and Fowlkes-Mallows index. JOURNAL OF BIOMEDICAL INFORMATICS, 144, 1-7 [10.1016/j.jbi.2023.104426].2023 Signature literature review reveals AHCY, DPYSL3, and NME1 as the most recurrent prognostic genes for neuroblastoma Chicco, D., Sanavia, T., Jurman, G. (2023). Signature literature review reveals AHCY, DPYSL3, and NME1 as the most recurrent prognostic genes for neuroblastoma. BIODATA MINING, 16(1) [10.1186/s13040-023-00325-1].2023 The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification Chicco, D., Jurman, G. (2023). The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification. BIODATA MINING, 16(1), 1-23 [10.1186/s13040-023-00322-4].2023 Ten simple rules for providing bioinformatics support within a hospital Chicco, D., Jurman, G. (2023). Ten simple rules for providing bioinformatics support within a hospital. BIODATA MINING, 16(1), 1-12 [10.1186/s13040-023-00326-0].2023 Ten quick tips for computational analysis of medical images Chicco, D., Shiradkar, R. (2023). Ten quick tips for computational analysis of medical images. PLOS COMPUTATIONAL BIOLOGY, 19(1), 1-14 [10.1371/journal.pcbi.1010778].