Publications

Journal papers:

1. "Temporal phenotyping and prognostic stratification of patients with sepsis through longitudinal clustering" written with Patrizia Ribino, Maria Mannone, Claudia Di Napoli, Giovanni Paragliola, and Francesca Gasparini was published in the BioData Mining journal.

2. "The DBCV index is more informative than DCSI, CDbw, and VIASCKDE indices for unsupervised clustering internal assessment of concave-shaped and density-based clusters" written with Giuseppe Sabino, Luca Oneto, and Giuseppe Jurman and published in the PeerJ Computer Science journal.

3. "A simple guide to the use of Student’s t-test, Mann-Whitney U test, Chi-squared test, and Kruskal-Wallis test in biostatistics" written with Andrea Sichenze and Giuseppe Jurman and published in the BioData Mining journal.

4. "TaGra: an open Python package for easily generating graphs from data tables through manifold learning" written with Davide Torre and published in the PeerJ Computer Science journal.

5. "DBSCAN and DBCV application to open medical records heterogeneous data for identifying clinically significant clusters of patients with neuroblastoma" written with Luca Oneto and Davide Cangelosi was published in the BioData Mining journal.

6. "Hyperdimensional computing in biomedical sciences: a brief review" written with Fabio Cumbo and published in the PeerJ Computer Science journal.

7. "A teaching proposal for a short course on biomedical data science" written with Vasco Coelho and published in the PLOS Computational Biology journal.

8. "Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an Alzheimer’s disease progression study" written with Patrizia Ribino, Claudia Di Napoli, Giovanni Paragliola, and Francesca Gasparini published in the BioData Mining journal.

9. "Eight quick tips for biologically and medically informed machine learning" written with Luca Oneto and published in the PLOS Computational Biology journal.

10. "The Venus score for the assessment of the quality and trustworthiness of biomedical datasets" written with Alessandro Fabris and Giuseppe Jurman and published in the BioData Mining journal.

11. "Gene signatures for cancer research: a 25-year retrospective and future avenues" written with Wei Liu and Huaqin He and published in the PLOS Computational Biology journal.

12. "Seven quick tips for gene-focused computational pangenomic analysis" written with Vincenzo Bonnici and published in the BioData Mining journal.

13. "Ten quick tips for clinical electroencephalographic (EEG) data acquisition and signal processing" written with Giulia Cisotto and published in the PeerJ Computer Science journal.

14. "Ten quick tips for electrocardiogram (ECG) signal processing" written with Angeliki-Ilektra Karaiskou and Maarten De Vos and published in the PeerJ Computer Science journal.

15. "Identifying prognostic factors for survival in intensive care unit patients with SIRS or sepsis by machine learning analysis on electronic health records" written with Maximiliano Mollura, Alessia Paglialonga, and Riccardo Barbieri and published in the PLOS Digital Health journal.

16. "Ensemble machine learning reveals key features for diabetes duration from electronic health records" written with Gabriel Cerono and published in the PeerJ Computer Science journal.

17. "Ten quick tips for fuzzy logic modeling of biomedical systems" written with Simone Spolaor and Marco S. Nobile and published in the PLOS Computational Biology journal.

18. "Clinical feature ranking based on ensemble machine learning reveals top survival factors for glioblastoma multiforme" written with Gabriel Cerono and Ombretta Melaiu, and published in the Journal of Healthcare Informatics Research.

19. 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" written with Riccardo Haupt, Paolo Uva, Alberto Garaventa, Roberto Luksch and Davide Cangelosi, and published in the European Journal of Cancer.

20. "Ten quick tips for bioinformatics analyses using an Apache Spark distributed computing environment" written with Umberto Ferraro Petrillo and Giuseppe Cattaneo, and published in the PLOS Computational Biology journal.

21. "Ten quick tips for avoiding pitfalls in multi-omics data integration analyses" written with Fabio Cumbo and Claudio Angione, and published in the PLOS Computational Biology journal.

22. "A statistical comparison between Matthews correlation coefficient (MCC), prevalence threshold, and Fowlkes–Mallows index" written with Giuseppe Jurman, and published in the Journal of Biomedical Informatics.

23. "Signature literature review reveals AHCY, DPYSL3, and NME1 as the most recurrent prognostic genes for neuroblastoma" written with Tiziana Sanavia and Giuseppe Jurman, and published in the BioData Mining journal.

24. "Ten simple rules for providing bioinformatics support within a hospital" written with Giuseppe Jurman and published in the BioData Mining journal.

25. "The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification" written with Giuseppe Jurman and published in the BioData Mining journal.

26. "Ten quick tips for computational analysis of medical images" written with Rakesh Shiradkar and published in the PLOS Computational Biology journal.

