Davide Chicco

16.3k total citations · 8 hit papers
70 papers, 9.5k citations indexed

About

Davide Chicco is a scholar working on Molecular Biology, Artificial Intelligence and Health Information Management. According to data from OpenAlex, Davide Chicco has authored 70 papers receiving a total of 9.5k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Molecular Biology, 29 papers in Artificial Intelligence and 9 papers in Health Information Management. Recurrent topics in Davide Chicco's work include Gene expression and cancer classification (21 papers), Bioinformatics and Genomic Networks (16 papers) and Biomedical Text Mining and Ontologies (12 papers). Davide Chicco is often cited by papers focused on Gene expression and cancer classification (21 papers), Bioinformatics and Genomic Networks (16 papers) and Biomedical Text Mining and Ontologies (12 papers). Davide Chicco collaborates with scholars based in Canada, Italy and United States. Davide Chicco's co-authors include Giuseppe Jurman, Matthijs J. Warrens, Niklas Tötsch, Marco Masseroli, Peter Sadowski, Pierre Baldi, Luca Oneto, Pietro Pinoli, Cristina Rovelli and Giuseppe Agapito and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

In The Last Decade

Davide Chicco

65 papers receiving 9.2k citations

Hit Papers

The advantages of the Matthews correlation coefficient (M... 2017 2026 2020 2023 2020 2021 2017 2021 2020 1000 2.0k 3.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation
    2020 3.4k citations Davide Chicco, Giuseppe Jurman BMC Genomics profile →
  2. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation
    2021 2.6k citations Davide Chicco, Matthijs J. Warrens et al. PeerJ Computer Science profile →
  3. Ten quick tips for machine learning in computational biology
    2017 588 citations Davide Chicco BioData Mining profile →
  4. The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation
    2021 537 citations Davide Chicco, Giuseppe Jurman et al. BioData Mining profile →
  5. Siamese Neural Networks: An Overview
    2020 372 citations Davide Chicco profile →
  6. Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone
    2020 348 citations Davide Chicco, Giuseppe Jurman profile →
  7. The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen’s Kappa and Brier Score in Binary Classification Assessment
    2021 267 citations Davide Chicco, Matthijs J. Warrens et al. IEEE Access profile →
  8. The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification
    2023 259 citations Davide Chicco, Giuseppe Jurman BioData Mining profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Davide Chicco Canada 22 2.5k 1.3k 660 652 642 70 9.5k
Giuseppe Jurman Italy 28 2.3k 0.9× 1.7k 1.3× 660 1.0× 626 1.0× 619 1.0× 97 9.9k
Nan Liu China 48 2.2k 0.9× 1.1k 0.8× 791 1.2× 335 0.5× 435 0.7× 577 10.0k
Naomi Altman United States 59 2.3k 0.9× 2.8k 2.2× 551 0.8× 655 1.0× 924 1.4× 153 14.9k
Guolin Ke China 13 2.5k 1.0× 613 0.5× 298 0.5× 805 1.2× 708 1.1× 22 8.0k
Su‐In Lee United States 26 2.1k 0.8× 1.8k 1.4× 727 1.1× 336 0.5× 275 0.4× 65 8.7k
Sotiris Kotsiantis Greece 32 4.9k 2.0× 576 0.4× 531 0.8× 758 1.2× 976 1.5× 185 11.3k
Gareth James United States 27 2.8k 1.1× 910 0.7× 357 0.5× 722 1.1× 728 1.1× 60 13.8k
Taifeng Wang China 15 2.3k 0.9× 647 0.5× 268 0.4× 716 1.1× 574 0.9× 33 7.4k
Igor Kononenko Slovenia 26 3.7k 1.5× 855 0.7× 489 0.7× 339 0.5× 1.1k 1.8× 117 7.9k
Qi Meng China 9 1.9k 0.8× 533 0.4× 265 0.4× 731 1.1× 444 0.7× 21 6.7k

Countries citing papers authored by Davide Chicco

Since Specialization
Citations

This map shows the geographic impact of Davide Chicco's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Davide Chicco with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Davide Chicco more than expected).

Fields of papers citing papers by Davide Chicco

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Davide Chicco. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Davide Chicco. The network helps show where Davide Chicco may publish in the future.

