Mario Rosario Guarracino
- Chief Research Fellow:HSE Campus in Nizhny Novgorod / Laboratory of Algorithms and Technologies for Networks Analysis (Nizhny Novgorod)
- Mario Rosario Guarracino has been at HSE University since 2014.
Publications17
- Book Computational Management Science. Network Analysis and Applications / Сост.: P. M. Pardalos, V. A. Kalyagin, M. R. Guarracino. Springer, 2024.
- Article Moosaei H., Khosravi S., Bazikar F., Hladík M., Guarracino M. R. A Novel Method for Solving Universum Twin Bounded Support Vector Machine in the Primal Space // Annals of Mathematics and Artificial Intelligence. 2023 doi
- Article Antonelli L., Polverino F., Albu A., Hada A., Asteriti I. A., Degrassi F., Guarguaglini G., Maddalena L., Mario R. Guarracino. ALFI: Cell cycle phenotype annotations of label-free time-lapse imaging data from cultured human cells // Scientific data. 2023. Vol. 10. Article 677. doi
- Article Giordano M., Maddalena L., Manzo M., Guarracino M. R. Adversarial attacks on graph-level embedding methods: a case study // Annals of Mathematics and Artificial Intelligence. 2023. Vol. 91. P. 259-285. doi
- Article Maddalena L., Granata I., Giordano M., Manzo M., Guarracino M. R. Integrating Different Data Modalities for the Classification of Alzheimer’s Disease Stages // SN Computer Science. 2023. Vol. 4. Article 249. doi
- Chapter Granata I., Giordano M., Manzo M., Guarracino M. R. Network-Based Computational Modeling to Unravel Gene Essentiality, in: Trends in Biomathematics: Modeling Epidemiological, Neuronal, and Social Dynamics.. Springer, 2023. doi P. 29-56. doi
- Article Manzo M., Giordano M., Maddalena L., Guarracino M. R., Granata I. Novel Data Science Methodologies for Essential Genes Identification Based on Network Analysis // Studies in Computational Intelligence. 2023. Vol. 1084. P. 117-145. doi
- Article Bombelli I., Manipur I., Guarracino M. R., Ferraro M. B. Representing ensembles of networks for fuzzy cluster analysis: a case study // Data Mining and Knowledge Discovery. 2023 doi (in press)
- Article Maddalena L., Antonelli L., Albu A., Hada A., Guarracino M. R. Artificial Intelligence for Cell Segmentation, Event Detection, and Tracking for Label-Free Microscopy Imaging // Algorithms. 2022. Vol. 15. No. 9. Article 313. doi
- Article Manipur I., Manzo M., Granata I., Giordano M., Maddalena L., Guarracino M. R. Netpro2vec: a Graph Embedding Framework for Biomedical Applications // IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2022. Vol. 19. No. 2. P. 729-740. doi
- Article Granata I., Manipur I., Giordano M., Maddalena L., Guarracino M. R. TumorMet: A repository of tumor metabolic networks derived from context-specific Genome-Scale Metabolic Models // Scientific data. 2022. Vol. 9. Article 607. doi
- Article Granata I., Manzo M., Kusumastuti A., Guarracino M. R. Learning from Metabolic Networks: Current Trends and Future Directions for Precision Medicine // Current Medicinal Chemistry. 2021. Vol. 28. No. 32. P. 6619-6653. doi
- Article Žilinskas J., Lančinskas A., Guarracino M. R. Pooled testing with replication as a mass testing strategy for the COVID-19 pandemics // Scientific Reports. 2021. Vol. 11. Article 3459. doi
- Article Manipur I., Granata I., Maddalena L., Guarracino M. R. Clustering analysis of tumor metabolic networks // BMC Bioinformatics. 2020. Vol. 21. No. 10. P. 349. doi
- Article Gokulnath P., de Cristofaro T., Manipur I., Di Palma A. A., Guarracino M. R., Zannini M. Long Non-Coding RNA HAND2-AS1 Acts as a Tumor Suppressor in High-Grade Serous Ovarian Carcinoma // International Journal of Molecular Sciences. 2020. Vol. 21. No. 11. P. 1-17. doi
- Article Viola M., Sangiovanni M., Toraldo G., Guarracino M. R. Semi-supervised generalized eigenvalues classification // Annals of Operations Research. 2019. Vol. 276. No. 1-2. P. 249-266. doi
- Article Guarracino M. R., Maddalena L. SDI+: A Novel Algorithm for Segmenting Dermoscopic Images // IEEE Journal of Biomedical and Health Informatics. 2018. Vol. 22. No. 2. P. 481-488. doi
Group Testing Method Developed for COVID-19
Researchers Mario Guarracino from the HSE Laboratory of Algorithms and Technologies for Networks Analysis in Nizhny Novgorod and Julius Žilinskas and Algirdas Lančinskas from Vilnius University, have proposed a new method of testing for COVID-19. This group method allows results to be obtained 13 times faster as compared to individual testing of each sample. The research paper was published in the journal Scientific Reports.