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Related Experiment Video

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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

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An unsupervised partition method based on association delineated revised mutual information.

Jing Chen1, Guangcheng Xi

  • 1Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing, 086-100190, PR China. jing.chen@ia.ac.cn

BMC Bioinformatics
|February 12, 2009
PubMed
Summary
This summary is machine-generated.

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Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...

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This study introduces a novel mutual information algorithm to identify patterns in traditional Chinese medicine (TCM) patient data. The method accurately classifies syndromes, offering a significant advancement for TCM diagnostics and research.

Area of Science:

  • Computational Biology
  • Traditional Chinese Medicine

Background:

  • Syndromes are fundamental to Traditional Chinese Medicine (TCM) diagnosis and herbal remedy prescription.
  • Limited research exists on the number and nature of TCM syndromes.
  • Mutual information offers a correlative measure for statistical dependencies.

Purpose of the Study:

  • To develop and validate a novel algorithm for identifying TCM syndromes.
  • To analyze patient and rat data using an entropy partition method based on mutual information.
  • To address the challenge of defining and quantifying TCM syndromes.

Main Methods:

  • A revised mutual information approach was used to discriminate associations.
  • An entropy partition method was employed for self-organized pattern discovery.

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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

  • The algorithm was validated using patient diagnostic data and rat data.

    • Main Results:

    • The algorithm achieved high sensitivity (96.48%) in classifying patient data patterns with clinical significance.
    • Analysis of rat data revealed relationships between vascular endothelial function and the neuro-endocrine-immune (NEI) network.
    • The super-additivity of cluster by mutual information was proven, introducing N-class association for reduced complexity.

    Conclusions:

    • The developed algorithm effectively solves data analysis problems in TCM for both human and animal subjects.
    • This computational approach provides a robust solution for syndrome identification in TCM.
    • The findings highlight the potential of mutual information in understanding complex biological networks within TCM.