Category: news

We’re excited to share that our latest paper is now published in Computer Methods and Programs in Biomedicine Update: “Including environmental factors into Multiple Sclerosis relapse prediction models: A comparative study.”

In this work, we investigate how environmental factors, such as air pollution, can contribute to predicting relapses in Multiple Sclerosis (MS). We developed a predictive framework combining longitudinal clinical data with environmental information to forecast relapse occurrence one week ahead, using data from 814 individuals.

The framework explores a range of Machine Learning and Deep Learning approaches, different feature selection strategies, and alternative combinations of clinical and environmental features. Among the evaluated configurations, a Random Forest classifier combined with Backward Feature Selection achieved the best performance, with an Area Under the ROC Curve (AUC) of 0.869 (95% CI: 0.833–0.898). Particulate matter (PM10) and nitrogen dioxide (NO2) also emerged as informative predictors in certain settings.

These findings highlight the potential of integrating longitudinal clinical information and environmental exposures to improve MS relapse prediction. We hope this work will contribute to the development of more accurate predictive models and support further research into the relationship between environmental factors and disease activity.

Congratulations to Elena Marinello, Erica Tavazzi, Enrico Longato, Barbara Di Camillo and all other authors!

The paper is available at: https://doi.org/10.1016/j.cmpbup.2026.100272

We’re excited to share that Matteo Baldan and Elena Marinello were awarded 1st and 3rd place, respectively, in the poster session at the MAQC Society Annual Meeting! 🥳👏

Both projects explored the potential of #MultilayerNetworks in biomedical research, highlighting how these approaches can help us represent and analyze the complexity of biological systems from complementary perspectives.

Matteo presented muxvizpy, a Python library developed to support the analysis and visualization of multilayer networks. Elena presented her work on integrating two molecular data types through network topology, exploring how multilayer network approaches can support the integration of complex biological data.

A special thank you to the MAQC Society, LIGHT S.c.ar.l., and JMP for supporting the event and sponsoring the awards!

Congratulations to Matteo and Elena on this achievement! 🎓🏆

We are happy to share the latest milestones reached by our PhD students, who recently presented the progress of their research activities.

Admission to the Third Year of the PhD Programme

Elena, Matteo, and Piero successfully presented the progress of their research and were admitted to the third year of the PhD programme.

Elena presented the latest developments in her research on phenotype- and patient-specific network representations of multi-omics data for interpretable network medicine (in collaboration with FBK). Her work combines recent advances in network science and machine learning to develop computational approaches for the analysis and interpretation of complex biomedical data.

Matteo presented his research on scalable and interpretable computational methods for large biomedical datasets. His work combines High-Performance Computing (HPC) and Large Language Models (LLMs) to extract knowledge from heterogeneous sources of biomedical data.

Piero presented his project on the inference of bacterial interaction networks from metagenomic data (in collaboration with Eubiome srl). His current work includes the simulation of large-scale complex interaction networks, the benchmarking of state-of-the-art network inference methods, and the development of robust computational pipelines for network reconstruction.

Admission to the Thesis External Evaluation Phase

Gaia and Matteo successfully presented the results of their PhD research and were admitted to the external thesis evaluation phase.

Gaia presented her PhD project on interactive visual analytics for interpretable biomedical network modelling. Her research focuses on designing and developing effective visualization approaches and network models for investigating cell–cell communication from single-cell transcriptomics data, as well as on leveraging interactive visualization to enhance explainable artificial intelligence in the biomedical domain.

Matteo presented the results of his three years of research on complex networks for the analysis of biological systems. His work spans several areas, from the simulation of microbial interaction networks to the development of scalable software libraries for large-scale multilayer networks, and from graph-based machine learning to multilayer network approaches for studying cellular communication in spatial transcriptomics.

Congratulations to Elena, Matteo, Piero, Gaia, and Matteo on reaching these important milestones, and best wishes for the next stages of their PhD journey!