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

该项目的目的是开发智能特征选择的新方法，这将允许对海量成像和基因组数据集进行高级联合建模，目的是为脆弱早产儿的定向神经保护治疗确定新的候选基因。成像遗传学是一个新兴领域，通过确定大脑的形态或功能变化与与疾病有关的遗传变异之间的具体联系，有巨大的潜力提高对复杂神经疾病的理解。不幸的是，涉及的大量成像和基因特征挑战了目前的分析方法，限制了它们从数据中找到统计上有意义的关系的能力。因此，该项目将使用稀疏预测建模和深度学习的先进技术，智能地压缩和组合最先进的多模式、发育成像和基因组学数据集，以提出敏感的基因-表型候选基因作为未来临床试验的目标。

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.