Biological time series analysis using a context free language: applicability to pulsatile hormone data.
Biological time series analysis using a context free language: applicability to pulsatile hormone data.
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DOI：
10.1371/journal.pone.0104087
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Klerman EB
中科院分区：
文献类型：
--
作者：
Dean DA 2nd;Adler GK;Nguyen DP;Klerman EB
我们提出了一种新的方法来分析生物时间序列数据，它使用上下文无关语言(CFL)表示，允许从时间序列中提取和量化重要特征。这种表示产生了分层自适应(HAP)分析，这是一套多项互补技术，能够快速分析数据，而不需要用户设置参数。HAP分析生成分级组织的参数分布，允许对时间序列的多尺度分量进行量化，并包括数据分析管道，该管道应用递归分析以生成分级组织的结果，以扩展诸如药代动力学和脉冲间隔等传统结果衡量标准。Pulsicons是一种新的基于文本的时间序列表示方法，也是从CFL方法中衍生出来的，作为一种客观的定性比较命名法。我们应用HAP分析了来自14名健康女性的24小时频繁采样的脉动皮质醇激素数据，这是已知的分析挑战。HAP分析在几秒钟内就产生了结果，并为每个参与者产生了数十个数字。结果将观察到的皮质醇数据的定性特征量化为一系列脉冲簇，每个脉冲簇由一个或多个嵌入的脉冲组成，并在该数据集中识别出两种极端表型。HAP分析被设计成对个体差异和缺失数据具有健壮性，并可应用于其他脉动激素。未来的工作可以将HAP分析扩展到其他时间序列数据类型，包括振荡和其他周期性生理信号。
We present a novel approach for analyzing biological time-series data using a context-free language (CFL) representation that allows the extraction and quantification of important features from the time-series. This representation results in Hierarchically AdaPtive (HAP) analysis, a suite of multiple complementary techniques that enable rapid analysis of data and does not require the user to set parameters. HAP analysis generates hierarchically organized parameter distributions that allow multi-scale components of the time-series to be quantified and includes a data analysis pipeline that applies recursive analyses to generate hierarchically organized results that extend traditional outcome measures such as pharmacokinetics and inter-pulse interval. Pulsicons, a novel text-based time-series representation also derived from the CFL approach, are introduced as an objective qualitative comparison nomenclature. We apply HAP to the analysis of 24 hours of frequently sampled pulsatile cortisol hormone data, which has known analysis challenges, from 14 healthy women. HAP analysis generated results in seconds and produced dozens of figures for each participant. The results quantify the observed qualitative features of cortisol data as a series of pulse clusters, each consisting of one or more embedded pulses, and identify two ultradian phenotypes in this dataset. HAP analysis is designed to be robust to individual differences and to missing data and may be applied to other pulsatile hormones. Future work can extend HAP analysis to other time-series data types, including oscillatory and other periodic physiological signals.
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影响因子：
5.8
作者：
JUSKO, WJ;SLAUNWHITE, WR;ACETO, T
通讯作者：
ACETO, T
DOI：
10.1152/ajpendo.00562.2002
发表时间：
2003-11-01
影响因子：
5.1
作者：
Klerman, EB;Adler, GK;Brown, EN
通讯作者：
Brown, EN
DOI：
10.1152/ajpendo.1998.275.1.e157
发表时间：
1998-07-01
影响因子：
5.1
作者：
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通讯作者：
Veldhuis, JD
影响因子：
2.9
作者：
Johnson, Michael L.;Pipes, Lenore;Evans, William S.
通讯作者：
Evans, William S.
影响因子：
4
作者：
Barrett, CL;Reidys, CM
通讯作者：
Reidys, CM