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CRBP-HFEF: Prediction of RBP-Binding Sites on circRNAs Based on Hierarchical Feature Expansion and Fusion

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Interdisciplinary Sciences: Computational Life Sciences Aims and scope Submit manuscript

Abstract

Circular RNAs (circRNAs) participate in the regulation of biological processes by binding to specific proteins and thus influence transcriptional processes. In recent years, circRNAs have become an emerging hotspot in RNA research. Due to powerful learning ability, the various deep learning frameworks have been used to predict the binding sites of RNA-binding protein (RPB) on circRNAs. These methods usually perform only single-level feature extraction of sequence information. However, the feature acquisition may be inadequate for single-level extraction. Generally, the features of deep and shallow layers of neural network can complement each other and are both important for binding site prediction tasks. Based on this concept, we propose a method that combines deep and shallow features, namely CRBP-HFEF. Specifically, features are first extracted and expanded for different levels of network. Then, the expanded deep and shallow features are fused and fed into the classification network, which finally determines whether they are binding sites. Compared to several existing methods, the experimental results on multiple datasets show that the proposed method achieves significant improvements in a number of metrics (with an average AUC of 0.9855). Moreover, much sufficient ablation experiments are also performed to verify the effectiveness of the hierarchical feature expansion strategy.

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Data availability

We used freely available data as described in Methods. The data are available at [https://github.com/wzf171/CRPBsites] and [https://github.com/kavin525zhang/CRIP].

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Funding

This work was supported by a National Natural Science Foundation of China (No. 61972002).

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Authors and Affiliations

  1. Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, and School of Electrical Engineering and Automation Anhui University, Hefei, 230601, Anhui, China

    Zheng Ma & Zhan-Li Sun

  2. School of Computer Science and Technology, Anhui University, Hefei, 230601, Anhui, China

    Mengya Liu

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Correspondence to Zhan-Li Sun.

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Ma, Z., Sun, ZL. & Liu, M. CRBP-HFEF: Prediction of RBP-Binding Sites on circRNAs Based on Hierarchical Feature Expansion and Fusion. Interdiscip Sci Comput Life Sci 15, 465–479 (2023). https://doi.org/10.1007/s12539-023-00572-0

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