معرفی
Jessica Gliozzo is a research fellow in bioinformatics at the IRCCS Ca' Granda – Ospedale Maggiore Policlinico of Milan and collaborates with the AnacletoLab (Bioinformatics and Computational Biology laboratory). She holds a BSc in Medical Biotechnology from the University of Milan (2014) and an MSc in Molecular Biotechnology and Bioinformatics from the same institution (2016).
Her primary research interests include:
- Bioinformatics
- Machine Learning
- Deep Learning applications in genomics
- Analysis of Next Generation Sequencing data
Dr. Gliozzo's current research focuses on the analysis of Next Generation Sequencing (NGS) data from patients affected by primitive hematopoietic proliferative disorders of the skin. She is actively developing deep learning neural networks for the prediction and prioritization of regulatory variants in human diseases and creating network-based semi-supervised models to predict patients' clinical outcomes by leveraging heterogeneous biological data.
Her recent publications demonstrate a strong trend toward integrating multi-omics data, developing knowledge graphs for RNA interactions, and applying advanced machine learning techniques to clinical problems, particularly in cancer research and genomic analysis. Her work spans computational biology, medical informatics, and artificial intelligence applications in healthcare, with a particular emphasis on dimensionality reduction techniques, network-based approaches, and resource-efficient computational methods.
- Network-based patient similarity analysis
- RNA knowledge graph construction
- Resource-limited medical image processing
- Large language models for protein science
- Multi-omics data integration
Dr. Gliozzo has maintained extensive collaborations with researchers including Giorgio Valentini, Elena Casiraghi, Marco Mesiti, and Alessandro Petrini across numerous publications in reputable journals such as BMC Bioinformatics, Scientific Reports, and Frontiers in Bioinformatics, demonstrating consistent research productivity from 2017 through 2025.
