iCRBP-LKHA: Large convolutional kernel and hybrid channel-spatial attention for identifying circRNA-RBP interaction sites

• DOI: 10.1371/journal.pcbi.1012399
• Publication Date: 2024-08-22T19:49:18Z
• Authors: Lin Yuan; Ling Zhao; Jinling Lai; Yufeng Jiang; Qinhu Zhang; Zhen Shen; Chun-Hou Zheng; De-Shuang Huang
• Keywords: Kernel (algebra); Identification

Circular RNAs (circRNAs) play vital roles in transcription and translation. Identification of circRNA-RBP (RNA-binding protein) interaction sites has become a fundamental step molecular cell biology. Deep learning (DL)-based methods have been proposed to predict achieved impressive identification performance. However, those cannot effectively capture long-distance dependencies, utilize the information multiple features. To overcome limitations, we propose DL-based model iCRBP-LKHA using deep hybrid networks for identifying sites. adopts five encoding schemes. Meanwhile, neural network architecture, which consists large kernel convolutional (LKCNN), block attention module with one-dimensional convolution (CBAM-1D) bidirectional gating recurrent unit (BiGRU), can explore local information, global context features automatically. verify effectiveness iCRBP-LKHA, compared its performance shallow algorithms on 37 circRNAs datasets stringent datasets. And state-of-the-art datasets, 31 linear The experimental results not only show that outperforms other competing methods, but also demonstrate potential this RNA-RBP