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...
Guardado en:
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: | |
Acceso en línea: | https://doaj.org/article/2abe17b337194922b763a8bb13735aaa |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Measuring luteinising hormone pulsatility with a robotic aptamer-enabled electrochemical reader
por: Shaolin Liang, et al.
Publicado: (2019) -
Hypothalamic effects of progesterone on regulation of the pulsatile and surge release of luteinising hormone in female rats
por: Wen He, et al.
Publicado: (2017) -
DynPeak: an algorithm for pulse detection and frequency analysis in hormonal time series.
por: Alexandre Vidal, et al.
Publicado: (2012) -
Early turbulence and pulsatile flows enhance diodicity of Tesla’s macrofluidic valve
por: Quynh M. Nguyen, et al.
Publicado: (2021) -
Rumor Detection Based on Attention CNN and Time Series of Context Information
por: Yun Peng, et al.
Publicado: (2021)