Robust and Discriminative Brain Genome Association Study.
Robust and Discriminative Brain Genome Association Study.
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DOI：
10.1007/978-3-030-32251-9_50
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Shen D
中科院分区：
文献类型：
--
作者：
Zhu X;Shen D
脑基因组关联（Brain Genome Association， BGA）研究，研究脑结构/功能（以神经影像学表型为特征）与遗传变异（以单核苷酸多态性（SNPs）为特征）之间的关系，在神经系统疾病的病理分析中具有重要意义。然而，目前的BGA研究是有限的，因为他们没有明确考虑疾病标签，来源重要性和样本重要性在他们的配方。为了解决这些问题，我们提出了一个鲁棒性和歧视性的BGA配方。具体来说，我们学习了两个变换矩阵，用于将两个异构数据源（即神经影像学数据和遗传数据）映射到一个公共空间，从而使来自同一主题（但不同来源）的样本彼此接近，并且具有不同标签的样本是可分离的。此外，我们在转换矩阵上添加了稀疏性约束，以便在两个数据源上进行特征选择。此外，在公式中还通过自适应无参数样本和源加权来考虑样本重要性和源重要性。我们使用阿尔茨海默病神经影像学倡议（ADNI）数据集进行了各种实验，以测试神经影像学表型和snp在公共空间中相互代表的程度。
Brain Genome Association (BGA) study, which investigates the associations between brain structure/function (characterized by neuroimaging phenotypes) and genetic variations (characterized by Single Nucleotide Polymorphisms (SNPs)), is important in pathological analysis of neurological disease. However, the current BGA studies are limited as they did not explicitly consider the disease labels, source importance, and sample importance in their formulations. We address these issues by proposing a robust and discriminative BGA formulation. Specifically, we learn two transformation matrices for mapping two heterogeneous data sources (i.e., neuroimaging data and genetic data) into a common space, so that the samples from the same subject (but diffrent sources) are close to each other, and also the samples with diffrent labels are separable. In addition, we add a sparsity constraint on the transformation matrices to enable feature selection on both data sources. Furthermore, both sample importance and source importance are also considered in the formulation via adaptive parameter-free sample and source weightings. We have conducted various experiments, using Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, to test how well the neuroimaging phenotypes and SNPs can represent each other in the common space.
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影响因子：
5.7
作者：
Stein, Jason L.;Hua, Xue;Lee, Suh;Ho, April J.;Leow, Alex D.;Toga, Arthur W.;Saykin, Andrew J.;Shen, Li;Foroud, Tatiana;Pankratz, Nathan;Huentelman, Matthew J.;Craig, David W.;Gerber, Jill D.;Allen, April N.;Corneveaux, Jason J.;DeChairo, Bryan M.;Potkin, Steven G.;Weiner, Michael W.;Thompson, Paul M.
通讯作者：
Thompson, Paul M.
DOI：
10.1007/978-3-319-10443-0_42
发表时间：
2014
期刊：
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子：
--
作者：
通讯作者：
--
通过局部结构学习和稀疏学习进行无监督特征选择
影响因子：
3.6
作者：
Lei, Cong;Zhu, Xiaofeng
通讯作者：
Zhu, Xiaofeng
用于光谱特征选择的动态图学习
影响因子：
3.6
作者：
Zheng, Wei;Zhu, Xiaofeng;Lei, Cong
通讯作者：
Lei, Cong
用于图像分类的块行稀疏多视图多标签学习
影响因子：
11.8
作者：
Zhu, Xiaofeng;Li, Xuelong;Zhang, Shichao
通讯作者：
Zhang, Shichao