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A Bidirectional Label Propagation Based Computational Model for Potential Microbe-Disease Association Prediction.

Lei Wang1,2, Yuqi Wang1, Hao Li1

  • 1Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, China.

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|April 27, 2019
PubMed
Summary

This study introduces NBLPIHMDA, a novel computational model for predicting microbe-disease associations. The model shows promising results in identifying potential microbial links to human diseases, aiding early diagnosis and prognosis.

Keywords:
5-fold cross validationCOPDbidirectional label propagationleave-one-out cross validationmicrobe-disease association

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Area of Science:

  • Microbiology
  • Computational Biology
  • Bioinformatics

Background:

  • Clinical observations increasingly link microbes to human diseases.
  • Understanding microbe-disease associations is crucial for disease prevention, diagnosis, and prognosis.

Purpose of the Study:

  • To propose a novel computational model, NBLPIHMDA, for inferring potential microbe-disease associations.
  • To leverage known microbe-disease associations to build a predictive framework.

Main Methods:

  • Constructed microbe and disease similarity networks using Gaussian interaction profile kernel similarity.
  • Applied bidirectional label propagation on the constructed networks to predict associations.
  • Validated the model on the Human Microbe-Disease Association database (HMDAD).

Main Results:

  • NBLPIHMDA achieved high performance in leave-one-out and 5-fold cross-validation (AUCs of 0.8777 and 0.8958 ± 0.0027).
  • Outperformed existing methods like LRLSHMDA, BiRWMP, and KATZHMDA.
  • Case studies on asthma, colorectal carcinoma, and COPD showed high accuracy in predicting known microbe-disease links.

Conclusions:

  • NBLPIHMDA demonstrates significant potential for predicting novel microbe-disease associations.
  • The model offers a valuable tool for advancing research in microbiome-related diseases.
  • Findings support the utility of NBLPIHMDA in disease prevention and early diagnosis strategies.