Engineering Big Data solutions to mining Imaging Genetics data through Deep Learning

The aim of this project is to develop new methods for intelligent feature selection, that will allow advanced joint modelling of vast imaging and genomics data sets, for the purpose of determining new gene candidates for targeted neuroprotective therapy of vulnerable preterm infants. Imaging genetics is an emerging field that has huge potential to improve understanding of complex neurological conditions, through identifying concrete links between morphological or functional changes in the brain and genetic variants linked to disease. Unfortunately, the immense numbers of imaging and genetic features involved challenge current methods of analysis, limiting their capacity to find statistically significant relationships from the data. This project will therefore use advanced techniques from sparse predictive modelling and Deep Learning to intelligently compress and combine state-of-the-art multi-modality, developmental imaging and genomics data sets so as to propose sensitive genotype-phenotype candidates as targets for future clinical trials.