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lncRNA - Long Non-coding RNAs02:39

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ILNCSIM: improved lncRNA functional similarity calculation model.

Yu-An Huang1, Xing Chen2,3, Zhu-Hong You4

  • 1College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.

Oncotarget
|March 31, 2016
PubMed
Summary
This summary is machine-generated.

We developed an Improved Long non-coding RNA (lncRNA) functional SIMilarity (ILNCSIM) model to quantify lncRNA similarity. This novel approach enhances the prediction of lncRNA functions and their associations with diseases.

Keywords:
cancerdirected acyclic graphdiseasefunctional similaritylncRNAs

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Long non-coding RNAs (lncRNAs) are increasingly recognized for their roles in biological processes and human diseases.
  • Developing computational models for lncRNA function inference and lncRNA-disease association identification is crucial.
  • Quantifying lncRNA functional similarity remains a challenge in the field.

Purpose of the Study:

  • To develop an Improved Long non-coding RNA functional SIMilarity (ILNCSIM) calculation model.
  • To enhance the accuracy of inferring lncRNA functions and identifying lncRNA-disease associations.
  • To leverage disease similarity metrics for improved lncRNA functional similarity assessment.

Main Methods:

  • ILNCSIM model development based on the principle that functionally similar lncRNAs are involved in similar diseases.
  • Integration of information content and hierarchical disease structure (directed acyclic graphs) for disease similarity calculation.
  • Combination of ILNCSIM with Laplacian Regularized Least Squares for lncRNA-Disease Association prediction.

Main Results:

  • ILNCSIM demonstrated reliable performance in cross-validation studies.
  • Achieved high AUC values (0.9316 and 0.9074 in leave-one-out; 0.9221 and 0.9033 in 5-fold) using MNDR and Lnc2cancer databases.
  • Significantly improved prediction performance compared to previous computational models.

Conclusions:

  • The ILNCSIM model provides an effective method for quantifying lncRNA functional similarity.
  • This model can serve as a valuable tool for predicting lncRNA functions.
  • ILNCSIM is anticipated to advance biomedical research in lncRNA-disease associations.