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MDADP: A Webserver Integrating Database and Prediction Tools for Microbe-Disease Associations
IEEE Journal of Biomedical and Health Informatics
|March 7, 2022
Summary
This study introduces MDADP, a novel webserver and database for identifying microbe-disease associations (MDAs). It offers interactive tools and computational models to predict potential microbial links to diseases, aiding research in microbiology and health.
Area of Science:
- Microbiology
- Computational Biology
- Bioinformatics
Background:
- Microbiota significantly influence human health.
- Computational methods for microbe-disease association (MDA) prediction exist but are limited by data.
- Lack of visual predictive tools hinders MDA research.
Purpose of the Study:
- To propose MDADP, a novel webserver for identifying latent microbe-disease associations (MDAs).
- To develop a new MDA database and interactive prediction tools.
- To provide a valuable resource for microbiology and disease research.
Main Methods:
- Manual collection of 2019 known MDAs between 58 diseases and 703 microbes.
- Integration of eight computational models using average ranking and co-confidence methods.
- Development of a webserver with interactive features and prediction tools.
Main Results:
- MDADP incorporates a manually curated MDA database.
- Eight computational models are integrated for predicting potential MDAs.
- The platform offers interactive access to MDA entities and candidate microbe identification.
Conclusions:
- MDADP is the first online platform combining a new MDA database with comprehensive prediction tools.
- It serves as a valuable resource for researchers in microbiology and disease-related fields.
- Facilitates the identification of potential microbe-disease links and candidate microbes.
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