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Related Experiment Video

Updated: Jul 12, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Modeling gene expression regulatory networks with the sparse vector autoregressive model.

André Fujita1, João R Sato, Humberto M Garay-Malpartida

  • 1Institute of Mathematics and Statistics, University of São Paulo, Rua do Matão, 1010 - São Paulo, 05508-090, SP, Brazil. fujita@ime.usp.br

BMC Systems Biology
|September 1, 2007
PubMed
Summary

The Sparse Vector Autoregressive (SVAR) model accurately infers gene regulatory networks from time-series microarray data, even with fewer samples than genes. This method controls false positives and identifies gene targets, overcoming limitations of existing models.

Related Experiment Videos

Last Updated: Jul 12, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Understanding molecular mechanisms requires detailed gene product networks.
  • Estimating gene regulatory networks from time-series microarray data presents challenges.
  • Existing methods struggle with inferring information flow and handling large networks with limited samples.

Purpose of the Study:

  • To address limitations in current gene regulatory network inference methods.
  • To propose a novel statistical model for analyzing gene expression data.
  • To accurately infer gene-gene interactions from time-series microarray data.

Main Methods:

  • Developed the Sparse Vector Autoregressive (SVAR) model.
  • Applied SVAR to simulated and real gene expression datasets.
  • Utilized time-series microarray data for network estimation.

Main Results:

  • SVAR successfully infers true positive regulatory edges, even when samples are fewer than genes.
  • The method effectively controls for false positives, outperforming existing approaches.
  • Applied to HeLa cell cycle data, SVAR identified known transcription factor targets.

Conclusions:

  • SVAR models gene regulatory networks effectively, particularly in low-sample-to-gene ratio scenarios.
  • The model naturally infers partial Granger causalities without prior information.
  • Introduced a statistical test for false discovery rate control, a novel capability for gene regulatory network models.