sysbiobig.dei.unipd.itSystems Biology and Bioinformatics Group – Department of Information Engineering – University of

Padova Systems Biology and Bioinformatics Group Department of Information Engineering – University of Padova Menu Home News People Publications Software Thesis topics Scroll down to content Systems Biology and Bioinformatics Group 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. Latest News Key Takeaways from Our Team’s Participation at the World Health Forum Veneto 2024 March 26, 2024 Advancing ALS Research: Collaborative Efforts with Precision ALS December 10, 2023 ✨ CIBB2023 Success: Advancing Computational Intelligence September 21, 2023 Latest Publications Khachatryan L, Xiang Y, Ivanov A, Glaab E, Graham G, Granata I, Giordano M, Maddalena L, Piccirillo M, Manipur I, Baruzzo G, Cappellato M, Avot B, Stan A, Battey J, Lo Sasso G, Boue S, Ivanov NV, Peitsch MC, Hoeng J, Falquet F, Di Camillo B, Guarracino M, Ulyantsev V, Sierro N, Poussin C. Results and Lessons Learned from the sbv IMPROVER Metagenomics Diagnostics for Inflammatory Bowel Disease Challenge. Sci Rep 2023 April 18, 2023 Tavazzi E, Gatta R, Vallati M, Cotti Piccinelli S, Filosto M, Padovani A, Castellano M, Di Camillo B. Leveraging Process Mining for Modeling Progression Trajectories in Amyotrophic Lateral Sclerosis. BMC Medical Inform Decis Mak 2022 February 2, 2023 Chicco D, Oneto L, Tavazzi E. Eleven quick tips for data cleaning and feature engineering. PLoS Comput Biol 2022 December 15, 2022 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 December 5, 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” Follow us twitter gitlab linkedin Department of Information Engineering – University of Padova wordpress linkedin instagram facebook youtube Link Proudly powered by WordPress We are using cookies to give you the best experience on our website. You can find out more about which cookies we are using or switch them off in settings . Accept Close GDPR Cookie Settings Privacy Overview Strictly Necessary Cookies Powered by GDPR Cookie Compliance Privacy Overview This website uses cookies so that we can provide you with the best user experience possible. Cookie information is stored in your browser and performs functions such as recognising you when you return to our website and helping our team to understand which sections of the website you find most interesting and useful. Strictly Necessary Cookies Strictly Necessary Cookie should be enabled at all times so that we can save your preferences for cookie settings. Enable or Disable Cookies If you disable this cookie, we will not be able to save your preferences. This means that every time you visit this website you will need to enable or disable cookies again. Enable All...