Mathematical Biosciences and Engineering (Mar 2022)

RLF-LPI: An ensemble learning framework using sequence information for predicting lncRNA-protein interaction based on AE-ResLSTM and fuzzy decision

  • Jinmiao Song,
  • Shengwei Tian,
  • Long Yu,
  • Qimeng Yang ,
  • Qiguo Dai ,
  • Yuanxu Wang,
  • Weidong Wu,
  • Xiaodong Duan

DOI
https://doi.org/10.3934/mbe.2022222
Journal volume & issue
Vol. 19, no. 5
pp. 4749 – 4764

Abstract

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Long non-coding RNAs (lncRNAs) play a regulatory role in many biological cells, and the recognition of lncRNA-protein interactions is helpful to reveal the functional mechanism of lncRNAs. Identification of lncRNA-protein interaction by biological techniques is costly and time-consuming. Here, an ensemble learning framework, RLF-LPI is proposed, to predict lncRNA-protein interactions. The RLF-LPI of the residual LSTM autoencoder module with fusion attention mechanism can extract the potential representation of features and capture the dependencies between sequences and structures by k-mer method. Finally, the relationship between lncRNA and protein is learned through the method of fuzzy decision. The experimental results show that the ACC of RLF-LPI is 0.912 on ATH948 dataset and 0.921 on ZEA22133 dataset. Thus, it is demonstrated that our proposed method performed better in predicting lncRNA-protein interaction than other methods.

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