标题： 一种多任务深度特征选择方法用于脑成像遗传学 显示英文标题

标题： A Multi-task Deep Feature Selection Method for Brain Imaging Genetics

摘要： 利用脑成像定量性状（QTs）来识别遗传风险因素是成像遗传学中的一个重要研究课题。 许多努力通过构建线性模型，例如线性回归（LR），来提取成像QTs与遗传因素（如单核苷酸多态性（SNPs））之间的关联。 然而，据我们所知，这些线性模型由于位点对成像QTs的隐秘和多样影响，无法完全揭示复杂的关系。 尽管深度学习模型可以提取非线性关系，但它们无法选择相关的遗传因素。 在本文中，我们提出了一种新的多任务深度特征选择（MTDFS）方法用于脑成像遗传学。 MTDFS首先添加一个多任务一对一层，并施加一种混合稀疏性诱导惩罚，以选择对异常成像QTs有显著贡献的相关SNPs。 然后构建一个多任务深度神经网络来建模成像QTs和SNPs之间的复杂关联。 MTDFS不仅可以提取非线性关系，还能赋予深度神经网络特征选择能力。 我们在真实的神经成像遗传数据上将MTDFS与LR和单任务DFS（DFS）方法进行了比较。 实验结果表明，MTDFS在QT-SNP关系识别和特征选择方面优于LR和DFS。 总之，MTDFS在识别风险位点方面非常有效，可以成为脑成像遗传学方法库的重要补充。 显示英文摘要

摘要： Using brain imaging quantitative traits (QTs) to identify the genetic risk factors is an important research topic in imaging genetics. Many efforts have been made via building linear models, e.g. linear regression (LR), to extract the association between imaging QTs and genetic factors such as single nucleotide polymorphisms (SNPs). However, to the best of our knowledge, these linear models could not fully uncover the complicated relationship due to the loci's elusive and diverse impacts on imaging QTs. Though deep learning models can extract the nonlinear relationship, they could not select relevant genetic factors. In this paper, we proposed a novel multi-task deep feature selection (MTDFS) method for brain imaging genetics. MTDFS first adds a multi-task one-to-one layer and imposes a hybrid sparsity-inducing penalty to select relevant SNPs making significant contributions to abnormal imaging QTs. It then builds a multi-task deep neural network to model the complicated associations between imaging QTs and SNPs. MTDFS can not only extract the nonlinear relationship but also arms the deep neural network with the feature selection capability. We compared MTDFS to both LR and single-task DFS (DFS) methods on the real neuroimaging genetic data. The experimental results showed that MTDFS performed better than both LR and DFS in terms of the QT-SNP relationship identification and feature selection. In a word, MTDFS is powerful for identifying risk loci and could be a great supplement to the method library for brain imaging genetics.