ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2019, Vol. 56 ›› Issue (9): 1889-1896.doi: 10.7544/issn1000-1239.2019.20180834

Previous Articles     Next Articles

Prediction of Disease Associated Long Non-Coding RNA Based on HeteSim

Ma Yi, Guo Xingli, Sun Yutong, Yuan Qianqian, Ren Yang, Duan Ran, Gao Lin   

  1. (School of Computer Science and Technology, Xidian University, Xi’an 710071)
  • Online:2019-09-10
  • Supported by: 
    This work was supported by the General Program of the National Natural Science Foundation of China (61672407, 61672406) and the Key Program of the National Natural Science Foundation of China (61432010, 61532014).

Abstract: A growing number of evidences indicate that long non-coding RNAs (lncRNAs) play important roles in many biological processes, and mutations or dysfunction in these long non-coding RNAs can cause serious diseases in human bodies, such as various cancers. Biological methods have been exploited to predict potential associations between diseases and long non-coding RNAs, which are of great significance for the exploration of pathogenesis, diagnosis, treatment, prognosis and prevention of complex diseases. Heterogeneous information network is constructed based on the known disease-gene associations. The association strength between lncRNAs and diseases can be measured by an association score in the heterogeneous network. A simple method called HeteSim is applied to calculate the association scores between lncRNAs and diseases. The method used in this paper is based on all paths existing between a given disease and a given lncRNA. The experiments show that our method can achieve superior performance than state-of-art methods.Our predictions for ovarian cancer and gastric cancer have been verified by biological experiments, indicating the effectiveness of this method. The case studies indicate that our method can give informative clues for further investigation. In conclusion, the only paths based on known disease-gene associations are exploited, and it is can be expected that other disease associated information can also be integrated into our method, and better performance can be available.

Key words: disease-gene prediction, correlation calculation, heterogeneous information networks, HeteSim, meta-path

CLC Number: