- 4 May 2026
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[SSRM] A computational tool for unsupervised variable selection and patient stratification | Roberta Coletti
4 May 2026 - 12:00 - 13:00
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
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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)
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[MatHBioS] Seminar: Trait-variability and competitive dynamics: a pattern formation analysis, (Davide Cusseddu, Politecnico di Torino)
4 May 2026 - 16:15 - 17:15
Room 2.23 - Building IX.Abstract:
Shigesada, Kawasaki, and Teramoto showed that introducing cross-diffusion effects in the spatial Lotka–Volterra competition
model can destabilise the spatially homogeneous equilibrium and generate spatial patterns. In this seminar, I will present recent work in collaboration with Tommaso Lorenzi and Gaetana Gambino, in which we examined the effect of phenotype heterogeneity in competing populations. In particular, in our modelling framework, phenotype diversity affects movement and interactions between individuals.
Motivated by cellular plasticity, we considered a regime of rapid phenotype switching and derived conditions for cross-diffusion- and phenotype-driven instabilities. Finally, I will present numerical simulations to illustrate the role of the phenotype distribution and its impact on competitive outcomes.This Seminar is funded by national funds through the FCT – Fundação para a Ciência e a Tecnologia, I.P., under the scope of the projects \href{https://doi.org/10.54499/UID/00297/2025}{\textcolor{blue}{UID/00297/2025}} and \href{https://doi.org/10.54499/UID/PRR/00297/2025}{\textcolor{blue}{UID/PRR/00297/2025}} (Center for Mathematics and Applications
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