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Deep-belief network for predicting potential miRNA-disease associations.

Xing Chen1, Tian-Hao Li2, Yan Zhao2

  • 1Artificial Intelligence Research Institute, China University of Mining and Technology.

Briefings in Bioinformatics
|May 22, 2021
PubMed
Summary

A new deep-belief network model (DBNMDA) effectively predicts microRNA-disease associations by integrating all available data, outperforming existing methods and aiding disease research.

Keywords:
association predictiondeep-belief networkdiseasemicroRNAsupervised fine-tuningunsupervised pre-training

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • MicroRNAs (miRNAs) are crucial in disease pathogenesis, development, diagnosis, and treatment.
  • Computational methods offer a cost-effective alternative to traditional experiments for predicting miRNA-disease associations.

Purpose of the Study:

  • To develop an advanced computational model, Deep Belief Network for miRNA-Disease Association prediction (DBNMDA), for identifying potential miRNA-disease links.
  • To improve prediction accuracy by leveraging information from all miRNA-disease pairs during model pre-training.

Main Methods:

  • Constructed feature vectors for all miRNA-disease pairs to pre-train Restricted Boltzmann Machines.
  • Fine-tuned the Deep Belief Network (DBN) using positive and selected negative samples to generate prediction scores.
  • Employed global leave-one-out cross-validation (LOOCV), local LOOCV, and 5-fold cross-validation for performance evaluation.

Main Results:

  • DBNMDA achieved high AUC values: 0.9104 (global LOOCV), 0.8232 (local LOOCV), and 0.9048 ± 0.0026 (5-fold CV).
  • Performance metrics surpassed those of previous computational models.
  • Case studies demonstrated high accuracy, with 84% (breast neoplasms), 100% (lung neoplasms), and 88% (esophageal neoplasms) of top predictions validated by recent literature.

Conclusions:

  • DBNMDA is a robust and effective computational method for predicting potential miRNA-disease associations.
  • The model's innovative pre-training strategy mitigates issues related to limited known associations.
  • DBNMDA offers a valuable tool for advancing research in miRNA-related diseases.