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.
- Conference announcement: we are involved in the organization of RECOMB 2021: 25th International Conference on Research in Computational Molecular Biology. Apr 18-21, 2021 – Padova, Italy
- Our Team “GIGI” are one of the best-performing teams in the Sub-challenge 2 of sbv IMPROVER METAGENOMICS DIAGNOSIS FOR INFLAMMATORY BOWEL DISEASE CHALLENGE (MEDIC)
- Conference announcement: we are involved in the organization of BITS 2020 Congress. The Congress will now take place on the new dates: Jul 6-9, 2021 – Verona, Italy
- Tavazzi E, Daberdaku S, Vasta R, Calvo A, Chiò A, Di Camillo B. Exploiting mutual information for the imputation of static and dynamic mixed-type clinical data with an adaptive k-nearest neighbours approach. BMC Med Inform Decis Mak 2020
- Vettoretti M, Longato E, Zandonà A, Li Y, Pagán JA, Siscovick D, Carnethon MR, Bertoni AG, Facchinetti A, Di Camillo B. Addressing practical issues of predictive models translation into everyday practice and public health management: a combined model to predict the risk of type 2 diabetes improves incidence prediction and reduces the prevalence of missing risk predictions. BMJ Open Diabetes Res Care 2020
- Longato E, Vettoretti M, Di Camillo B. A practical perspective on the concordance index for the evaluation and selection of prognostic time-to-event models. J Biomed Inform 2020
- 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”