Statistics and Risk Management Seminar

Department of Mathematics, NOVA MATH/FCT NOVA

Title: A computational tool for unsupervised variable selection and patient stratification

Speaker: Roberta Coletti, NOVA Math

Date | Time: May 04, 2026 | 12:00

Location: VII-1.11

Teams: https://teams.microsoft.com/meet/36124291051668?p=9wzuxJVsdpJQkGb9ww

Abstract: In this study, we developed TRIM-IT, a computational framework for data-driven patient stratification and biomarker discovery based on high-dimensional omics data. To address the challenges associated with high dimensionality, TRIM-IT first performs unsupervised variable selection to reduce data complexity while preserving dataset structure. The selected variables are then used for unsupervised clustering and downstream analyses to characterize the identified patient groups. Applied to glioblastoma transcriptomics data, TRIM-IT uncovered three distinct patient clusters associated with tumor histology, significantly different survival outcomes, and molecular profiles suggestive of potential biomarker candidates. 

Short Bio: Roberta Coletti studied mathematics at Sapienza University of Rome. She completed a PhD in mathematics at the University of Trento, with a thesis on ordinary differential equation models of prostate cancer immunotherapy. Between 2021 and 2024, she was a researcher at the Center for Mathematics and Applications at NOVA University of Lisbon. Her work focuses on identifying molecular biomarkers for glioma cancer by analyzing large multi-omics datasets using statistical and machine learning methods. 

Organizers: Isabel Natário and Mina Norouzirad and Marta Lopes

LogosTodos.JPG

This work is funded by national funds through the FCT – Fundação para a Ciência e a Tecnologia, I.P., under the scope of the projects UID/00297/2025 (https://doi.org/10.54499/UID/00297/2025) and UID/PRR/00297/2025 (https://doi.org/10.54499/UID/PRR/00297/2025) (Center for Mathematics and Applications - NOVA Math)