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Related Concept Videos

Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...
Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
Properties of Laplace Transform-II01:16

Properties of Laplace Transform-II

Time differentiation, convolution, integration, and periodicity are fundamental concepts in analyzing functions and signals over time. Each concept provides a unique perspective on how functions evolve, interact, and repeat, offering essential tools for various scientific and engineering applications.
Time differentiation involves analyzing the rate of change of a function over time. Mathematically, it is the derivative of a function with respect to time. This concept can be likened to tracking...
Discrete-Time Fourier Series01:20

Discrete-Time Fourier Series

The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

The plasma drug concentration-time curve is a crucial tool in pharmacokinetics, representing the drug's concentration in plasma at different time intervals post-administration. This curve illustrates the drug's journey from absorption into the systemic circulation, distribution to body tissues, and eventual elimination through excretion or biotransformation.
Two pivotal parameters are the minimum effective concentration (MEC) and the minimum toxic concentration (MTC). The MEC is the lowest drug...
Sampling Theorem01:15

Sampling Theorem

In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.

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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Periodicity detection method for small-sample time series datasets.

Daisuke Tominaga1

  • 1Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Aomi 2-4-7, Koto, Tokyo, 135-0064, Japan.

Bioinformatics and Biology Insights
|December 15, 2010
PubMed
Summary

This study presents a new algorithm for detecting periodicity in gene expression time series data. The method excels with limited data points, offering higher sensitivity and noise robustness compared to existing techniques.

Keywords:
circadian rhythmdiscrete Fourier transformgene expression time seriesinformation criterionperiodicity detection

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Gene expression time series often display periodic patterns driven by multiple signaling pathways.
  • Analyzing such data is challenging due to limited sampling points in DNA microarray experiments.
  • Existing periodicity detection methods typically require a substantial number of data points.

Purpose of the Study:

  • To evaluate the performance of a previously developed periodicity detection algorithm for small-sample gene expression time series data.
  • To compare this algorithm against conventional and novel methods using statistical analysis of harmonic power.
  • To demonstrate the algorithm's effectiveness in identifying periodic behavior with limited data.

Main Methods:

  • Utilized a detection algorithm based on the discrete Fourier transform and Akaike's information criterion.
  • Performed a comparative analysis with conventional and newly proposed periodicity detection methods.
  • Employed statistical analysis of harmonic power for evaluating performance on small-sample time series.

Main Results:

  • The developed algorithm demonstrates higher sensitivity for detecting multiple harmonics in gene expression data.
  • The method exhibits greater robustness against noise compared to other tested approaches.
  • Computational time is manageable for small-sample datasets, despite potential "combinatorial explosion" with larger datasets.

Conclusions:

  • The algorithm is effective for periodicity detection in gene expression time series with limited sampling points.
  • It offers superior sensitivity and noise resistance, making it a valuable tool for microarray data analysis.
  • The algorithm's script is publicly available for use in gene expression analysis.