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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

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
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Related Experiment Video

Updated: May 18, 2026

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
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Detecting service chains and feature interactions in sensor-driven home network services.

Takuya Inada1, Hiroshi Igaki, Kosuke Ikegami

  • 1Graduate School of System Informatics, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe, Hyogo 657-8510, Japan. inada@ws.cs.kobe-u.ac.jp

Sensors (Basel, Switzerland)
|September 27, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a framework to detect service chains in sensor-driven systems using event-condition-action rules. It also presents a method to identify feature interactions within these chains by measuring state deviations.

Keywords:
detectionfeature interactionshome network systemsensor-driven servicesmart homevalidation

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Area of Science:

  • Computer Science
  • Software Engineering
  • Distributed Systems

Background:

  • Sensor-driven services can trigger cascading effects due to interdependencies.
  • Understanding these service chains is crucial for managing system behavior and potential issues.

Purpose of the Study:

  • To develop a framework for formalizing and detecting service chains in sensor-driven systems.
  • To propose a method for identifying feature interactions within detected service chains.

Main Methods:

  • Utilized event-condition-action (ECA) rules to formalize and detect service chains.
  • Characterized service chain deviation by quantifying the gap between expected and actual service states to identify feature interactions.

Main Results:

  • Successfully detected 11 distinct service chains within 7 practical sensor-driven services.
  • Identified 6 instances of feature interactions among the detected service chains.

Conclusions:

  • The proposed framework and method are effective in detecting service chains and feature interactions in sensor-driven environments.
  • This work provides valuable tools for analyzing and managing the complexity of interconnected sensor-driven services.