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Related Experiment Video

Updated: Sep 11, 2025

Network Pharmacology Prediction and Experimental Validation of Trichosanthes-Fritillaria thunbergii Action Mechanism Against Lung Adenocarcinoma
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Microbe-Drug Association Prediction Model Based on Adaptive Network Fusion of Structural-Topological Information With

Liugen Wang, Bai Zhang, Hanwen Wu

    IEEE Transactions on Computational Biology and Bioinformatics
    |August 14, 2025
    PubMed
    Summary
    This summary is machine-generated.

    A new computational model, ANFAISMDA, efficiently identifies microbe-drug associations to combat microbial drug resistance. This approach aids in developing better therapies and monitoring resistance patterns.

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    Area of Science:

    • Computational biology
    • Microbiology
    • Pharmacology

    Background:

    • Microbial drug resistance is a critical global health issue.
    • Understanding microbe-drug relationships is key to developing effective treatments and combating resistance.
    • Efficient computational methods are needed for identifying these associations.

    Purpose of the Study:

    • To propose ANFAISMDA, a novel computational model for identifying potential microbe-drug associations.
    • To enhance the accuracy and efficiency of predicting microbe-drug interactions.
    • To support the optimization of antimicrobial therapy and resistance monitoring.

    Main Methods:

    • Utilized microbial 16S rRNA gene sequences and drug SMILES structures for feature extraction.
    • Employed a symmetric matrix completion algorithm to obtain topological information.
    • Developed an adaptive network fusion algorithm to integrate structural and topological data.
    • Implemented an integration strategy to improve prediction performance.

    Main Results:

    • ANFAISMDA demonstrated reliability and validity through experimental verification.
    • The model successfully identified potential microbe-drug associations.
    • Visualization revealed interesting patterns in the top 50 microbe-drug associations.

    Conclusions:

    • The ANFAISMDA model is a valuable tool for optimizing antimicrobial therapy and monitoring drug resistance.
    • The approach contributes to a deeper understanding of microbe-drug interaction mechanisms.
    • This study addresses significant challenges posed by bacterial resistance.