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

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dc.citation.issue1
dc.citation.volume23
dc.contributor.authorZhang J
dc.contributor.authorLiu B
dc.contributor.authorWang Z
dc.contributor.authorLehnert K
dc.contributor.authorGahegan M
dc.coverage.spatialEngland
dc.date.available2022-06-29
dc.date.available2022-06-10
dc.date.issued29/06/2022
dc.description(c) The Author/s
dc.description.abstractBACKGROUND: 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.
dc.description.publication-statusPublished online
dc.format.extent257 - ?
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/35768792
dc.identifier10.1186/s12859-022-04798-5
dc.identifier.citationBMC Bioinformatics, 2022, 23 (1), pp. 257 - ?
dc.identifier.doi10.1186/s12859-022-04798-5
dc.identifier.eissn1471-2105
dc.identifier.elements-id454498
dc.identifier.harvestedMassey_Dark
dc.identifier.urihttps://hdl.handle.net/10179/17777
dc.languageeng
dc.relation.isPartOfBMC Bioinformatics
dc.subjectBioinformatics
dc.subjectConvolutional neural network
dc.subjectGraph convolution network
dc.subjectRNA-binding protein
dc.subjectBinding Sites
dc.subjectNeural Networks, Computer
dc.subjectProtein Binding
dc.subjectRNA
dc.subjectRNA-Binding Proteins
dc.subject.anzsrc01 Mathematical Sciences
dc.subject.anzsrc06 Biological Sciences
dc.subject.anzsrc08 Information and Computing Sciences
dc.titleDeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites.
dc.typeJournal article
pubs.declined2022-10-12T15:39:52.512+1300
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Mathematical and Computational Sciences
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