Abstract
Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs can directly bind to RNA-binding proteins (RBP) and play an important role in a variety of biological activities. The interactions between circRNAs and RBPs are key to comprehending the mechanism of posttranscriptional regulation. Accurately identifying binding sites is very useful for analyzing interactions. In past research, some predictors on the basis of machine learning (ML) have been presented, but prediction accuracy still needs to be ameliorated. Therefore, we present a novel calculation model, CRBPDL, which uses an Adaboost integrated deep hierarchical network to identify the binding sites of circular RNA-RBP. CRBPDL combines five different feature encoding schemes to encode the original RNA sequence, uses deep multiscale residual networks (MSRN) and bidirectional gating recurrent units (BiGRUs) to effectively learn high-level feature representations, it is sufficient to extract local and global context information at the same time. Additionally, a self-attention mechanism is employed to train the robustness of the CRBPDL. Ultimately, the Adaboost algorithm is applied to integrate deep learning (DL) model to improve prediction performance and reliability of the model. To verify the usefulness of CRBPDL, we compared the efficiency with state-of-the-art methods on 37 circular RNA data sets and 31 linear RNA data sets. Moreover, results display that CRBPDL is capable of performing universal, reliable, and robust. The code and data sets are obtainable at https://github.com/nmt315320/CRBPDL.git.
Highlights
Circular RNA is a special circular endogenous noncoding RNA produced by selective shearing [1,2]
In order to identify the interaction of circRNA with 37 different types of circRNA binding proteins, we developed an integrated deep learning network based on hierarchical network, called CRBPDL
We find that the value of our proposed integrated deep network model CRBPDL on ACC, SE, SP and Matthew’s correlation coefficient (MCC) is significantly higher than the experimental results of random forest algorithm (RF), support vector machine (SVM) and logistics, and the average values of SE, SP, MCC, ACC are 0.8548, 0.7796, 0.6897, and 0.8739
Summary
Circular RNA (circRNA) is a special circular endogenous noncoding RNA produced by selective shearing [1,2]. As a protein that binds to double-stranded or single-stranded RNA, RBPs are present throughout the life of RNA and mediate the maturation [11], transport [12], positioning and translation of RNA [13]. RBPs affect the entire process of the circRNA life cycle, and some RBPs are involved in the generation of circRNAs, such as Quking (QKI), FUS, and HNRNPL. They are involved in almost every aspect of the cyclic RNA life cycle, including generation [14], posttranscriptional regulation [15], and functional execution [16]. Predicting the binding site of RNA and RBP can provide insight into the mechanisms underlying diseases involving RBPs and help to further explore the role of circRNA in disease pathophysiology
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