27. "Eleven quick tips for data cleaning and feature engineering" written with Luca Oneto and Erica Tavazzi and published in the PLOS Computational Biology journal.

28. "Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning" written with Abbas Alameer, Sara Rahmati, and Giuseppe Jurman and published in BioData Mining.

29. "A brief survey of tools for genomic regions enrichment analysis" written with Giuseppe Jurman and published in Frontiers in Bioinformatics.

30. "A survey on publicly available open datasets of electronic health records (EHRs) of patients with neuroblastoma", written with Davide Cangelosi and Gabriel Cerono and published in the Data Science Journal.

31. "The ABC recommendations for validation of supervised machine learning results in biomedical sciences" written with Giuseppe Jurman and published in the Frontiers in Big Data journal.

32. "Ten simple rules for organizing a special session at a scientific conference" written with Philip E. Bourne and published in the PLOS Computational Biology journal.

33. "Nine quick tips for pathway enrichment analysis" written with Giuseppe Agapito and published in the PLOS Computational Biology journal.

34. "An invitation to greater use of Matthews correlation coefficient in robotics and artificial intelligence" written with Giuseppe Jurman and published in the Frontiers in Robotics and AI journal

35. "geoCancerPrognosticDatasetsRetriever, a bioinformatics tool to easily identify cancer prognostic datasets on Gene Expression Omnibus (GEO)" written with Abbas Alameer and published in the Bioinformatics Oxford journal.

36. "A machine learning analysis of health records of patients with chronic kidney disease at risk of cardiovascular disease" written with Christopher A. Lovejoy and Luca Oneto and published in the IEEE Access journal.

37. "The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation" written with Giuseppe Jurman and Matthijs J. Warrens and published in the PeerJ Computer Science journal.

38. "Arterial disease computational prediction and health record feature ranking among patients diagnosed with inflammatory bowel disease" written with Giuseppe Jurman and published in the IEEE Access journal.

39. "The Matthews correlation coefficient (MCC) is more informative than Cohen’s Kappa and Brier score in binary classification assessment" written with Matthijs J. Warrens and Giuseppe Jurman and published in the IEEE Access journal.

40. "The benefits of the Matthews correlation coefficient (MCC) over the diagnostic odds ratio (DOR) in binary classification assessment" written with Giuseppe Jurman and Valery Starovoitov and published in the IEEE Access journal.

41. "Machine learning compared to conventional statistical models for predicting myocardial infarction readmission and mortality: a systematic review" written mainly by Sung Ming Cho and Douglas Lee and published in the Canadian Journal of Cardiology.

42. "An ensemble learning approach for enhanced classification of patients with hepatitis and cirrhosis" written with Giuseppe Jurman and published in the IEEE Access journal.

43. "The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation"  written with Niklas Toetsch and Giuseppe Jurman and published in the BioData Mining journal.

44. "Data analytics and clinical feature ranking of medical records of patients with sepsis" written with Luca Oneto and published in the BioData Mining journal.

45. "Computational intelligence identifies alkaline phosphatase (ALP), alpha-fetoprotein (AFP), and hemoglobin levels as most predictive survival factors for hepatocellular carcinoma" written with Luca Oneto and published in the Health Informatics Journal.

46. "An enhanced Random Forests approach to predict heart failure from small imbalanced gene expression data" written with Luca Oneto and published in the IEEE/ACM Transactions on Computational Biology and Bioinformatics.

47. "Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality" written mainly by Sheojung Shin and Douglas Lee and published in the European Society of Cardiology (ESC) Heart Failure journal.

48. "Survival prediction of patients with sepsis from age, sex, and septic episode number alone" written with Giuseppe Jurman and published in the Scientific Reports journal.

49. "Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone" written by me and Giuseppe Jurman and published in BMC Medical Informatics and Decision Making.

50. "The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation" written by me and Giuseppe Jurman and published in BMC Genomics.

51. "Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen" written with the AstraZeneca-Sanger Drug Synergy Prediction DREAM Challenge consortium and published in Nature Communications.

52. "Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach" written with the ALS Stratification DREAM Challenge consortium and published in Scientific Reports.

53. "Computational prediction of diagnosis and feature selection on mesothelioma patient health records" written by me and Cristina Rovelli, and published on PLOS One.

54. “Bioconda: sustainable and comprehensive software distribution for the life sciences” written mainly by Björn Grüning, Ryan Dale, Andreas Sjödin, Brad A. Chapman, Jillian Rowe, Christopher H. Tomkins-Tinch, Renan Valieris, Johannes Köster, and the Bioconda Team, and published in Nature Methods.

55. "Supervised deep learning embeddings for the prediction of cervical cancer diagnosis" written with  Kelwin Fernandes​, Jaime S. Cardoso, Jessica Fernandes, and published in PeerJ Computer Science.

