Fast flexible feature selection for high dimensional challenging data

该项目旨在提供新的框架，通过结构变系数回归模型，快速灵活地选择特征，并对异质数据进行适当建模。其成果将是一系列新的统计方法和概念，使复杂的生物科学数据的建模更加强大。该项目将创建科学，以建立可靠的统计模型，考虑模型的不确定性，影响结果如何解释，并附带软件。这将是对食品和健康研究优先领域（包括肉类科学、亨廷顿氏病和肾移植等领域）模型置信度评估的重大改进。

The project aims to provide new frameworks for fast flexible feature selection and appropriate modelling of heterogeneous data through structural varying-coefficient regression models. The outcomes will be a series of new statistical methods and concepts enabling more powerful modelling of complex bioscience data. The project will create the science for building reliable statistical models taking model uncertainty into account, impacting how results will be interpreted, and with accompanying software. This will be a significant improvement in the assessment of model confidence in the food and health research priority areas including areas such as meat science, Huntington’s disease, and kidney transplantation.