Abstract
BackgroundMany evidences have demonstrated that circRNAs (circular RNA) play important roles in controlling gene expression of human, mouse and nematode. More importantly, circRNAs are also involved in many diseases through fine tuning of post-transcriptional gene expression by sequestering the miRNAs which associate with diseases. Therefore, identifying the circRNA-disease associations is very appealing to comprehensively understand the mechanism, treatment and diagnose of diseases, yet challenging. As the complex mechanism between circRNAs and diseases, wet-lab experiments are expensive and time-consuming to discover novel circRNA-disease associations. Therefore, it is of dire need to employ the computational methods to discover novel circRNA-disease associations.ResultIn this study, we develop a method (DWNN-RLS) to predict circRNA-disease associations based on Regularized Least Squares of Kronecker product kernel. The similarity of circRNAs is computed from the Gaussian Interaction Profile(GIP) based on known circRNA-disease associations. In addition, the similarity of diseases is integrated by the mean of GIP similarity and sematic similarity which is computed by the direct acyclic graph (DAG) representation of diseases. The kernels of circRNA-disease pairs are constructed from the Kronecker product of the kernels of circRNAs and diseases. DWNN (decreasing weight k-nearest neighbor) method is adopted to calculate the initial relational score for new circRNAs and diseases. The Kronecker product kernel based regularised least squares approach is used to predict new circRNA-disease associations. We adopt 5-fold cross validation (5CV), 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) to assess the prediction performance of our method, and compare it with other six competing methods (RLS-avg, RLS-Kron, NetLapRLS, KATZ, NBI, WP).ConlusionThe experiment results show that DWNN-RLS reaches the AUC values of 0.8854, 0.9205 and 0.9701 in 5CV, 10CV and LOOCV, respectively, which illustrates that DWNN-RLS is superior to the competing methods RLS-avg, RLS-Kron, NetLapRLS, KATZ, NBI, WP. In addition, case studies also show that DWNN-RLS is an effective method to predict new circRNA-disease associations.
Highlights
Many evidences have demonstrated that circRNAs play important roles in controlling gene expression of human, mouse and nematode
decreasing weight k-nearest neighbor (DWNN)-RLS computes the Gaussian interaction profile (GIP) kernel similarities of circRNAs and diseases based on the known circRNA-disease associations
Performance evaluation In this study, we conduct 5-fold cross validation (5CV), 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) to evaluate the performance of DWNN-RLS for predicting new circRNA-disease associations
Summary
Many evidences have demonstrated that circRNAs (circular RNA) play important roles in controlling gene expression of human, mouse and nematode. CircRNAs are involved in many diseases through fine tuning of post-transcriptional gene expression by sequestering the miRNAs which associate with diseases. As forming a circRNA is usually considered a rare event in cells, it was suggested that they may be considered errors of normal splicing process [4, 7]. Despite their existence in both unicellular and multicellular organisms, they have been previously even disregarded as transcriptional noise or artifacts [8]. CircRNAs are expected to have other functions independent of their host genes because they have much longer half-life than other linear RNA transcripts [10]. Unlike the above functions of circRNAs are based on the fact that they are located to the cytoplasm, some circRNAs such as exon-intron circRNAs are retained in the nucleus and they may promote with transcription [17]
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