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The epidermal growth factor receptor (EGFR) is a cell surface receptor, which controls cell growth and division. Mutations affecting the receptor expression could lead to cancer. Analysis of EGFR interactions with living cells requires measuring separations between 5 and 60nm. The separations are calculated by analysing time-series of diffraction limited spots, generated by labelled EGFRs. Finding such time-series manually is time consuming and non-reproducible. This project uses machine learning algorithms in combination with understanding of the data collection process and analysis requirements to optimise the data selection process, by automatically rejecting non-analysable time-series. The comparison to the manual process shows that the automated process significantly decreases the time required for data selection and decreases the uncertainty in the distance measurements.
About the author
Teodor Boyadzhiev graduated bachelor degree in The University of Edinburgh in 2013. His education continued by pursuing PhD degree in King¿s College London and graduated in 2021. Currently he works at the Institute of Mathematics and Informatics at the Bulgarian Academy of Sciences.
