Grazia Gargano
Research Fellow
Ph.D. Candidate in Computer Science and Mathematics
Research Fellow
Ph.D. Candidate in Computer Science and Mathematics
Department of Biosciences, Biotechnologies and Environment (DBBA), University of Bari Aldo Moro
Department of Mathematics, University of Bari Aldo Moro
Hematology and Cell Therapy Unit, Istituto Tumori Bari "Giovanni Paolo II" - IRCCS
Cutting-edge technologies to decode tumor microenvironment in Multiple Myeloma. Gramegna D, Mondelli P, Pappagallo A, Gargano G, Vegliante MC, Quinto AM, Rossini B, Pasciolla G, Roccaro G, Cea M, Iaccino M, Guarini A, Ciavarella S. Hematological Oncology (2026). in press
Identification of Differentially Expressed Genes in RNA-Seq Data via Semi-Rigid Orthogonal Sparse KL-NMTF. Gargano G, Esposito F, Del Buono N, Ciavarella S, Vegliante MC (2026) BMC Bioinformatics. in press
AI-derived five-gene signature predicts risk in multiple myeloma under bortezomib-based therapy. Gargano G, Pappagallo SA, Quinto AM, Rossini B, Gramegna D, Mondelli P, Vegliante M C, Opinto G, Esposito F, Zaccaria GM, Solli V, Palumbo O, Terragna C, Cavo M, Del Buono N, Guarini A, Ciavarella S. (2025). Scientific reports, 16(1), 919. https://doi.org/10.1038/s41598-025-30527-y
AI in Pediatric Urology: Deep Learning-Based Approach Supporting Posterior Urethral Valves Diagnosis on VCUG Imaging. Russo C, Settembre G, Gargano G, de Biase MS, De Fazio R. (2026). In: Rodolà, E., Galasso, F., Masi, I. (eds) Image Analysis and Processing - ICIAP 2025 Workshops. ICIAP 2025. Lecture Notes in Computer Science, vol 16170. Springer, Cham. https://doi.org/10.1007/978-3-032-11381-8_12
AI-Driven Insights into Microbial Biomarkers for Colorectal Cancer Progression. Gargano G, Settembre G, Del Buono N. Procedia Computer Science, 270, 4936–4945. https://doi.org/10.1016/j.procs.2025.09.620.
Superpixel-based plastic litter detection in UAV hyperspectral imaging using spectral-textural features. Settembre G, Gargano G, Del Buono N. Procedia Computer Science, 270, 4997–5006. https://doi.org/10.1016/j.procs.2025.09.626
Enhanced MRI Brain Tumor Detection and Classification via Topological Data Analysis and Low-Rank Tensor Decomposition. De Benedictis SG, Gargano G, Settembre G. Journal of Computational Mathematics and Data Science, vol. 13 (2024) p.100103. https://doi.org/10.1016/j.jcmds.2024.100103.
A targeted gene signature stratifying mediastinal gray zone lymphoma into classical HL-like or PMBL-like subtypes. Gargano G et al. Haematologica, Vol. 109 No. 11 (2024), pp. 3771-3775. https://doi.org/10.3324/haematol.2024.285266.
Reversal of MYB-dependent suppression of MAFB expression overrides leukaemia phenotype in MLL-rearranged AML. Negri A, Ward C, Bucci A, D'Angelo G, Cauchy P, Radesco A, Ventura AB, Walton DS, Clarke M, Mandriani B, Pappagallo SA, Mondelli P, Liao K, Gargano G et al. Cell Death Dis. 2023 Nov 23;14(11):763. https://doi.org/10.1038/s41419-023-06276-z.
A Decision-tree Approach to Stratify DLBCL Risk Based on Stromal and Immune Microenvironment Determinants. Zaccaria GM, Vegliante MC, Mezzolla G, Stranieri M, Volpe G, Altini N, Gargano G et al. Hemasphere. 2023 Apr 5;7(4):e862. https://doi.org/10.1097/HS9.0000000000000862.
Detecting Anomalies in Marine Data: A Framework for Time Series Analysis. Del Buono N, Esposito F, Gargano G, Selicato L, Taggio N, Ceriola G, and Iasillo D. (2023). In Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13810, pp. 485–500. Springer, Cham. https://doi.org/10.1007/978-3-031-25599-1_36.
NR1H3 (LXRα) is associated with pro-inflammatory macrophages, predicts survival and suggests potential therapeutic rationales in diffuse large b-cell lymphoma. Vegliante MC, Mazzara S, Zaccaria GM, De Summa S, Esposito F, Melle F, Motta G, Sapienza MR, Opinto G, Volpe G, Bucci A, Gargano G et al. Hematol Oncol. 2022; 1- 12. https://doi.org/10.1002/hon.305.
A New Ensemble Method for Detecting Anomalies in Gene Expression Matrices. Selicato L, Esposito F, Gargano G, Vegliante MC, Opinto G, Zaccaria GM, Ciavarella S, Guarini A, Del Buono N. Mathematics 2021, 9, 882. https://doi.org/10.3390/math9080882.
Validation of a Gene Signature Based on Tumor Microenvironment Composition for Risk Stratification of Pediatric Patients with Classical Hodgkin Lymphoma. Angarano R, Muggeo P, Grassi M, Daniele R, Loiotine M, Martino R, Gargano G, Ingravallo G, Bellitti E, Opinto G, Vegliante MC, Ciavarella S, Pappagallo SA, Guarini A, Santoro N. Klinische Pädiatrie. 2025;237(6):385–385. doi:10.1055/s-0045-1812969
A 16-gene signature reflecting tumor microenvironment predicts the risk of multiple myeloma patients treated by bortezomib-based therapies. Gargano G et al. (2023) Clinical Lymphoma, Myeloma and Leukemia 23: S251-S252. https://doi.org/10.1016/S2152-2650(23)02001-3.
Validation of Argo (Automatic record generator for Onco-Hematology), a New App Supporting the Automatic Conversion of Paper-Based Pathology Reports in Standardized Ecrfs. Zaccaria GM, Berloco F, Clemente F, Pappagallo SA, Vegliante MC, Gargano G et al. Blood 2022; 140 (Supplement 1): 10720–10721. https://doi.org/10.1182/blood-2022-158808.
A Digital Gene-Expression Signature Supports Mediastinal Gray Zone Lymphoma Stratification within Classical Hodgkin or Primary Mediastinal B-Cell Lymphoma. Vegliante MC, Gargano G et al. Blood 2022; 140 (Supplement 1): 9312–9313. https://doi.org/10.1182/blood-2022-159478.
Contacts
E-mail: grazia.gargano@uniba.it; grazia.gargano@oncologico.bari.it; gargano.grz@gmail.com
Address: Room 16, Second floor, Dept. of Mathematics, Via E. Orabona, 4, Bari, Italy
Office Tel: +39 080 5443387