Elena Safrygina

Translating Spatio-Temporal Imaging Data Into Clinical Data Using Machine Learning

Project Summary

Tumour heterogeneity at the protein level has been associated with poor prognosis in several human carcinomas. Current approaches for assessing protein function rely on intensity-based methods, which are limited by their subjectivity and specificity.  A novel assay using amplified, time-resolved Forster resonance energy transfer (FRET) is a highly specific and sensitive method and can be adapted to any protein.

The aim of the project is to combine both methods to reveal molecular heterogeneity at the protein level and, using machine learning techniques, translate it to interpretable format, which can be widely used by clinicians.


Research Interests

Probabilistic modelling, Machine Learning, and interdisciplinary applications to biomedical sciences.

My research aim is to extract valuable information and automate the inference of clinical data using Machine Learning.


Specialist diploma in Engineering, Ural Federal University, Ekaterinburg

MSc Data Science, Birkbeck College, University of London


Prof Banafshé Larijani

Dr Julian Padget

Elena Safrygina