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Predicting potential interactions between lncRNAs and proteins via combined graph auto-encoder methods.

Jingxuan Zhao1, Jianqiang Sun2, Stella C Shuai3

  • 1University of Science and Technology Liaoning, 66459, Anshan, China.

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Summary

This study introduces LPICGAE, a deep learning method to predict long noncoding RNA-protein interactions (LPIs). LPICGAE effectively identifies potential LPIs, outperforming existing computational methods.

Keywords:
LncRNAgraph auto-encoderlncRNA-protein interactionsproteinvariational graph auto-encoder

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • Long noncoding RNAs (lncRNAs) are crucial in human biological processes, interacting with proteins.
  • Experimental identification of lncRNA-protein interactions (LPIs) is time-consuming and costly.
  • Computational models are essential for efficient LPI prediction.

Purpose of the Study:

  • To develop a novel deep learning method, LPICGAE, for predicting human lncRNA-protein interactions (LPIs).
  • To improve the efficiency and accuracy of LPI prediction compared to existing methods.

Main Methods:

  • Utilized a combined graph auto-encoder (LPICGAE) framework.
  • Employed a variational graph auto-encoder for low-dimensional representation learning.
  • Reconstructed the adjacency matrix for interaction inference and alternately minimized loss functions.

Main Results:

  • Achieved an average Area Under the Receiver Operating Characteristic Curve (AUC) of 0.974.
  • Obtained an average accuracy of 0.985 in 5-fold cross-validation.
  • Demonstrated superior performance over six state-of-the-art computational methods.

Conclusions:

  • LPICGAE effectively predicts potential lncRNA-protein interactions.
  • The method offers a significant advancement in identifying LPIs.
  • LPICGAE can aid researchers in discovering novel lncRNA-protein relationships efficiently.