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Author Spotlight: A Computational Pipeline for Analyzing Chimeric Noncoding RNA-Target RNA Interactions in High-Throughput Sequencing Data
Published on: December 1, 2023
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FMLNCSIM: fuzzy measure-based lncRNA functional similarity calculation model.
Xing Chen1, Yu-An Huang2, Xue-Song Wang1
1School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China.
Oncotarget
|June 21, 2016
Summary
We developed a novel Fuzzy Measure-based lncRNA functional SIMilarity (FMLNCSIM) model to calculate long non-coding RNA functional similarity. This model enhances lncRNA-disease association prediction, improving upon existing methods.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Long non-coding RNAs (lncRNAs) play crucial roles in biological processes and diseases.
- Accurate calculation of lncRNA functional similarity is vital for understanding lncRNA functions and identifying disease associations.
- Current methods for large-scale lncRNA functional similarity calculation are limited.
Purpose of the Study:
- To develop a novel model, FMLNCSIM, for calculating lncRNA functional similarity on a large scale.
- To infer lncRNA functions and identify potential lncRNA-disease associations.
- To improve the accuracy of lncRNA-disease association prediction.
Main Methods:
- Developed the Fuzzy Measure-based LNCRNA functional SIMilarity (FMLNCSIM) model.
- Integrated information content with fuzzy measure theory applied to disease MeSH descriptor directed acyclic graphs.
- Combined FMLNCSIM with the Laplacian Regularized Least Squares for lncRNA-Disease Association (LRLSLDA) model.
Main Results:
- The integrated LRLSLDA-FMLNCSIM model demonstrated strong performance in predicting lncRNA-disease associations.
- Achieved high AUC values (0.8266 and 0.9338 in global LOOCV; 0.7979 and 0.9237 in 5-fold cross-validation) using LncRNADisease and MNDR databases.
- Significantly outperformed previous classical models.
Conclusions:
- FMLNCSIM provides an effective approach for calculating lncRNA functional similarity.
- The integrated model shows significant improvements in lncRNA-disease association prediction.
- FMLNCSIM holds potential for future research in identifying functionally similar lncRNAs and inferring lncRNA functions.
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