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Updated: Jun 14, 2025

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MRDPDA: A multi-Laplacian regularized deepFM model for predicting piRNA-disease associations.

Yajun Liu1, Fan Zhang1, Yulian Ding2

  • 1Shaanxi Key Laboratory for Network Computing and Security Technology, School of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China.

Journal of Cellular and Molecular Medicine
|September 3, 2024
PubMed
Summary

This study introduces MRDPDA, a new computational method to predict piRNA-disease associations using limited data. MRDPDA effectively identifies potential biomarkers for various diseases, improving upon existing methods.

Keywords:
DeepFMLaplacian regularizedpiRNApiRNA‐disease association

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Area of Science:

  • Biochemistry
  • Genomics
  • Bioinformatics

Background:

  • PIWI-interacting RNAs (piRNAs) are crucial small non-coding RNAs involved in gene regulation and genome stability.
  • piRNAs show promise as biomarkers and therapeutic targets for numerous diseases.
  • Current computational methods struggle to accurately predict piRNA-disease associations (PDAs) due to data limitations.

Purpose of the Study:

  • To develop a novel computational method, MRDPDA, for predicting PDAs from limited, multi-source data.
  • To enhance the accuracy of PDA prediction by integrating a deep factorization machine (deepFM) with specialized regularization techniques.

Main Methods:

  • MRDPDA employs a deepFM model combined with regularizations from multiple limited datasets using separate Laplacians.
  • A unified objective function integrates embedding loss for similarity, optimizing the embedding for prediction.
  • A balanced benchmark dataset (piRPheno) and a deep autoencoder for negative set generation were utilized.

Main Results:

  • MRDPDA demonstrated superior performance compared to three state-of-the-art methods on the piRPheno dataset.
  • The method achieved high accuracy in both five-fold cross-validation and independent testing.
  • Case studies confirmed the practical effectiveness of MRDPDA in identifying PDAs.

Conclusions:

  • MRDPDA offers a robust and effective approach for predicting piRNA-disease associations, particularly with limited data.
  • The developed method has significant implications for identifying novel disease biomarkers and therapeutic strategies involving piRNAs.
  • This work advances computational approaches in small non-coding RNA research and disease association studies.