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Related Concept Videos

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...
Drug Concentration Versus Time Correlation01:15

Drug Concentration Versus Time Correlation

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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Protein Networks02:26

Protein Networks

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Protein Networks

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Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
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Related Experiment Video

Updated: May 15, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

A dynamic time order network for time-series gene expression data analysis.

Pengyue Zhang1, Raphaël Mourad, Yang Xiang

  • 1Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.

BMC Systems Biology
|January 4, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic time order network to identify early and late drug-responsive genes in cancer cells, crucial for discovering novel biomarkers. The model successfully identified potential estrogen response biomarkers.

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Related Experiment Videos

Last Updated: May 15, 2026

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
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A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Standard gene expression analysis struggles to differentiate early and late drug-responsive genes in cancer.
  • Distinguishing temporal gene responses is vital for identifying effective cancer biomarkers.

Purpose of the Study:

  • To develop a novel computational model for distinguishing and connecting early and late drug-responsive gene targets.
  • To identify potential biomarkers for cancer drug treatments, specifically focusing on estrogen response.

Main Methods:

  • A dynamic time order network model was developed using integrated differential equations.
  • Spline regression was employed for precise modeling of time-varying gene expression.
  • A likelihood ratio test was used to infer the temporal order of gene expression pairs.

Main Results:

  • The model successfully identified time-ordered relationships between genes within the cell cycle system.
  • Late-response genes related to estradiol (E2) treatment in breast cancer cells were discovered.
  • These late-response genes show promise as potential biomarkers for E2 treatment.

Conclusions:

  • The dynamic time order network effectively distinguishes temporal gene expression patterns.
  • The validated approach aids in the discovery of novel gene biomarkers for targeted cancer therapies.
  • This method offers a significant advancement in analyzing time-series gene expression data for biomarker identification.