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Neural Regulation01:37

Neural Regulation

Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
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Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
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Related Experiment Video

Updated: Jun 14, 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

Multilevel support vector regression analysis to identify condition-specific regulatory networks.

Li Chen1, Jianhua Xuan, Rebecca B Riggins

  • 1Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA 22203, USA.

Bioinformatics (Oxford, England)
|April 9, 2010
PubMed
Summary
This summary is machine-generated.

A new multilevel support vector regression (ml-SVR) method reliably identifies condition-specific gene regulatory modules by reducing false positives. This computational biology approach improves upon existing methods for cancer studies.

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Systems biology

Background:

  • Identifying gene regulatory modules is crucial but challenging in computational biology.
  • Existing methods often yield high false positives, particularly in human cancer studies.
  • Novel strategies are required for accurate regulatory module identification.

Purpose of the Study:

  • To introduce a new computational approach, multilevel support vector regression (ml-SVR), for identifying condition-specific gene regulatory modules.
  • To enhance the reliability of regulatory module identification by suppressing false positive predictions.

Main Methods:

  • The ml-SVR approach employs a multilevel analysis strategy.
  • A two-stage support vector regression (SVR) method integrates binding motif and gene expression data.
  • A significance analysis procedure assesses the importance of each regulatory module.

Main Results:

  • ml-SVR demonstrated superior performance over existing methods in identifying regulators and target genes using simulation and yeast cell cycle data.
  • Application to breast cancer cell line data identified condition-specific regulatory modules linked to estrogen treatment.
  • The method successfully identified biologically relevant modules related to estrogen signaling in breast cancer.

Conclusions:

  • The ml-SVR approach offers a reliable strategy for condition-specific gene regulatory module identification.
  • This method shows promise for advancing computational biology, especially in cancer research.
  • The ml-SVR MATLAB package is available for download.