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Related Concept Videos

Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein Networks02:26

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
IP3/DAG Signaling Pathway01:11

IP3/DAG Signaling Pathway

Membrane lipids such as phosphatidylinositol (PI) are precursors for several membrane-bound and soluble second messengers. Specific kinases phosphorylate PI and produce phosphorylated inositol phospholipids. One such inositol phospholipids are the  phosphatidylinositol-4,5 bisphosphate [PI(4,5)P2], present in the inner half of the lipid bilayer. Upon ligand binding, GPCR stimulates Gq proteins to turn on phospholipase Cꞵ. Activated phospholipase Cꞵ cleaves PI(4,5)P2 and produces two-second...
Drug Discovery: Overview01:26

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

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Related Experiment Video

Updated: May 28, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Published on: December 7, 2021

DART: Denoising Algorithm based on Relevance network Topology improves molecular pathway activity inference.

Yan Jiao1, Katherine Lawler, Gargi S Patel

  • 1Statistical Genomics Group, Paul O'Gorman Building, UCL Cancer Institute, University College London, 72 Huntley Street, London WC1E 6BT, UK.

BMC Bioinformatics
|October 21, 2011
PubMed
Summary

Denoising pathway information before analysis significantly improves the accuracy of molecular pathway activity predictions. This approach, using the Denoising Algorithm based on Relevance network Topology (DART), enhances understanding of cancer genomics and clinical outcomes.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Inferring molecular pathway activity is crucial for simplifying genomic data, understanding clinical outcome heterogeneity, and linking molecular profiles to cancer imaging traits.
  • Current methods integrate molecular profiles with interaction data or pathway databases, but optimal use of pathway knowledge remains unclear.

Purpose of the Study:

  • To introduce and evaluate a novel algorithm, DART, for denoising pathway information prior to pathway activity inference.
  • To demonstrate the benefits of denoising pathway knowledge for improving the accuracy of pathway activity predictions in cancer genomics.

Main Methods:

  • Developed the Denoising Algorithm based on Relevance network Topology (DART) to filter noise in pathway information.
  • Applied DART to simulated and real multidimensional cancer genomic data, comparing its performance against algorithms lacking prior information relevance assessment.
  • Utilized the Netpath resource and breast cancer gene expression data to assess DART's robustness in inferring pathway activity correlations.

Main Results:

  • DART significantly improves pathway activity predictions by denoising prior pathway information before analysis.
  • Genes encoding hubs in expression correlation networks are identified as more reliable markers of pathway activity.
  • DART reveals a hypothesized association between estrogen signaling and mammographic density in ER+ breast cancer.

Conclusions:

  • Evaluating the consistency of pathway database information within molecular tumor profiles substantially enhances pathway activity inference.
  • A denoising strategy, like DART, should be integrated into approaches for inferring pathway activity from prior pathway models.