Category: important_news

🧬 Our group had a great time participating in BITS2026, the 22nd Annual Conference of the Bioinformatics Italian Society, held in Padua from May 27–29, 2026.

The conference provided an excellent opportunity to share our latest research, exchange ideas with colleagues, and connect with the vibrant Italian and international bioinformatics community.

A special highlight was the 2nd International BioHackathon, organized by our group members Giulia Cesaro and Giacomo Bruzzo in collaboration with Young BITS, CINI Young InfoLife, and ISCB RSG Italy. Bringing together talented young scientists from across Italy and Europe, the BioHackathon challenged participants to decode biological networks from omics data through collaboration, creativity, and innovation. The event showcased the enthusiasm and scientific excellence of the next generation of computational biologists.

🎤 We were also proud to see our PhD student Matteo Andriolo present our work on ArchetypalC, a scalable and high-performance C++ implementation of Archetypal Analysis for large-scale bioinformatics applications. ArchetypalC supports sparse matrices, parallel computation, and interactive visualization, enabling efficient exploration of complex biological datasets.

Our group also contributed with two additional posters:

🎤 Elena Marinello presented GRAPHIA, a pipeline for deriving patient-specific network features by weighting a shared biological interaction graph according to individual gene expression profiles.

🎤 Mikele Milia presented scTransformer, a novel framework for SingleCellTranscriptomics that incorporates prior biological knowledge into the self-attention mechanism of Transformer models.

We’re excited to share that our latest paper is now published in Frontiers in Bioinformatics: “N2SIMBA: from Network topology to SIMulation of interactions and BActerial abundance, using microbial consumer resource model.”

In this work, we introduce N2SIMBA, a modular simulation framework designed to generate realistic 16S rDNA-seq count tables starting from known microbial interaction networks. Since experimentally validated ground-truth interaction networks are rarely available, especially for complex bacterial communities, N2SIMBA provides a flexible in silico tool to systematically benchmark network inference methods.

The framework leverages the Microbial Consumer Resource Model to simulate microbial community dynamics through metabolite-mediated interactions, accounting for both competitive and promotional effects between taxa. By combining ecological modelling with sequencing count simulation, N2SIMBA enables users to study how community composition changes under different network topologies, environmental conditions, and perturbations.

We hope this framework will be useful for researchers interested in microbial ecology, systems biology, and the design of robust bacterial communities. New features and extensions are already under active development!.

Congratulations to Matteo Baldan, Giacomo Baruzzo, Piero Mariotto, Ada Rossato, Marco Cappellato, and Barbara Di Camillo for this work.

The paper is available at: https://doi.org/10.3389/fbinf.2026.1783447

On February 12, 2026, Mikele Milia successfully defended his PhD thesis in Information Engineering at the University of Padua. His outstanding research and dedication culminated in a PhD awarded with honors. 🏅🏆

🖥️ His PhD thesis focused on the integration of omics data with prior biological knowledge as input to advanced models to generate meaningful insights. His outstanding work addressed significant methodological and computational challenges posed by large-scale biological data.

We are pleased to announce the publication of our latest paper, “Validity of Feature Importance in Low-Performing Machine Learning for Tabular Biomedical Data”
in IEEE Access: 10.1109/ACCESS.2025.3618851

🔍 This study investigates how machine learning (ML) performance affects the validity of feature importance in biomedical datasets.
While high model accuracy is often considered a prerequisite for interpreting feature importance, this assumption has rarely been examined. In this work, we challenge this notion by showing that even low-performing models can provide reliable feature importance in biomedical contexts.

🖥️ We developed an experimental framework to assess the stability of feature importance, finding that performance degradation due to a limited number of samples behaves as conventionally expected, reducing validity, whereas degradation caused by a limited number of features preserves the validity of feature importance to a much greater extent.

👥 This research is the result of a collaboration between our group, Georgia Institute of Technology and Seoul National University. Congratulations to the first author, Dr. Youngro Lee, who was a visiting Ph.D. student in our group during his doctoral studies at Seoul National University. Great job, Youngro!

Congratulations to our post-doc researcher Giulia Cesaro who has been awarded the PhD Thesis Award 🏆 by the Istituto di BioRobotica – Scuola Superiore Sant’Anna during the XLIV Annual School 2025 of the Gruppo Nazionale di Bioingegneria (GNB).

✨ Her doctoral dissertation was recognized by the committee “for the originality of the content and the ability to apply engineering approaches to the analysis of cell-to-cell communication from transcriptomic data, in a complex biological system, tackled with methods that reveal and model its complexity.” 💻