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MANIEA: a microbial association network inference method based on improved Eclat association rule mining algorithm.

Maidi Liu1, Yanqing Ye1, Jiang Jiang1

  • 1College of Systems Engineering, National University of Defense Technology, 410073 Changsha, China.

Bioinformatics (Oxford, England)
|May 11, 2021
PubMed
Summary
This summary is machine-generated.

We developed MANIEA, a novel microbial association network inference method. It efficiently mines positive and negative microbial rules, improving network accuracy and reflecting complex interactions for better microbiome analysis.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Microbiome systems are complex networks requiring accurate network inference for applications in clinical diagnosis and disease treatment.
  • Current methods often focus on pairwise associations, failing to capture comprehensive microbial interactions and potentially overlooking dominant microorganisms.
  • Existing association rule mining methods have limitations in efficiency, handling negative correlations, and result redundancy, hindering high-quality network inference.

Purpose of the Study:

  • To propose a novel microbial association network inference method, MANIEA, capable of mining both positive and negative microbial association rules.
  • To develop a method for transforming association rules into microbial association networks that effectively represent co-occurrence and causal correlations.
  • To evaluate the performance of MANIEA against existing popular network inference methods using authentic microbial abundance datasets.

Main Methods:

  • An improved Eclat algorithm was utilized for mining positive and negative microbial association rules.
  • A novel approach was developed to translate mined association rules into microbial association networks, visualizing complex relationships.
  • MANIEA was experimentally validated on three real-world microbial abundance datasets.

Main Results:

  • MANIEA demonstrated superior performance in mining both positive and negative microbial association rules compared to existing methods.
  • The proposed transformation method effectively represents co-occurrence and causal correlations within the inferred microbial association networks.
  • MANIEA exhibited advantages in correlation forms, computational efficiency, adjustability, and overall network characteristics.

Conclusions:

  • MANIEA offers an effective and efficient approach for microbial association network inference, overcoming limitations of previous methods.
  • The method provides a more comprehensive understanding of microbial interactions by considering both positive and negative correlations.
  • MANIEA enhances the accuracy and utility of microbial association networks for various biological and clinical applications.