Computer Science ›› 2022, Vol. 49 ›› Issue (2): 265-271.doi: 10.11896/jsjkx.201100132

• Artificial Intelligence • Previous Articles     Next Articles

Ensemble Regression Decision Trees-based lncRNA-disease Association Prediction

REN Shou-peng1, LI Jin1, WANG Jing-ru1, YUE Kun2   

  1. 1 School of Software,Yunnan University,Kunming 650091,China
    2 School of Information Science & Engineering,Yunnan University,Kunming 650091,China
  • Received:2020-11-18 Revised:2021-05-30 Online:2022-02-15 Published:2022-02-23
  • About author:REN Shou-peng,born in 1997,master.His main research interests include bioinformatics and machine learning.
    LI Jin,born in 1975,Ph.D,professor.His main research interests include machine learning and bioinformatics.
  • Supported by:
    Foundation of National Natural Science Foundation of China United Yunnan Province(U1802271),Foundation of Outstanding Youth Project of Basic Research in Yunnan Province(2019FJ011) and Foundation of Key Project of Basic Research in Yunnan Province(201901BB050052).

Abstract: Long non-coding RNA (lncRNA) plays an important role in various complex human diseases.The development of effective prediction methods to infer the potential associations between lncRNA and diseases will not only help biologists understand the pathogenesis of diseases,but also contribute to the diagnosis,prevention,and treatment of human diseases.In this paper,an ensemble regression decision tree-based lncRNA-disease association method (ERDTLDA) is proposed to solve the lncRNA-disease association problem.First,ERDTLDA uses the open-source data of lncRNA to construct lncRNA,disease similarity matrix,lncRNA-disease association matrix respectively.Then,we obtain lncRNA,disease feature representations from these matrices.Principal component analysis is further exploited for feature extraction.Finally,a CART regression decision tree is used to yield association scores.An ensemble strategy for multiple decision trees is proposed to further improve the accuracy of our model.The results of LOOCV experiments show that the AUC of our method on three real lncRNA-disease datasets are 0.905 5,0.896 9 and 0.912 9 respectively,which are 6.46%,5.4% and 6.02% higher than the existing methods,respectively.Additionally,breast cancer,lung cancer,and gastric cancer are also used as case studies to further verify the accuracy and effectiveness of ERDTLDA.

Key words: Association prediction, CART decision tree, Ensemble learning, Feature extraction, lncRNA-disease

CLC Number: 

  • TP391
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