Molecular Genetics and Genomics

, Volume 294, Issue 6, pp 1477–1486 | Cite as

LLCLPLDA: a novel model for predicting lncRNA–disease associations

  • Guobo Xie
  • Shuhuang Huang
  • Yu LuoEmail author
  • Lei MaEmail author
  • Zhiyi Lin
  • Yuping Sun
Original Article


Long noncoding RNAs play a significant role in the occurrence of diseases. Thus, studying the relationship prediction between lncRNAs and disease is becoming more popular. Researchers hope to determine effective treatments by revealing the occurrence and development of diseases at the molecular level. However, the traditional biological experimental way to verify the association between lncRNAs and disease is very time-consuming and expensive. Therefore, we developed a method called LLCLPLDA to predict potential lncRNA–disease associations. First, locality-constrained linear coding (LLC) is leveraged to project the features of lncRNAs and diseases to local-constraint features, and then, a label propagation (LP) strategy is used to mix up the initial association matrix and the obtained features of lncRNAs and diseases. To demonstrate the performance of our method, we compared LLCLPLDA with five methods in the leave-one-out cross-validation and fivefold cross-validation scheme, and the experimental results show that the proposed method outperforms the other five methods. Additionally, we conducted case studies on three diseases: cervical cancer, gliomas, and breast cancer. The top five predicted lncRNAs for cervical cancer and gliomas were verified, and four of the five lncRNAs for breast cancer were also confirmed.


Locality-constrained linear coding Label propagation lncRNA–disease associations Prediction 



This work was supported by the National Natural Science Foundation of China (618002072, 61702112), the Natural Science Foundation of Guangdong Province (2018A030313389), the Science and Technology Plan Project of Guangdong Province (2017A040405050, 2016B030306004, 2016B030301008), and the Opening Project of the Guangdong Province Key Laboratory of Computational Science (2018012).

Compliance with ethical standards

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Computer ScienceGuangdong University of TechnologyGuangzhouChina
  2. 2.Institute of AutomationChinese Academy of SciencesBeijingChina

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