DeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites

Abstract

Background Addressing the laborious nature of traditional biological experiments by using an efficient computational approach to analyze RNA-binding proteins (RBPs) binding sites has always been a challenging task. RBPs play a vital role in post-transcriptional control. Identification of RBPs binding sites is a key step for the anatomy of the essential mechanism of gene regulation by controlling splicing, stability, localization and translation. Traditional methods for detecting RBPs binding sites are time-consuming and computationally-intensive. Recently, the computational method has been incorporated in researches of RBPs. Nevertheless, lots of them not only rely on the sequence data of RNA but also need additional data, for example the secondary structural data of RNA, to improve the performance of prediction, which needs the pre-work to prepare the learnable representation of structural data. Results To reduce the dependency of those pre-work, in this paper, we introduce DeepPN, a deep parallel neural network that is constructed with a convolutional neural network (CNN) and graph convolutional network (GCN) for detecting RBPs binding sites. It includes a two-layer CNN and GCN in parallel to extract the hidden features, followed by a fully connected layer to make the prediction. DeepPN discriminates the RBP binding sites on learnable representation of RNA sequences, which only uses the sequence data without using other data, for example the secondary or tertiary structure data of RNA. DeepPN is evaluated on 24 datasets of RBPs binding sites with other state-of-the-art methods. The results show that the performance of DeepPN is comparable to the published methods. Conclusion The experimental results show that DeepPN can effectively capture potential hidden features in RBPs and use these features for effective prediction of binding sites.

Advances in Machine Learning for Sensing and Condition Monitoring

“…CNN and GCN have been applied by Zhang et al [90] to extract discriminative features from RNA sequences. They developed a method based on two-layer CNN and GCN in parallel to extract the hidden features, followed by a fully connected layer to make the prediction of RNA-binding proteins for the anatomy of the essential mechanism of gene regulation.…”

Role of Optimization in RNA–Protein-Binding Prediction

“…Various experimental techniques are available for the detection of RBP sites, such as crosslinking immunoprecipitation (HITS-CLIP), light-activated-ribonucleotide-enhanced crosslinking and immunoprecipitation (PAR-CLIP), and individual-nucleotide resolution crosslinking and immunoprecipitation (iCLIP) [ 6 ].…”

“…This is due to its capacity to uncover hidden patterns in complex biological data [ 13 ]. In particular, convolutional neural networks (CNNs) have demonstrated promising results for bioinformatics prediction tasks, including peptides [ 14 ], splice sites [ 15 ], and RNA–protein binding sites [ 6 ]. CNNs have been the primary mechanism for extracting RBP information in deep-learning-based approaches [ 6 ].…”

Study on Gene Splicing Site Recognition Based on Particle Swarm Optimization Twin Support Vector Machine Algorithm for Smart Healthcare

“…Spliceator used a convolutional neural network and is trained on carefully validated data from over 100 organisms. Zhang et al [15] introduced the DeepPN, a deep parallel neural network that is constructed with a convolutional neural network (CNN) and graph convolutional network (GCN) for detecting RBPs binding sites. Ghazanfari et al [16] used all the data and valuable information such as isoform sequences, expression profiles, and gene ontology graphs and proposes a comprehensive model based on deep neural networks.…”