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C3: connect separate connected components to form a succinct disease module.

Bingbo Wang1, Jie Hu2, Yajun Wang3

  • 1School of Computer Science and Technology, Xidian University, Xi'an, People's Republic of China. bingbowang@xidian.edu.cn.

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Summary
This summary is machine-generated.

A new method connects fragmented disease proteins into a complete module, improving disease understanding and drug target identification. This approach reveals a consistent connectivity pattern across diseases and multi-omics data.

Keywords:
Biological networkConnectivity patternDisease module

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Area of Science:

  • Systems biology
  • Network medicine
  • Computational biology

Background:

  • Identifying precise disease modules is crucial for understanding disease mechanisms and discovering drug targets.
  • Incomplete human interactome data leads to fragmented disease modules, posing challenges in detecting complete disease neighborhoods.
  • The connectivity patterns within disease modules remain an open research question.

Purpose of the Study:

  • To address the challenge of fragmented disease modules in incomplete human interactomes.
  • To propose a novel method for detecting complete and succinct disease modules.
  • To validate a conserved connectivity pattern of disease modules across various diseases and multi-omics data.

Main Methods:

  • Exploratory analysis of protein-protein interaction networks to identify intermediate nodes connecting separate components.
  • Development of the connect separate connected components (C3) method based on topological properties of intermediate nodes.
  • Application of the C3 method to a large corpus of diseases and analysis of multi-omics data, including The Cancer Genome Atlas.

Main Results:

  • Most separate connected components of disease-associated proteins can be connected via a few intermediate nodes to form a complete disease module.
  • The C3 method effectively detects succinct and purer disease modules compared to existing methods.
  • The identified connectivity pattern holds true for perturbed genes in multi-omics data, including cancer genomics.

Conclusions:

  • The C3 tool successfully identifies connected disease neighborhoods for 299 diseases and cancers using multi-omics data.
  • The method enhances understanding of interconnections between phenotypically related genes across different omics datasets.
  • C3 aids in studying complex pathological processes and discovering novel therapeutic targets.