Davide Chicco

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 1049, tel: +390264487916

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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

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].