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Related Concept Videos

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A signal x(t) is a set of data or a time function representing a variable of interest. Signals typically convey information about a phenomenon, such as atmospheric temperature, humidity, human voice, television images, a dog's bark, or birdsongs. More generally, a signal can be a function of more than one independent variable. For instance, images depend on horizontal and vertical positions and can be regarded as two-dimensional signals. However, this text will focus on one-dimensional...
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Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging
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TENET: topological feature-based target characterization in signalling networks.

Huey Eng Chua1, Sourav S Bhowmick1, Lisa Tucker-Kellogg2

  • 1School of Computer Engineering, Nanyang Technological University.

Bioinformatics (Oxford, England)
|June 17, 2015
PubMed
Summary

Tenet identifies drug targets in biochemical networks using network topology. This approach surpasses traditional methods by analyzing network connectivity for better target characterization.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Traditional target characterization relies on molecular features and biological function, requiring extensive experiments.
  • These methods are often infeasible for large networks with poorly understood proteins and ignore network connectivity.
  • Systems biology provides network connectivity data, complementing traditional approaches.

Purpose of the Study:

  • To present Tenet (Target charactErization using NEtwork Topology), a novel network-based approach for target characterization.
  • To leverage network topology features for identifying and characterizing potential drug targets.
  • To develop a model that predicts the suitability of molecules for drug targeting based on network properties.

Main Methods:

  • Tenet computes topological features of nodes within signalling networks.
  • A support vector machine (SVM) approach is used to identify predictive topological features.
  • A characterization model is generated to quantify the likelihood of a node being a target based on feature importance and combination.

Main Results:

  • Tenet effectively characterizes known targets in signalling networks using topological features.
  • The developed model identifies key topological features for target discrimination.
  • Empirical studies on BioModels signalling networks demonstrate Tenet's effectiveness and superiority over state-of-the-art methods.

Conclusions:

  • Network topology analysis offers valuable insights for drug target characterization.
  • Tenet provides a powerful and efficient method for identifying potential drug targets in complex biological networks.
  • The Tenet approach complements traditional methods and enhances drug discovery research.