IDSAI Seminar: Professor Claudio Angione
| Dates: | 2 May 2023 |
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| Times: | 14:00 - 15:00 |
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| What is it: | Seminar |
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| Organiser: | Digital Futures |
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| Who is it for: | Early years, External researchers, Adults, Alumni |
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Speaker: Claudio Angione, Professor of Artificial Intelligence at Teesside University (TU)
Title: Combining machine learning and metabolic modelling approaches to characterise the cell phenotype
In recent biomedical research, deep learning has been widely used for the exploitation of omics data when predicting the cell phenotype, suffering however from a lack of biological 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.
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, discuss the mathematical and practical aspects involved, and show 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.
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