The Research Centre for Computational Biology will engage in developing solutions for the analysis, management and integration of data produced by other Centres and for making such data available and usable by scientists more broadly (i.e. computational experts and non-experts alike).
Together with other Human Technopole Research Centres, it will take statistical, computational and bioinformatics approaches to study a variety of problems such as disease-associated biological processes or the functioning of the healthcare system, mainly by developing advanced technologies, novel algorithms and software for computational simulations and big data analysis. The development of computational tools for drug discovery and repurposing is an example of research line that will be pursued within the Centre.
Digital analysis and processing of biomedical imaging data will also be pursued extensively within the Research Centre for Computational Biology. Such bio-image computation work will focus on both light microscopy and structural biology datasets, initially through close interactions with the Research Centres for Neurogenomics and Structural Biology and external partners. In most of these areas the volume of data to be handled and analysed is very large, leading to a need to develop and use machine learning or artificial intelligence methods, as well as computer vision, computer graphics and high-performance computing methods.
The Iorio Group works at the interface of biology, machine learning, statistics and information theory with the goal of understanding and predicting how genomic alterations and molecular traits from other omics contribute to pathological processes, biological circuits’ rewiring and have an impact on therapeutic response in human cancers and other diseases.
The research conducted in the Jug Group is pushing the boundary of what image analyses and machine learning can do for quantifying biological (image) data. The common denominator of such projects is the indisputable necessity to analyse large amounts of light microscopy data without causing impossible amounts of manual data curation and data processing to life-science researchers (aka our users and collaborators).