Whole-Volume Clustering of Time Series Data from Zebrafish Brain Calcium Images via Mixture Modeling.

IF 2.1 4区数学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Statistical Analysis and Data Mining Pub Date : 2018-02-01 Epub Date: 2017-12-06 DOI:10.1002/sam.11366

Hien D Nguyen, Jeremy F P Ullmann, Geoffrey J McLachlan, Venkatakaushik Voleti, Wenze Li, Elizabeth M C Hillman, David C Reutens, Andrew L Janke

引用次数: 6

Abstract

Calcium is a ubiquitous messenger in neural signaling events. An increasing number of techniques are enabling visualization of neurological activity in animal models via luminescent proteins that bind to calcium ions. These techniques generate large volumes of spatially correlated time series. A model-based functional data analysis methodology via Gaussian mixtures is suggested for the clustering of data from such visualizations is proposed. The methodology is theoretically justified and a computationally efficient approach to estimation is suggested. An example analysis of a zebrafish imaging experiment is presented.

Abstract Image

查看原文本刊更多论文

基于混合建模的斑马鱼脑钙图像时间序列数据的全体积聚类。

钙是神经信号事件中普遍存在的信使。越来越多的技术通过与钙离子结合的发光蛋白使动物模型的神经活动可视化。这些技术产生大量的空间相关时间序列。提出了一种基于模型的功能数据分析方法，通过高斯混合方法对来自此类可视化的数据进行聚类。该方法在理论上是合理的，并提出了一种计算效率高的估计方法。给出了斑马鱼成像实验的实例分析。

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分钟内获得全文求助全文

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.