Computational Biology and Bioinformatics.
We develop computational methods for the integrated analysis of -omics data, with a focus on transcriptional regulatory networks, transcription factor binding site enrichment and deep learning on biological sequences.
We combine statistics, network theory, machine learning and molecular biology to understand how gene expression programs are orchestrated in the cell — and what happens when they go wrong.
Our work starts from a simple idea: biological function emerges from networks of interactions, not from isolated genes. We build and analyse transcriptional regulatory networks from experimental data and from predicted transcription factor binding sites, looking for the principles that govern gene expression in physiological and pathological conditions.
We design open, reproducible software tools — such as the TRANSPARENT framework for TFBS enrichment analysis and DNABERT-based models for binding-site prediction — and apply them to concrete cases, from oncology and viral infection to neurodegenerative and complex diseases, in close collaboration with experimental groups inside and outside CNR.
The group is part of the BIOSYS research group of the Institute for System Analysis and Computer Science «Antonio Ruberti» (IASI-CNR), where systems modelling meets the life sciences.
From network reconstruction to disease mechanisms, each line contributes to a systems-level reading of gene regulation.
Reconstruction and analysis of gene–transcription factor networks from experimental data and predicted transcription factor binding sites (TFBS), to identify key regulatory modules. With TRANSPARENT we detect over-represented transcription factors in disease-associated gene sets, under rigorous statistical control.
Deep learning and Transformer-based language models (e.g. DNABERT) applied to DNA, RNA and protein sequences: prediction of transcription factor binding sites directly from sequence, sequence representation learning, and reviews of the state of the art for nucleotide and protein sequence analysis.
Methods to combine transcriptomics, epigenomics and genomics into predictive models, reconciling heterogeneous scales and data sources.
From molecular signature to mechanism: we apply network analysis to cancer, neurodegenerative diseases (ALS, multiple sclerosis) and other complex disorders to generate hypotheses that can be tested in the lab.
An interdisciplinary team spanning biology, neuroscience, computer science, mathematics and statistics. We always look for curiosity and rigour, in equal measure.
Neuroscientist working on neurodegeneration and neuroinflammation in CNS diseases, mainly ALS and multiple sclerosis; purinergic and histaminergic signalling; preclinical pharmacology in primary cultures, cell lines, Drosophila and mouse models.
Computational biology; discrete mathematics and graph theory applied to biological systems, with a focus on transcription factors, gene regulation and deep learning for TFBS prediction. Degree in Mathematics, Master in Bioinformatics, PhD in Genetics and Molecular Biology; at IASI-CNR since 2011.
NGS data analysis, transcriptional regulatory networks and Transformer-based language models for biological sequences.
Systems biology and bioinformatics: network medicine, multi-omics data integration and computational/mathematical methods for the analysis of biomedical data in complex diseases.
Optimization models and algorithms, operations research, machine learning and artificial intelligence, with applications to bioinformatics; co-author of the group's work on deep learning and language models for biological sequences.
Bachelor's degree in Biology and Master's degree in Bioinformatics, University of Rome Tor Vergata. Works on NGS data analysis, transcription factor binding site enrichment and development of the TRANSPARENT framework.
Researchers who are part of our family, with whom we share projects, papers, and ideas.
Cleveland, Ohio (USA). Bioinformatics, machine learning and hyperdimensional computing for microbial genomics and biomedical data; author of tools such as MetaSBT. Long-standing co-author on transcription factor synergism in humans.
cumbof.github.io ↗
Chennai (India). Deep learning and Transformer-based language models for nucleotide and protein sequences; co-author with the group on TFBS prediction (DNABERT-based frameworks, TFBS-Finder) and on the analysis of SARS-CoV-2.
Faculty page ↗A selection of the group's most recent work (2024–2026). The full list is available in the institutional repositories.
Interested in a collaboration, a thesis or a PhD with us? Get in touch — we are happy to reply.