Fused Lasso Regression for Identifying Differential Correlations in Brain Connectome Graphs.

IF 2.1 4区数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Statistical Analysis and Data Mining Pub Date : 2018-10-01 Epub Date: 2018-07-11 DOI:10.1002/sam.11382

Donghyeon Yu, Sang Han Lee, Johan Lim, Guanghua Xiao, R Cameron Craddock, Bharat B Biswal

引用次数: 3

Abstract

In this paper, we propose a procedure to find differential edges between two graphs from high-dimensional data. We estimate two matrices of partial correlations and their differences by solving a penalized regression problem. We assume sparsity only on differences between two graphs, not graphs themselves. Thus, we impose an ℓ ₂ penalty on partial correlations and an ℓ ₁ penalty on their differences in the penalized regression problem. We apply the proposed procedure to finding differential functional connectivity between healthy individuals and Alzheimer's disease patients.

Abstract Image

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

融合Lasso回归识别脑连接组图的差异相关性。

本文提出了一种求高维数据中两个图之间的微分边的方法。我们通过求解一个惩罚回归问题来估计两个偏相关矩阵及其差异。我们只在两个图之间的差异上假设稀疏性，而不是图本身。因此，在惩罚回归问题中，我们对偏相关性施加了一个l2惩罚，对它们的差异施加了一个l1惩罚。我们将提出的程序应用于寻找健康个体和阿尔茨海默病患者之间的差异功能连接。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Statistical Analysis and Data Mining COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

CiteScore

3.20

自引率

7.70%

发文量

期刊介绍： Statistical Analysis and Data Mining addresses the broad area of data analysis, including statistical approaches, machine learning, data mining, and applications. Topics include statistical and computational approaches for analyzing massive and complex datasets, novel statistical and/or machine learning methods and theory, and state-of-the-art applications with high impact. Of special interest are articles that describe innovative analytical techniques, and discuss their application to real problems, in such a way that they are accessible and beneficial to domain experts across science, engineering, and commerce. The focus of the journal is on papers which satisfy one or more of the following criteria: Solve data analysis problems associated with massive, complex datasets Develop innovative statistical approaches, machine learning algorithms, or methods integrating ideas across disciplines, e.g., statistics, computer science, electrical engineering, operation research. Formulate and solve high-impact real-world problems which challenge existing paradigms via new statistical and/or computational models Provide survey to prominent research topics.