Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (8): 226-232.doi: 10.11896/jsjkx.221000202

• Artificial Intelligence • Previous Articles     Next Articles

Fusion Neural Network-based Method for Predicting LncRNA-disease Association

LI Qiaojun1,2, ZHANG Wen3, YANG Wei1,2   

  1. 1 Henan Province Industrial Internet of Things Application Engineering Technology Research Centre,Nanyang,Henan 473000,China
    2 School of Electronic Information Engineering,Henan Polytechnic Institute,Nanyang,Henan 473000,China
    3 College of Informatics,Huazhong Agricultural University,Wuhan 430070,China
  • Received:2022-10-25 Revised:2022-12-23 Online:2023-08-15 Published:2023-08-02
  • About author:LI Qiaojun,born in 1983,master,associate professor,is a member of China Computer Federation.Her main research interests include neural network and big data mining.
  • Supported by:
    Henan Science and Technology Project(212102310086).

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Abstract: Aberrant expression of long non-coding RNA(LncRNA) is closely associated with the physiological and pathological processes of diseases.Identifying potential associations between LncRNA and diseases are helpful to understand the molecular pathogenesis of diseases.Previous researches were scarcely integrated with heterogeneous multi-source data and seldom learned high-dimensional feature representations.In this paper,we propose a new method named FNNLDA,which based on fusion neural networks(FNN) to predict the associated LncRNAs of candidate disease.FNNLDA integrates multiple data related to LncRNAs,diseases,and miRNAs.And employs the idea of multi-model fusion to learn high-level features of LncRNA-disease pairs by using two deep learning models:stacked self-encoder and fusion neural network,separately.Finally,fusing the prediction scores of the two modules to predict the LncRNA-disease associations.Five-fold cross-validation test show that the AUC value of FNNLDA method is 12.5%,15.1%,3.4% and 5.8% higher than that of SIMCLDA,MFLDA,CNNLDA and LRLSLDA,respectively.It indicates that this method has a significant improvement in LncRNA-disease prediction.The results of the study based on stomach cancer disease cases demonstrate that FNNLDA can effectively identify potential LncRNAs associated with disease.

Key words: LncRNA-disease, Association prediction, Fusion neural network, Stacked autoencoder

CLC Number: 

  • TP391
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