Dr Claudio Angione - Aware machine learning pipelines to predict and characterize cell behaviour

Dr Claudio Angione is a reader in computer science and the lead of the computational systems biology and data analytics research group at Teesside University. Here he discusses 'Building mechanism-aware machine learning pipelines to predict and characterize cell behaviour'. To find out more about this talk visit: http://swantalks.blogspot.com/p/speak... To find out more about Stefanie and her research: https://www.scedt.tees.ac.uk/c.angion... Abstract In recent biomedical research, machine learning has been widely used for the inspection and exploitation of omics data when predicting cell phenotype, suffering however from lack of interpretability. In parallel, constraint-based mathematical modelling of metabolism has gained popularity due to its scope and flexibility, enabling mechanistic insights into the genotype-phenotype-environment relationship within cells, often via integration with omics data. These two computational frameworks have mostly been used in isolation, having distinct research communities associated with them. However, their complementary characteristics and common mathematical bases make them particularly suitable to be combined. I will describe how machine learning can be combined with constraint-based modelling, discussing the mathematical and practical aspects involved, and showing several applications in biotechnology and biomedicine. Instead of applying machine learning to omics data directly, we propose a multi-view approach merging experimental omics data and model-generated predictions, based on known biochemistry. This architecture can contribute with disjoint information towards biologically-informed and interpretable machine learning, including key mechanistic information in an otherwise biology-agnostic learning process.