Ontology-Based representation of cancer driver genes for predictive purposes
Stratifying cancer patients based on their genomic signature allows predicting disease progression and delivering targeted treatments to which patients are more likely to respond. Therefore, many computational approaches have been used to identify gene expression signatures that enable customised prognostication and therapies for individuals. However, the goal of identifying such signatures may only be achieved by relating genes to phenotypes, the specific biological processes and molecular functions genes can be involved in and the cellular locations at which gene products are active. The aim of this project is to find a meaningful embedding for genes by interlinking multiple ontologies using machine learning. The embedding will then be used as input to deep learning algorithms that are used for predicting patients’ survival related characteristics.
The ideal candidate for this project will have great machine learning programming skills in Python. Good knowledge of ontologies is required too. Familiarity with autoencoders and biomedical data is a big plus.