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EDLMFC: an ensemble deep learning framework with multi-scale features combination for ncRNA-protein interaction

Jingjing Wang1, Yanpeng Zhao1, Weikang Gong1

  • 1Department of Biomedical Engineering, Faculty of Environment and Life, Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing University of Technology, Beijing, 100124, China.

BMC Bioinformatics
|March 20, 2021
PubMed
Summary

A new ensemble deep learning method, EDLMFC, accurately predicts non-coding RNA (ncRNA) and protein interactions. This computational approach offers a faster alternative to experimental methods for understanding ncRNA functions.

Keywords:
Conjoint k-merEnsemble deep learningIndependent testMulti-scale features combinationncRNA–protein interactionsncRNA–protein networks

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Non-coding RNA (ncRNA) and protein interactions are crucial in biological processes.
  • Experimental prediction methods for ncRNA-protein interactions are inefficient.
  • Computational approaches are needed for accurate and rapid prediction.

Purpose of the Study:

  • To develop an efficient computational method for predicting ncRNA-protein interactions.
  • To improve the accuracy and speed of ncRNA-protein interaction prediction.

Main Methods:

  • An ensemble deep learning model named EDLMFC was developed.
  • Multi-scale features including primary sequence, secondary structure, and tertiary structure were integrated.
  • Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BLSTM) were combined.

Main Results:

  • EDLMFC achieved high accuracy (93.8% on RPI1807, 89.7% on NPInter v2.0, 86.1% on RPI488).
  • The method demonstrated effectiveness in predicting interactions across different organisms.
  • EDLMFC successfully identified hub ncRNAs and proteins in Mus musculus networks.

Conclusions:

  • EDLMFC significantly enhances the accuracy of ncRNA-protein interaction prediction.
  • The method provides valuable insights for ncRNA function research.
  • Source code and datasets are publicly available for reproducibility.