iCircRBP-DHN: identification of circRNA-RBP interaction sites using deep hierarchical network

Yang, Yuning; Hou, Zilong; Ma, Zhiqiang; Li, Xiangtao; Wong, Ka-Chun

doi:10.1093/bib/bbaa274

Cited by 59 publications

(45 citation statements)

References 26 publications

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“…In this part, the prediction performance of CRBPDL, iCircRBP-DHN [ 37 ] and CRIP [ 36 ], PASSION [ 35 ], CSCRSites [ 41 ] and CircSLNN [ 42 ] and five other existing calculation methods are measured by AUC. CSCRSites was based on multiple convolutional thermal coding layers to identify cancer-specific RBP binding sites on circular RNAs.…”

Section: Resultsmentioning

confidence: 99%

“…Zhang advanced a new stacked codon coding scheme and combined it with hybrid deep learning to complete the prediction [ 36 ]. Yang et al constructed a multiscale neural network and predicted the binding site of circRBA-RBP based on contextual sequence information [ 37 ]. However, the feature learning network is relatively simple, and there is still potential for improvement in prediction performance.…”

Section: Introductionmentioning

confidence: 99%

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CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach

2022

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Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs can directly bind to RNA-binding proteins (RBP) and play an important role in a variety of biological activities. The interactions between circRNAs and RBPs are key to comprehending the mechanism of posttranscriptional regulation. Accurately identifying binding sites is very useful for analyzing interactions. In past research, some predictors on the basis of machine learning (ML) have been presented, but prediction accuracy still needs to be ameliorated. Therefore, we present a novel calculation model, CRBPDL, which uses an Adaboost integrated deep hierarchical network to identify the binding sites of circular RNA-RBP. CRBPDL combines five different feature encoding schemes to encode the original RNA sequence, uses deep multiscale residual networks (MSRN) and bidirectional gating recurrent units (BiGRUs) to effectively learn high-level feature representations, it is sufficient to extract local and global context information at the same time. Additionally, a self-attention mechanism is employed to train the robustness of the CRBPDL. Ultimately, the Adaboost algorithm is applied to integrate deep learning (DL) model to improve prediction performance and reliability of the model. To verify the usefulness of CRBPDL, we compared the efficiency with state-of-the-art methods on 37 circular RNA data sets and 31 linear RNA data sets. Moreover, results display that CRBPDL is capable of performing universal, reliable, and robust. The code and data sets are obtainable at https://github.com/nmt315320/CRBPDL.git.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach

2022

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“…In our work, we consider three kinds of features: one-hot encoding, KNFP (Y. Yang et al, 2021 ), and PSNP ( Dou et al, 2020 ).…”

Section: Methodsmentioning

confidence: 99%

“…The KNFP (Y. Yang et al, 2021 ) pattern represents the local contextual features at different levels. KNFP integrates various short-distance sequence order information and retains a large number of original sequence modes ( Chen et al, 2015 ).…”

Section: Methodsmentioning

confidence: 99%

“…Compared with previous machine learning methods, our model applies three encoding methods, one-hot encoding, K-tuple nucleotide frequency pattern (KNFP) (Y. Yang et al, 2021 ), and PSNP ( Dou et al, 2020 ) to extract RNA sequence features. Our model consists of a convolutional neural network (CNN), a capsule neural network, and a bidirectional gated recurrent unit (BiGRU) network with a self-attention mechanism (see Figure 2 ).…”

Section: Introductionmentioning

confidence: 99%

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PseUdeep: RNA Pseudouridine Site Identification with Deep Learning Algorithm

et al. 2021

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Background: Pseudouridine (Ψ) is a common ribonucleotide modification that plays a significant role in many biological processes. The identification of Ψ modification sites is of great significance for disease mechanism and biological processes research in which machine learning algorithms are desirable as the lab exploratory techniques are expensive and time-consuming.Results: In this work, we propose a deep learning framework, called PseUdeep, to identify Ψ sites of three species: H. sapiens, S. cerevisiae, and M. musculus. In this method, three encoding methods are used to extract the features of RNA sequences, that is, one-hot encoding, K-tuple nucleotide frequency pattern, and position-specific nucleotide composition. The three feature matrices are convoluted twice and fed into the capsule neural network and bidirectional gated recurrent unit network with a self-attention mechanism for classification.Conclusion: Compared with other state-of-the-art methods, our model gets the highest accuracy of the prediction on the independent testing data set S-200; the accuracy improves 12.38%, and on the independent testing data set H-200, the accuracy improves 0.68%. Moreover, the dimensions of the features we derive from the RNA sequences are only 109,109, and 119 in H. sapiens, M. musculus, and S. cerevisiae, which is much smaller than those used in the traditional algorithms. On evaluation via tenfold cross-validation and two independent testing data sets, PseUdeep outperforms the best traditional machine learning model available. PseUdeep source code and data sets are available at https://github.com/dan111262/PseUdeep.

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Deciphering 3'UTR Mediated Gene Regulation Using Interpretable Deep Representation Learning

Yang,

Li,

Pang

et al. 2024

Advanced Science

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The 3' untranslated regions (3'UTRs) of messenger RNAs contain many important cis‐regulatory elements that are under functional and evolutionary constraints. It is hypothesized that these constraints are similar to grammars and syntaxes in human languages and can be modeled by advanced natural language techniques such as Transformers, which has been very effective in modeling complex protein sequence and structures. Here 3UTRBERT is described, which implements an attention‐based language model, i.e., Bidirectional Encoder Representations from Transformers (BERT). 3UTRBERT is pre‐trained on aggregated 3'UTR sequences of human mRNAs in a task‐agnostic manner; the pre‐trained model is then fine‐tuned for specific downstream tasks such as identifying RBP binding sites, m6A RNA modification sites, and predicting RNA sub‐cellular localizations. Benchmark results show that 3UTRBERT generally outperformed other contemporary methods in each of these tasks. More importantly, the self‐attention mechanism within 3UTRBERT allows direct visualization of the semantic relationship between sequence elements and effectively identifies regions with important regulatory potential. It is expected that 3UTRBERT model can serve as the foundational tool to analyze various sequence labeling tasks within the 3'UTR fields, thus enhancing the decipherability of post‐transcriptional regulatory mechanisms.

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iCircRBP-DHN: identification of circRNA-RBP interaction sites using deep hierarchical network

Cited by 59 publications

References 26 publications

CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach

CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach

PseUdeep: RNA Pseudouridine Site Identification with Deep Learning Algorithm

Deciphering 3'UTR Mediated Gene Regulation Using Interpretable Deep Representation Learning

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