A Computational Framework to Infer Human Disease-Associated Long Noncoding RNAs

As a major class of noncoding RNAs, long noncoding RNAs (lncRNAs) have been implicated in various critical biological processes. Accumulating researches have linked dysregulations and mutations of lncRNAs to a variety of human disorders and diseases. However, to date, only a few human lncRNAs have been associated with diseases. Therefore, it is very important to develop a computational method to globally predict potential associated diseases for human lncRNAs.

Researchers at the Chinese Academy of Sciences have developed a computational framework to accomplish this by combining human lncRNA expression profiles, gene expression profiles, and human disease-associated gene data. Applying this framework to available human long intergenic noncoding RNAs (lincRNAs) expression data, they show that the framework has reliable accuracy. As a result, for non-tissue-specific lincRNAs, the AUC of this algorithm is 0.7645, and the prediction accuracy is about 89%. This study will be helpful for identifying novel lncRNAs for human diseases, which will help in understanding the roles of lncRNAs in human diseases and facilitate treatment.


Availability – The corresponding codes for our method and the predicted results are all available at http://asdcd.amss.ac.cn/MingXiLiu/lncRNA​-disease.html.

  • Liu M-X, Chen X, Chen G, Cui Q-H, Yan G-Y (2014) A Computational Framework to Infer Human Disease-Associated Long Noncoding RNAs. PLoS ONE 9(1), e84408. [article]

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