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Related Experiment Video

Updated: Feb 16, 2026

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Published on: April 7, 2023

Novel human microbe-disease association prediction using network consistency projection.

Wenzheng Bao¹, Zhichao Jiang¹, De-Shuang Huang²

¹Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Caoan Road 4800, Shanghai, 201804, China.

BMC Bioinformatics

|January 4, 2018

Summary

Identifying microbe-disease associations is crucial for understanding human diseases. A new computational model, Network Consistency Projection for Human Microbe-Disease Association prediction (NCPHMDA), accurately predicts these links, aiding disease diagnosis and prognosis.

Keywords:

Association prediction Disease Microbe Network consistency projection

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Area of Science:

Microbiology
Computational Biology
Human Health

Background:

Microbial community imbalance is linked to complex human diseases.
Accurate microbe-disease association identification is vital for disease pathology understanding, diagnosis, and prognosis.
Limited computational models exist for large-scale microbe-disease association prediction.

Purpose of the Study:

To develop a computational model for predicting human microbe-disease associations.
To leverage the assumption that microbes with similar functions share similar association patterns with diseases.

Main Methods:

Proposed the Network Consistency Projection for Human Microbe-Disease Association prediction (NCPHMDA) model.
Integrated known microbe-disease associations and Gaussian interaction profile kernel similarity for microbes and diseases.
Employed cross-validation techniques (global LOOCV, local LOOCV, 5-fold CV).

Main Results:

NCPHMDA achieved high performance with AUCs of 0.9039, 0.7953, and an average AUC of 0.8918.
Case studies on colon cancer, asthma, and type 2 diabetes showed top predictions validated by clinical literature (9/10, 9/10, 8/10).

Conclusions:

NCPHMDA is a non-parametric, universal network-based method.
The model can predict associated microbes for diseases without requiring negative samples.
NCPHMDA is expected to serve as an effective biological resource for clinical guidance.