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