56. “Novelty indicator for enhanced prioritization of predicted Gene Ontology annotations“  written with Fernando Palluzzi (Istituto Europeo di Oncologia) Marco Masseroli (Dipartimento di Elettronica e Informazione, Politecnico di Milano), IEEE/ACM Transactions on Computational Biology and Bioinformatics, IEEE, 2017. Accepted and in press.

57. “Ontology-based prediction and prioritization of gene functional annotations” written with Marco Masseroli (Dipartimento di Elettronica e Informazione, Politecnico di Milano), IEEE/ACM Transactions on Computational Biology and Bioinformatics, Volume 13, Issue 2, pp. 248 – 260, IEEE, 2016.

58. “Software suite for gene and protein annotation prediction and similarity search” written with Marco Masseroli (Dipartimento di Elettronica e Informazione, Politecnico di Milano), IEEE/ACM Transactions on Computational Biology and Bioinformatics, Volume 12, Issue 4, pp. 837 – 843, IEEE, 2015.

59. “Computational algorithms to predict Gene Ontology annotations” written with Marco Masseroli and Pietro Pinoli (Dipartimento di Elettronica e Informazione, Politecnico di Milano), BMC Bioinformatics, Volume 6, Supplement 6, S4, 2015.

Book chapters:

1. Davide Chicco, Marco Masseroli, "Biological and Medical Ontologies: Protein Ontology (PRO)", Editor(s): Shoba Ranganathan, Michael Gribskov, Kenta Nakai, Christian Schönbach, Encyclopedia of Bioinformatics and Computational Biology, Academic Press, 2019, Pages 832-837, ISBN 9780128114322,.

2. "geneExpressionFromGEO: An R package to facilitate data reading from Gene Expression Omnibus (GEO)", Microarray Data Analysis, pages 187-194, 2021.

3. "Brief survey on machine learning in epistasis", Epistasis, pages 169-179, 2021.

4. "Siamese neural networks: an overview" (article PDF), Artificial Neural Networks (3rd edition), pages 73-94, 2020.

5. “Validation pipeline for computational prediction of genomics annotations”, written with Marco Masseroli (Dipartimento di Elettronica e Informazione – DEI, Politecnico di Milano), Computational Intelligence Methods for Bioinformatics and Biostatistics, Lecture Notes in Computer Science (LNCS), Vol. 9874, pp. 233-244, Springer. Extended version of the paper entitled “Validation procedures for predicted Gene Ontology annotations”,  Proceedings of CIBB 2015 – the Twelfth International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Naples, Italy, 10 – 12 September 2015.

6. “Extended Spearman and Kendall coefficients for gene annotation list correlation”, written with Eleonora Ciceri and Marco Masseroli (Dipartimento di Elettronica e Informazione – DEI, Politecnico di Milano), Computational Intelligence Methods for Bioinformatics and Biostatistics, Lecture Notes in Computer Science (LNCS), Vol. 8623, pp. 1-12, Springer. Extended version of the paper entitled “Correlation of Gene Function Annotation Lists through Enhanced Spearman and Kendall Measures”,  Proceedings of CIBB 2014 – the Eleventh International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, Cambridge, Great Britain, 26 – 28 June 2014.

7. “Weighting scheme methods for enhanced genomic annotation prediction” written with Marco Masseroli and Pietro Pinoli (Dipartimento di Elettronica e Informazione – DEI, Politecnico di Milano), Computational Intelligence Methods for Bioinformatics and Biostatistics, Lecture Notes in Computer Science (LNCS), Vol. 8452, pag. 76-89, Springer. Extended version of the scientific paper entitled “Enhanced Probabilistic Latent Semantic Analysis with Weighting Schemes to Predict Genomic Annotations”  and presented at  CIBB2013 – the 10th International Meeting On Computational Intelligence Methods For Bioinformatics And Biostatistics, Nice, Cote d’Azur, France 20 – 22 June 2013.

8. “Genomic annotation prediction based on integrated information” written with Marco Masseroli and Marco Tagliasacchi (Dipartimento di Elettronica e Informazione – DEI, Politecnico di Milano), Computational Intelligence Methods for Bioinformatics and Biostatistics, Lecture Notes in Computer Science (LNCS), Vol. 7548, Springer. Editors: Biganzoli, E.; Vellido, A.; Ambrogi, F.; Tagliaferri, R. Extended version of the scientific paper entitled “Biomolecular annotation prediction through information integration”  and presented at  CIBB2011 – 8th International Meeting On Computational Intelligence Methods For Bioinformatics And Biostatistics, 30 June, 1° July, 2 July 2011, Gargnano Sul Garda, Lombardia, Italy.