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circRNA-binding protein site prediction based on multi-view deep learning, subspace learning and multi-view

Hui Li1, Zhaohong Deng2, Haitao Yang1

  • 1Jiangnan University, Wuxi, Jiangsu 214012, China.

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|September 27, 2021
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Summary
This summary is machine-generated.

This study introduces DMSK, a novel multi-view deep learning method to accurately predict RNA-binding protein (RBP) binding sites on circular RNAs (circRNAs). DMSK improves upon existing methods by integrating sequence, structure, and composition features for better RBP-circRNA interaction identification.

Keywords:
WGCCAcircRNA-RBP binding site predictiondeep feature learningmulti-view TSK fuzzy system

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Circular RNAs (circRNAs) play a role in autoimmune diseases by binding to RNA-binding proteins (RBPs).
  • Predicting RBP binding sites on circRNAs is crucial but challenging due to limited research and underutilized sequence characteristics.
  • Existing methods primarily focus on linear RNAs and fail to fully exploit circRNA structure and composition information.

Purpose of the Study:

  • To develop an efficient multi-view classification method for identifying circRNA-RBP interaction sites.
  • To address the limitations of existing methods in exploiting diverse circRNA features for binding site prediction.
  • To improve the accuracy of predicting specific RBP binding positions on circRNAs.

Main Methods:

  • Proposed a multi-view deep learning method (DMSK) integrating sequence, structure, and composition features.
  • Utilized pseudo-amino acid and pseudo-dipeptide sequences for feature extraction, RNAfold for secondary structure prediction, and sequence embedding for context-dependent features.
  • Employed a hybrid CNN-LSTM network for deep feature extraction and subspace learning (VW-GCCA) for common feature extraction.
  • Trained a multi-view TSK fuzzy system for classification and prediction of RBP binding sites.

Main Results:

  • The DMSK method demonstrated improved prediction performance compared to existing methods.
  • Successfully integrated multi-view features (sequence, structure, composition) for enhanced circRNA-RBP interaction site prediction.
  • The developed multi-view classifier effectively predicted specific RBP binding sites on circRNAs.

Conclusions:

  • DMSK offers a significant advancement in predicting circRNA-RBP binding sites by effectively utilizing multi-view data.
  • The study highlights the importance of integrating diverse sequence and structural features for accurate circRNA-RBP interaction analysis.
  • The proposed method provides a valuable tool for understanding the regulatory roles of circRNAs in diseases like autoimmune disorders.