Abstract
BackgroundIn recent years, increasing evidences have indicated that long non-coding RNAs (lncRNAs) are deeply involved in a wide range of human biological pathways. The mutations and disorders of lncRNAs are closely associated with many human diseases. Therefore, it is of great importance to predict potential associations between lncRNAs and complex diseases for the diagnosis and cure of complex diseases. However, the functional mechanisms of the majority of lncRNAs are still remain unclear. As a result, it remains a great challenge to predict potential associations between lncRNAs and diseases.ResultsHere, we proposed a new method to predict potential lncRNA-disease associations. First, we constructed a bipartite network based on known associations between diseases and lncRNAs/protein coding genes. Then the cluster association scores were calculated to evaluate the strength of the inner relationships between disease clusters and gene clusters. Finally, the gene-disease association scores are defined based on disease-gene cluster association scores and used to measure the strength for potential gene-disease associations.ConclusionsLeave-One Out Cross Validation (LOOCV) and 5-fold cross validation tests were implemented to evaluate the performance of our method. As a result, our method achieved reliable performance in the LOOCV (AUCs of 0.8169 and 0.8410 based on Yang’s dataset and Lnc2cancer 2.0 database, respectively), and 5-fold cross validation (AUCs of 0.7573 and 0.8198 based on Yang’s dataset and Lnc2cancer 2.0 database, respectively), which were significantly higher than the other three comparative methods. Furthermore, our method is simple and efficient. Only the known gene-disease associations are exploited in a graph manner and further new gene-disease associations can be easily incorporated in our model. The results for melanoma and ovarian cancer have been verified by other researches. The case studies indicated that our method can provide informative clues for further investigation.
Highlights
In recent years, increasing evidences have indicated that long noncoding RNAs are deeply involved in a wide range of human biological pathways
Prediction of long noncoding RNAs (lncRNAs) associated with diseases For the gene-disease pairs without edges in the bipartite network, our method can calculate an association score for a pair which can be used to measure the potential association strength of this gene-disease pair
Performance evaluation Leave-One Out Cross Validation (LOOCV) and 5-fold cross validation were applied to evaluate the prediction performance of our method based on known lncRNA-disease associations from the dataset of Yang [27] and Lnc2Cancer 2.0 database [30]
Summary
In recent years, increasing evidences have indicated that long noncoding RNAs (lncRNAs) are deeply involved in a wide range of human biological pathways. It is of great importance to predict potential associations between lncRNAs and complex diseases for the diagnosis and cure of complex diseases. It remains a great challenge to predict potential associations between lncRNAs and diseases. LncRNAs are the biggest part of non-coding RNAs which are longer than 200 nucleotides and are not translated into proteins [3, 4]. It is estimated that about 62% of the human genome is transcribed to produce long non-coding RNAs. Compared with protein-coding transcripts, lncRNAs have fewer exons and are expressed at lower levels [5, 6]. LncRNAs show extensive mechanisms to play their biological roles compared to small ncRNAs [7]. As shown by more and more studies that lncRNAs play crucial functional roles in cytoplasm and nucleus through cis or trans-regulatory mechanisms [6], and play important roles in different cellular pathways [8, 9]
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