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Updated: Jul 3, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
Published on: March 7, 2018
An approach to inferring transcriptional regulation among genes from large-scale expression data
Javier Herrero1, Ramón Díaz-Uriarte, Joaquín Dopazo
1Unidad de Bioinformática, Centro Nacional de Investigaciones Oncológicas (CNIO). Melchor Fernández Almagro 3, Madrid 28029, Spain.
Abstract:
The use of DNA microarrays opens up the possibility of measuring the expression levels of thousands of genes simultaneously under different conditions. Time-course experiments allow researchers to study the dynamics of gene interactions. The inference of genetic networks from such measures can give important insights for the understanding of a variety of biological problems. Most of the existing methods for genetic network reconstruction require many experimental data points, or can only be applied to the reconstruction of small subnetworks. Here we present a method that reduces the dimensionality of the dataset and then extracts the significant dynamic correlations among genes. The method requires a number of points achievable in common time-course experiments.
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