Interdisciplinary Sciences: Computational Life Sciences ( IF 3.9 ) Pub Date : 2023-05-26 , DOI: 10.1007/s12539-023-00572-0
Zheng Ma 1 , Zhan-Li Sun 1 , Mengya Liu 2
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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.
Graphical Abstract
中文翻译：
CRBP-HFEF：基于分层特征扩展和融合预测 circRNA 上的 RBP 结合位点
摘要
环状RNA（circRNA）通过与特定蛋白质结合来参与生物过程的调节，从而影响转录过程。近年来，circRNA已成为RNA研究的新兴热点。由于强大的学习能力，各种深度学习框架已被用来预测RNA结合蛋白（RPB）在circRNA上的结合位点。这些方法通常仅执行序列信息的单级特征提取。然而，特征获取可能不足以进行单级提取。一般来说，神经网络深层和浅层的特征可以互补，对于结合位点预测任务都很重要。基于这个概念，我们提出了一种结合深层和浅层特征的方法，即CRBP-HFEF。具体来说，首先针对不同级别的网络提取和扩展特征。然后，扩展的深层和浅层特征被融合并输入分类网络，最终确定它们是否是结合位点。与现有的几种方法相比，多个数据集上的实验结果表明，该方法在多项指标上取得了显着的改进（平均 AUC 为 0.9855）。此外，还进行了足够多的消融实验来验证分层特征扩展策略的有效性。
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