Research
The Sysbiobig group research activity is centred in the development and application of advanced modelling, data mining and machine learning methods for high-throughput biological data analysis in the field of Bioinformatics and Health Informatics.
In particular, the Sysbiobig group has been working on the preprocessing and analysis of static and dynamic transcriptomics and metagenomics data and has developed and applied different advanced data mining and machine learning methods for robust biomarker discovery, predictive modelling and clustering
The group has also a great expertise in the development/application of differential equation based models, Boolean and Bayesian Networks in the field of reverse engineering and systems biology.
Recently, the group has also started to explore a new research line in the field of Synthetic biology. By implementing new biological systems in the form of living cells, engineered through rationally designed synthetic genetic circuits, our group aims to exploit engineered probiotic bacterial cells as a novel therapeutic approach.
- Three New PhD Graduates in Our Research Group!
- Sharing Our Research in High Performance Computing applied to Bioinformatics at PDP 2025 & Best Paper Award!
- Building Bridges Between Engineering and Medicine Through AI
- Baruzzo G, Cesaro G, Di Camillo B. quickSparseM: a library for memory- and time-efficient computation on large, sparse matrices with application to omics data. Proceedings of the 33rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP) 2025
- Peek N, Hindricks G, Akbarov A, Tijssen JGP, Jenkins DA, Kapacee Z, Parkes LM, van der Geest RJ, Longato E, et al.. Sudden cardiac death after myocardial infarction: individual participant data from pooled cohorts. Eur Heart J 2024.
- Guazzo A, Atzeni M, Idi E, Trescato I, Tavazzi E, Longato E, Manera U, Chiò A, Gromicho M, AlvesI, de Carvalho M, Vettoretti M, Di Camillo B. Predicting Clinical Events Characterizing the Progression of Amyotrophic Lateral Sclerosis via Machine Learning Approaches Using Routine Visits Data: A Feasibility Study. BMC Med Inform Decis Mak 2024
- Trescato I, Tavazzi E, Vettoretti M, Vasta R, Chiò A, Di Camillo B. DYNAMITE: Integrating Archetypal Analysis and Process Mining for Interpretable Disease Progression Modelling. IEEE J Biomed Health Inform 2024.
Main Fundings
- 2021-2024 European Project H2020: “BRAINTEASER – BRinging Artificial INTelligencE home for a better cAre of amyotrophic lateral sclerosis and multiple SclERosis”.
- 2018-2021 PRIN (Call 2017): “Deconstruct and rebuild phenotypes: a multimodal approach toward personalized medicine in ALS (DECIPHER-ALS)”.
- 2017-2020 Information Engineering Department, University of Padova “Proactive Project Grant”: “From Single-Cell to Multi-Cells Information Systems Analysis
- 2016-2019 European Project H2020: “PULSE: Participatory Urban Living for Sustainable Environments”