ILNCSIM: Improved lncRNA functional similarity calculation model

Yu An Huang, Xing Chen, Zhu Hong You, De Shuang Huang, Chun Chung Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

100 Citations (Scopus)

Abstract

Increasing observations have indicated that lncRNAs play a significant role in various critical biological processes and the development and progression of various human diseases. Constructing lncRNA functional similarity networks could benefit the development of computational models for inferring lncRNA functions and identifying lncRNA-disease associations. However, little effort has been devoted to quantifying lncRNA functional similarity. In this study, we developed an Improved LNCRNA functional SIMilarity calculation model (ILNCSIM) based on the assumption that lncRNAs with similar biological functions tend to be involved in similar diseases. The main improvement comes from the combination of the concept of information content and the hierarchical structure of disease directed acyclic graphs for disease similarity calculation. ILNCSIM was combined with the previously proposed model of Laplacian Regularized Least Squares for lncRNA-Disease Association to further evaluate its performance. As a result, new model obtained reliable performance in the leave-one-out cross validation (AUCs of 0.9316 and 0.9074 based on MNDR and Lnc2cancer databases, respectively), and 5-fold cross validation (AUCs of 0.9221 and 0.9033 for MNDR and Lnc2cancer databases), which significantly improved the prediction performance of previous models. It is anticipated that ILNCSIM could serve as an effective lncRNA function prediction model for future biomedical researches.
Original languageEnglish
Pages (from-to)25902-25914
Number of pages13
JournalOncotarget
Volume7
Issue number18
DOIs
Publication statusPublished - 1 May 2016

Keywords

  • Cancer
  • Directed acyclic graph
  • Disease
  • Functional similarity
  • LncRNAs

ASJC Scopus subject areas

  • Oncology

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