Predicting RBP Binding Sites of RNA With High-Order Encoding Features and CNN-BLSTM Hybrid Model

RNA binding protein (RBP) is extensively involved in various cellular regulatory processes through the interaction with RNAs. Capturing the RBP binding preferences is fundamental for revealing the pathogenesis of complex diseases. Many experimental detection techniques are still time-consuming and l...

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Bibliographic Details
Published in:IEEE/ACM transactions on computational biology and bioinformatics 2022-07, Vol.19 (4), p.2409-2419
Main Authors: Wang, Zhaowei, Dai, Qiguo, Song, Jinmiao, Duan, Xiaodong, Yang, Hongpeng, Yang, Ziqi
Format: Article
Language:English
Subjects:
Artificial neural networks
bidirectional long short-term memory network
Binding sites
Biological system modeling
Computational modeling
Computer applications
Convolution neural networks
Deep learning
Encoding
Feature extraction
high-order encoding
Long short-term memory
Neural networks
Nucleotide sequence
Nucleotides
Pathogenesis
Protein structure
Proteins
Ribonucleic acid
RNA
RNA binding protein
Secondary structure
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Summary:RNA binding protein (RBP) is extensively involved in various cellular regulatory processes through the interaction with RNAs. Capturing the RBP binding preferences is fundamental for revealing the pathogenesis of complex diseases. Many experimental detection techniques are still time-consuming and labor-intensive, therefore, it is indispensable to develop a computational method with convincing accuracy. In this study, we proposed a CNN-BLSTM hybrid deep learning framework, named DeepDW, for predicting the RBP binding sites on RNAs with high-order encoding features of RNA sequence and secondary structure. The high-order encoding strategy was used to characterize the dependencies among adjacency nucleotides. For CNN-BLSTM hybrid model, DeepDW first employed two 1-D convolutional neural networks (CNNs) for learning the local features from high-order encoded matrices of RNA sequence and structure separately, and then applied two bidirectional long short-term memory networks (BLSTMs) to capture the global information in a higher level. Moreover, a series of experiments were carried out on 31 public datasets to evaluate our proposed framework, and DeepDW achieved superior performance than the state-of-the-art methods. The results indicated that the combination of high-order encoding method and CNN-BLSTM hybrid model had advantages in identifying RBP-RNA binding sites.
ISSN:1545-5963
1557-9964
DOI:10.1109/TCBB.2021.3083930