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

通过深度学习挖掘成像遗传学数据的工程大数据解决方案

• 批准号: 2371261
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2371261
• 关键词: Engineering; Big; Data; solutions; mining

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.

该项目的目的是开发智能特征选择的新方法，这将允许对大量成像和基因组学数据集进行高级联合建模，以确定用于脆弱早产儿的靶向神经保护治疗的新基因候选者。成像遗传学是一个新兴的领域，具有巨大的潜力，以提高对复杂的神经系统疾病的理解，通过确定大脑中的形态或功能变化和与疾病相关的遗传变异之间的具体联系。不幸的是，所涉及的大量成像和遗传特征挑战了当前的分析方法，限制了它们从数据中发现统计学显著关系的能力。因此，该项目将使用稀疏预测建模和深度学习的先进技术，智能压缩和联合收割机结合最先进的多模态，发育成像和基因组学数据集，以提出敏感的基因型-表型候选者作为未来临床试验的目标。