Francesco Napolitano

Research Scientists


​Frrancesco is a Computer Scientist with a background in Mathematical Models and Machine Learning. He focused his M.Sc.
(University of Salerno, SA, Italy) and Ph.D. (University of Salerno and University of California, Irvine, CA, USA) on
supervised and unsupervised data analysis, particularly for clustering of complex, high-dimensional data. He developed
and applied data analysis techniques to problems from different fields, including fault detection in avionics and computational
pharmacology, before focusing on bioinformatics and systems biology.

Selected Publications

1. ​F. Napolitano. “repo: an R package for data-centered management of bioinformatic pipelines”. In: BMC Bioinformatics 18 (2017), p. 112. ISSN: 1471-2105. DOI: 10.1186/s12859-017-1510-6.
2. F. Napolitano, D. Carrella, B. Mandriani, S. Pisonero, F. Sirci, D. Medina, N. Brunetti-Pierri, and D. di Bernardo. “gene2drug: a Computational Tool for Pathway-based Rational Drug Repositioning”. In: Bioinformatics (Dec. 2017).
3. F. Napolitano, F. Sirci, D. Carrella, and D. di Bernardo. “Drug-set enrichment analysis: a novel tool to investigate drug mode of action”. In: Bioinformatics 32.2 (2016), pp. 235–241. ISSN: 1367-4803, 1460-2059.
4. D. Carrella, F. Napolitano, R. Rispoli, M. Miglietta, A. Carissimo, L. Cutillo, F. Sirci, F. Gregoretti, and D. di Bernardo. “Mantra 2.0: an online collaborative resource for drug mode of action and repurposing by network analysis”. In: Bioinformatics 30.12 (2014), pp. 1787–1788.
5. P. Galdi, F. Napolitano, and R. Tagliaferri. “A comparison between Affinity Propagation and assessment based methods in finding the best number of clusters”. In: Computational Intelligence Methods for Bioinformatics and Biostatistics. Ed. by C. Di Serio, P. Liò, A. Nonis, and R. Tagliaferri. Lecture Notes in Bioinformatics. Springer International Publishing, 2014. (*) First two authors equally contributed.
6. F. Napolitano, R. Tagliaferri, and P. Baldi. “An Adaptive Reference Point Approach to Efficiently Search Large Chemical Databases”. In: Recent Advances of Neural Network Models and Applications. Ed. by S. Bassis, A. Esposito, and F. C. Morabito. Springer International Publishing,
2014, pp. 63–74.
7. F. Napolitano, R. Mariani-Costantini, and R. Tagliaferri. “Bioinformatic pipelines in Pythonwith Leaf”. In: BMC Bioinformatics 14.1 (2013), pp. 1–14. ISSN: 1471-2105.
8. F. Napolitano, Y. Zhao, V. M. Moreira, R. Tagliaferri, J. Kere, M. D’Amato, and D. Greco. “Drug Repositioning: A Machine-Learning Approach through Data Integration”. In: Journal of Cheminformatics 5.1 (2013), p. 30. ISSN: 1758-2946.
9. F. Napolitano, R. Tagliaferri, and P. Baldi. “A scalable reference-point based algorithm to efficiently search large chemical databases”. In: The 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010, pp. 1–6. ISBN: 978-1-4244-6916-1.
10. F. Napolitano, G. Raiconi, R. Tagliaferri, A. Ciaramella, A. Staiano, and G. Miele. “Clustering and visualization approaches for human cell cycle gene expression data analysis”. In: International Journal of Approximate Reasoning 47 (2008), pp. 70–84. ISSN: 0888613X.