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The group develops computational methods that extract information from with a wide range of time-series data. While time-series data is more expensive to produce than steady-state data, tracing a system\u2019s transient behaviour provides essential information on how different species interact with each other and reveals causal mechanisms.<\/span> <\/p>\n\n\n\nBringing together diverse backgrounds in mathematics and computational sciences, our group engages with this overall problem from different perspectives. Current initiatives include developing methods for inferring causality in gene regulatory networks from both average and single cell data, and pinpointing the source of diseases in these networks. <\/p>\n\n\n\n
The models are subsequently validated experimentally, ensuring that our research has broad-ranging applications in biomedicine, including neurodegenerative diseases, cardiovascular diseases, mechanisms of circadian rhythms and stem cell differentiation.<\/p>\n<\/div>\n<\/div>\n<\/div><\/section>\n\n\n\n\n
\nOur research projects<\/h2>\n\n\n\n
The AI Modelling and Prediction group is involved in several research projects focusing on algorithm development and data analaysis from time-series data:<\/p>\n\n\n\n