- 2010: Ph.D. in Computer Science, University of Salerno, Italy.
- 2006: M.Sc. in Computer Science, University of Salerno, Italy.
- 2019-current: Research Scientist, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Saudi Arabia.
- 2013-2019: Senior Postdoctoral Researcher, Systems and Synthetic Biology lab, Telethon Institute of Genetics and Medicine (TIGEM), Naples, Italy.
- 2010-2012: Postdoctoral Researcher, Dpt. of Computer Science, University of Salerno, Salerno, Italy.
Honors and Awards
- 2019: Veronesi Fellowship, Fondazione Umberto Veronesi, Milan Italy.
- 2012: Fellowship from University of Salerno, Salerno, Italy.
Francesco 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 the 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.
Francesco's main research area includes the application of data analysis and machine learning techniques to large omics data. He has been investigating the transcriptomic effects of drug treatments and how they can be analyzed to infer drug mechanisms of action, with applications to drug discovery and repositioning. He also researched related methodologies in the context of drug-facilitated production of human-induced pluripotent stem cells (hIPS) and gene-drug prioritization in precision medicine. His other research areas include metabolic simulations with applications to rare genetic disorders and the development of bioinformatics data analysis and management tools.
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. (*) The 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 Python with 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 approach for human cell cycle gene expression data analysis”. In: International Journal of Approximate Reasoning 47 (2008), pp. 70–84. ISSN: 0888613X.