ArticleMolecules (Basel, Switzerland)2019
Identifying Cancer-Specific circRNA-RBP Binding Sites Based on Deep Learning.
Article in Molecules (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed.
- The circular RNA landscape: Biogenesis, functions, identification pipelines, and biomedical applications.Non-coding RNA research · 2026Review
- Review
- CircRNAs: functions and emerging roles in cancer and immunotherapy.BMC medicine · 2025Review
- Expanded insights into the mechanisms of RNA-binding protein regulation of circRNA generation and function in cancer biology and therapy.Genes & diseases · 2025Review
- CR-deal: Explainable Neural Network for circRNA-RBP Binding Site Recognition and Interpretation.Interdisciplinary sciences, computational life sciences · 2025Article
- RNA-binding proteins and autophagy in lung cancer: mechanistic insights and therapeutic perspectives.Discover oncology · 2025Review
- EnrichRBP: an automated and interpretable computational platform for predicting and analysing RNA-binding protein events.Bioinformatics (Oxford, England) · 2024Article
- CRBPSA: CircRNA-RBP interaction sites identification using sequence structural attention model.BMC biology · 2024Article
- Predicting circRNA-RBP Binding Sites Using a Hybrid Deep Neural Network.Interdisciplinary sciences, computational life sciences · 2024Article
- iCRBP-LKHA: Large convolutional kernel and hybrid channel-spatial attention for identifying circRNA-RBP interaction sites.PLoS computational biology · 2024Article
- CircNetVis: an interactive web application for visualizing interaction networks of circular RNAs.BMC bioinformatics · 2024Article
- CircSI-SSL: circRNA-binding site identification based on self-supervised learning.Bioinformatics (Oxford, England) · 2024Article
- CircTADA2A up-regulates MAPK8 by targeting MiR-214-3p and recruiting EIF4A3 to promote the invasion and migration of non-small cell lung cancer cells.Histology and histopathology · 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
- Involvement of circRNAs in the Development of Heart Failure.International journal of molecular sciences · 2022Review
- Circular RNA circ_0006089 regulates the IGF1R expression by targeting miR-143-3p to promote gastric cancer proliferation, migration and invasion.Cell cycle (Georgetown, Tex.) · 2022Article
- Circular RNAs in hepatocellular carcinoma: Recent advances.World journal of gastrointestinal oncology · 2022Review
- Circular RNA: A novel and potential regulator in pathophysiology of schizophrenia.Metabolic brain disease · 2022Review
- Genomics enters the deep learning era.PeerJ · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Circular RNAs (circRNAs) are extensively expressed in cells and tissues, and play crucial roles in human diseases and biological processes. Recent studies have reported that circRNAs could function as RNA binding protein (RBP) sponges, meanwhile RBPs can also be involved in back-splicing. The interaction with RBPs is also considered an important factor for investigating the function of circRNAs. Hence, it is necessary to understand the interaction mechanisms of circRNAs and RBPs, especially in human cancers. Here, we present a novel method based on deep learning to identify cancer-specific circRNA-RBP binding sites (CSCRSites), only using the nucleotide sequences as the input. In CSCRSites, an architecture with multiple convolution layers is utilized to detect the features of the raw circRNA sequence fragments, and further identify the binding sites through a fully connected layer with the softmax output. The experimental results show that CSCRSites outperform the conventional machine learning classifiers and some representative deep learning methods on the benchmark data. In addition, the features learnt by CSCRSites are converted to sequence motifs, some of which can match to human known RNA motifs involved in human diseases, especially cancer. Therefore, as a deep learning-based tool, CSCRSites could significantly contribute to the function analysis of cancer-associated circRNAs.
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.