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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.
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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Related Experiment Video

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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

Predicting disease phenotypes based on the molecular networks with condition-responsive correlation.

Sejoon Lee1, Eunjung Lee, Kwang H Lee

  • 1Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea. sejoon@biosoft.kaist.ac.kr

International Journal of Data Mining and Bioinformatics
|May 6, 2011
PubMed
Summary

This study introduces a novel network-based classification method that analyzes both gene expression levels and their interactions. This approach improves disease modeling and classification compared to traditional gene-centric methods.

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In Vivo Modeling of the Morbid Human Genome using Danio rerio
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Published on: August 24, 2013

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Network-based methods are used to analyze complex biological data, especially with limited sample sizes.
  • Previous methods primarily focused on gene expression levels, overlooking interaction dynamics.

Purpose of the Study:

  • To develop a novel network-based classification approach.
  • To integrate gene expression levels and condition-responsive correlations (CRCs) for improved classification.

Main Methods:

  • Proposed a novel network-based classification method.
  • Identified condition-responsive interactions (edges) and discriminative expression levels (nodes) across phenotypes.
  • Utilized Condition-Responsive Correlations (CRCs) for edge analysis.

Main Results:

  • The novel method focuses on both discriminative nodes and condition-responsive edges.
  • Modules with condition-responsive interactions were identified as potential disease models.
  • Demonstrated improved classification performance over conventional gene-centric methods.

Conclusions:

  • Network-based classification integrating node expression and edge correlations offers a powerful approach.
  • Condition-responsive interactions are crucial for building accurate molecular models of diseases.
  • The proposed method enhances disease classification and modeling capabilities.