ArticlePLoS computational biology2022
CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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The trial behind it
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Who cites it
21 citing papers in PubMed, 52 citations in OpenAlex.
- AGCECDA: attention-guided heterogeneous graph collaborative embedding for circRNA-drug sensitivity association prediction.BMC biology · 2026Article
- Interpretable prediction and generation of ASC-speck aptamers using multiscale deep biological learning models.Bioinformatics advances · 2026Article
- CR-deal: Explainable Neural Network for circRNA-RBP Binding Site Recognition and Interpretation.Interdisciplinary sciences, computational life sciences · 2025Article
- DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions.BMC biology · 2025Article
- Deep profiling of gene expression across 18 human cancers.Nature biomedical engineering · 2025Article
- An Integrated TCN-CrossMHA Model for Predicting circRNA-RBP Binding Sites.Interdisciplinary sciences, computational life sciences · 2025Article
- CRBPSA: CircRNA-RBP interaction sites identification using sequence structural attention model.BMC biology · 2024Article
- A deep profile of gene expression across 18 human cancers.bioRxiv : the preprint server for biology · 2024Article
- iCRBP-LKHA: Large convolutional kernel and hybrid channel-spatial attention for identifying circRNA-RBP interaction sites.PLoS computational biology · 2024Article
- Extracellular vesicle-delivered hsa_circ_0090081 regulated by EIF4A3 enhances gastric cancer tumorigenesis.Cell division · 2024Article
- CircSI-SSL: circRNA-binding site identification based on self-supervised learning.Bioinformatics (Oxford, England) · 2024Article
- EMDL_m6Am: identifying N6,2'-O-dimethyladenosine sites based on stacking ensemble deep learning.BMC bioinformatics · 2023Article
- iCircDA-NEAE: Accelerated attribute network embedding and dynamic convolutional autoencoder for circRNA-disease associations prediction.PLoS computational biology · 2023Article
- Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review.Biology · 2023Review
- Functions of Circular RNA in Human Diseases and Illnesses.Non-coding RNA · 2023Review
- Circ_C4orf36 Promotes the Proliferation and Osteogenic Differentiation of BMSCs by Regulating VEGFA.Biochemical genetics · 2023Article
- CircSSNN: circRNA-binding site prediction via sequence self-attention neural networks with pre-normalization.BMC bioinformatics · 2023Article
- Nucleotide-level prediction of CircRNA-protein binding based on fully convolutional neural network.Frontiers in genetics · 2023Article
- PseU-ST: A new stacked ensemble-learning method for identifying RNA pseudouridine sites.Frontiers in genetics · 2023Article
- New insights on circular RNAs and their potential applications as biomarkers, therapeutic agents, and preventive vaccines in viral infections: with a glance at SARS-CoV-2.Molecular therapy. Nucleic acids · 2022Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.