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Quantifying Cytoskeleton Dynamics Using Differential Dynamic Microscopy
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Kernel differential subgraph reveals dynamic changes in biomolecular networks.

Jiang Xie1, Dongfang Lu1, Jiaxin Li1

  • 1* School of Computer Engineering and Science, Shanghai University, 99 Shang Da Road, Shanghai 200444, P. R. China.

Journal of Bioinformatics and Computational Biology
|December 29, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to find key gene network changes in diseases like cancer. It identifies crucial genes and modules involved in non-small cell lung cancer progression.

Keywords:
Kernel differential subgraphcomplex biological networksdynamic processtopology differential value

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Area of Science:

  • Systems biology
  • Network science
  • Genomics

Background:

  • Major diseases, including cancer, pose significant threats, yet their underlying mechanisms remain unclear.
  • Complex biological networks offer insights into disease dynamics, but analyzing networks with changing components is challenging.

Purpose of the Study:

  • To propose a novel topology-based kernel differential subgraph (TKDS) method for identifying core disease modules in gene regulatory networks with varying nodes.
  • To address limitations of existing methods by considering both common and different nodes between networks.

Main Methods:

  • Developed the TKDS method to analyze gene regulatory networks with different node sets.
  • Calculated differential values (D-values) for topological changes in common nodes and identified similar gene pairs for different nodes.
  • Applied TKDS to non-small cell lung cancer (NSCLC) gene regulatory networks.

Main Results:

  • Identified 30 genes strongly associated with NSCLC.
  • Extracted kernel differential subgraphs (KDSs) for both cancer and normal states.
  • Revealed two significant functional modules essential for NSCLC processes, validated by Gene Ontology (GO) analysis and literature mining.

Conclusions:

  • The TKDS method effectively identifies essential KDSs by considering both shared and unique genes between biological networks.
  • TKDS offers a novel approach for pinpointing disease-related genes and modules, with potential applications in predicting critical genes for other diseases.
  • The identified KDSs and genes are crucial for understanding NSCLC pathogenesis.