Co-authorship network of co-authors of Davide Chicco

This figure shows the co-authorship network connecting the top 25 collaborators of Davide Chicco. A scholar is included among the top collaborators of Davide Chicco based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Davide Chicco. Davide Chicco is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Oneto, Luca & Davide Chicco. (2025). Nine quick tips for trustworthy machine learning in the biomedical sciences. PLoS Computational Biology. 21(10). e1013624–e1013624. 1 indexed citations
2.
3.
Chicco, Davide, Luca Oneto, & Davide Cangelosi. (2025). DBSCAN and DBCV application to open medical records heterogeneous data for identifying clinically significant clusters of patients with neuroblastoma. BioData Mining. 18(1). 40–40. 2 indexed citations
4.
Ribino, Patrizia, Claudia Di Napoli, Giovanni Paragliola, Davide Chicco, & Francesca Gasparini. (2025). Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an Alzheimer’s disease progression study. BioData Mining. 18(1). 26–26. 1 indexed citations
5.
Oneto, Luca & Davide Chicco. (2025). Eight quick tips for biologically and medically informed machine learning. PLoS Computational Biology. 21(1). e1012711–e1012711. 5 indexed citations
6.
Mollura, Maximiliano, Davide Chicco, Alessia Paglialonga, & Riccardo Barbieri. (2024). Identifying prognostic factors for survival in intensive care unit patients with SIRS or sepsis by machine learning analysis on electronic health records. SHILAP Revista de lepidopterología. 3(3). e0000459–e0000459. 3 indexed citations
7.
Chicco, Davide, Riccardo Haupt, Alberto Garaventa, et al.. (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. 113291–113291. 5 indexed citations
8.
Chicco, Davide, Simone Spolaor, & Marco S. Nobile. (2023). Ten quick tips for fuzzy logic modeling of biomedical systems. PLoS Computational Biology. 19(12). e1011700–e1011700. 3 indexed citations
9.
Chicco, Davide & Giuseppe Jurman. (2023). Ten simple rules for providing bioinformatics support within a hospital. BioData Mining. 16(1). 6–6. 5 indexed citations
10.
Chicco, Davide, Luca Oneto, & Erica Tavazzi. (2022). Eleven quick tips for data cleaning and feature engineering. PLoS Computational Biology. 18(12). e1010718–e1010718. 29 indexed citations
11.
Chicco, Davide, et al.. (2022). Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning. BioData Mining. 15(1). 28–28. 3 indexed citations
12.
Chicco, Davide & Luca Oneto. (2021). Data analytics and clinical feature ranking of medical records of patients with sepsis. BioData Mining. 14(1). 12–12. 12 indexed citations
13.
Chicco, Davide, Christopher A. Lovejoy, & Luca Oneto. (2021). A Machine Learning Analysis of Health Records of Patients With Chronic Kidney Disease at Risk of Cardiovascular Disease. IEEE Access. 9. 165132–165144. 16 indexed citations
14.
Chicco, Davide, Matthijs J. Warrens, & Giuseppe Jurman. (2021). The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen’s Kappa and Brier Score in Binary Classification Assessment. IEEE Access. 9. 78368–78381. 267 indexed citations breakdown →
15.
16.
Austin, Peter C., Heather J. Ross, Husam Abdel‐Qadir, et al.. (2020). Machine Learning vs. Conventional Statistical Models for Predicting Heart Failure Readmission and Mortality. ESC Heart Failure. 8(1). 106–115. 109 indexed citations
17.
Chicco, Davide & Luca Oneto. (2020). An Enhanced Random Forests Approach to Predict Heart Failure From Small Imbalanced Gene Expression Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 18(6). 2759–2765. 14 indexed citations
18.
Chicco, Davide & Giuseppe Jurman. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics. 21(1). 6–6. 3395 indexed citations breakdown →
19.
Chicco, Davide & Giuseppe Jurman. (2020). Survival prediction of patients with sepsis from age, sex, and septic episode number alone. Scientific Reports. 10(1). 17156–17156. 38 indexed citations
20.
Chicco, Davide & Michael M. Hoffman. (2017). Genome Informatics 2016. Genome biology. 18(1). 5–5. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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