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Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions
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Prioritizing disease-causing microbes based on random walking on the heterogeneous network.

Xianjun Shen1, Yao Chen1, Xingpeng Jiang1

  • 1School of Computer, Central China Normal University, Wuhan 430079, China.

Methods (San Diego, Calif.)
|June 20, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a novel bioinformatics method to predict disease-microbe associations by analyzing complex biological networks. The approach effectively identifies potential microbial links to diseases like Type 2 diabetes, aiding in understanding disease mechanisms and developing therapies.

Keywords:
Disease networkHeterogeneous networkMicrobe networkRandom walk

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Area of Science:

  • Bioinformatics
  • Microbiology
  • Systems Biology

Background:

  • The human microbiota plays a crucial role in health and disease, yet known disease-microbe associations are limited.
  • Vast amounts of microbial sequencing data exist, but extracting meaningful disease associations remains challenging.
  • Network topology analysis is a promising bioinformatics approach for understanding complex biological relationships.

Purpose of the Study:

  • To develop and evaluate a novel computational method for prioritizing candidate microbes and predicting potential disease-microbe associations.
  • To enhance the understanding of microbe-disease interactions and their underlying mechanisms.
  • To identify novel microbial biomarkers for diseases such as Type 2 diabetes, Asthma, and Psoriasis.

Main Methods:

  • Construction of a heterogeneous network integrating known disease-microbe relationships, disease networks, and microbe networks.
  • Extension of the random walk algorithm to navigate and analyze the heterogeneous network.
  • Validation using leave-one-out cross-validation and Receiver Operating Characteristic (ROC) curve analysis.

Main Results:

  • The proposed method effectively predicts potential disease-microbe associations missed by analyzing microbe or disease networks in isolation.
  • The algorithm successfully ranked candidate microbes associated with Type 2 diabetes, Asthma, and Psoriasis.
  • Demonstrated the utility of network-based approaches in uncovering novel microbial roles in human diseases.

Conclusions:

  • The developed algorithm is effective in disclosing potential disease-microbe associations, offering a valuable tool for bioinformatics research.
  • The findings provide a foundation for improved clinical understanding, diagnosis, and therapeutic strategies related to the human microbiome.
  • This approach facilitates the discovery of new associations crucial for advancing personalized medicine and disease management.