Biological time series analysis using a context free language: applicability to pulsatile hormone data.

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 multipl...

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Autores principales:	Dennis A Dean, Gail K Adler, David P Nguyen, Elizabeth B Klerman
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2abe17b337194922b763a8bb13735aaa
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spelling	oai:doaj.org-article:2abe17b337194922b763a8bb13735aaa2021-11-25T06:02:02ZBiological time series analysis using a context free language: applicability to pulsatile hormone data.1932-620310.1371/journal.pone.0104087https://doaj.org/article/2abe17b337194922b763a8bb13735aaa2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25184442/?tool=EBIhttps://doaj.org/toc/1932-6203We 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.Dennis A DeanGail K AdlerDavid P NguyenElizabeth B KlermanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 9, p e104087 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dennis A Dean Gail K Adler David P Nguyen Elizabeth B Klerman Biological time series analysis using a context free language: applicability to pulsatile hormone data.
description	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.
format	article
author	Dennis A Dean Gail K Adler David P Nguyen Elizabeth B Klerman
author_facet	Dennis A Dean Gail K Adler David P Nguyen Elizabeth B Klerman
author_sort	Dennis A Dean
title	Biological time series analysis using a context free language: applicability to pulsatile hormone data.
title_short	Biological time series analysis using a context free language: applicability to pulsatile hormone data.
title_full	Biological time series analysis using a context free language: applicability to pulsatile hormone data.
title_fullStr	Biological time series analysis using a context free language: applicability to pulsatile hormone data.
title_full_unstemmed	Biological time series analysis using a context free language: applicability to pulsatile hormone data.
title_sort	biological time series analysis using a context free language: applicability to pulsatile hormone data.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/2abe17b337194922b763a8bb13735aaa
work_keys_str_mv	AT dennisadean biologicaltimeseriesanalysisusingacontextfreelanguageapplicabilitytopulsatilehormonedata AT gailkadler biologicaltimeseriesanalysisusingacontextfreelanguageapplicabilitytopulsatilehormonedata AT davidpnguyen biologicaltimeseriesanalysisusingacontextfreelanguageapplicabilitytopulsatilehormonedata AT elizabethbklerman biologicaltimeseriesanalysisusingacontextfreelanguageapplicabilitytopulsatilehormonedata
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Biological time series analysis using a context free language: applicability to pulsatile hormone data.

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