Data Analysis for Fluorescence Microscopy. Optimising Measurements of Epidermal Growth Factor Receptor Oligomers in Cells Using Machine Learning Algorithms

      Аннотация: 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.

Ключевые слова: Machine Learning Algorithms, Bayesian Modelling, epidermal growth factor receptor, fluorescence microscopy