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.
- Join the BRAINTEASER mid-term workshop
- 🚨CALL FOR PAPERS: From translational bioinformatics computational methodologies to personalized medicine
- Check out our new paper “MAST: a hybrid Multi-Agent Spatio-Temporal model of tumor microenvironment informed using a data-driven approach” on Bioinformatics Advances
- Cesaro G., Milia M., Baruzzo G., Finco G., Morandini F., Lazzarini A., Alotto P., de Miranda N.F., Trajanoski Z., Finotello F., Di Camillo B. MAST: a hybrid Multi-Agent Spatio-Temporal model of tumor microenvironment informed using a data-driven approach, Bioinformatics Advances 2022
- Cappellato M, Baruzzo G, Di Camillo B. Investigating differential abundance methods in microbiome data: A benchmark study. PLOS Computational Biology 2022
- Tavazzi E, Daberdaku S, Zandonà A, Vasta R, Nefussy B, Lunetta C, Mora G, Mandrioli J, Grisan E, Tarlarini C, Calvo A, Moglia C, Gotkine M, Drory V, Chiò A, Di Camillo B. Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression. J Neurol 2022
- Baruzzo G, Patuzzi I, Di Camillo B. Beware to ignore the rare: how imputing zero-values can improve the quality of 16S rRNA gene studies results. BMC Bioinformatics 2022
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”
