Article

CircSSNN: circRNA-binding site prediction via sequence self-attention neural networks with pre-normalization

2023-02-07

Abstract excerpt

Circular RNAs (circRNAs) play a significant role in some diseases by acting as transcription templates. Therefore, analyzing the interaction mechanism between circRNA and RNA-binding proteins (RBPs) has far-reaching implications for the prevention and treatment of diseases. Existing models for circRNA-RBP identification most adopt CNN, RNN, or their variants as feature extractors. Most of them have drawbacks such...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 4b7b4ac9-ed4e-56e3-9450-a91401b86725
DOI: 10.1101/2023.02.07.527436

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

CircSSNN: circRNA-binding site prediction via sequence self-attention neural networks with pre-normalizationDOI 10.1101/2023.02.07.527436Read

Predicting circRNA-RBP interaction sites using a codon-based encoding and hybrid deep neural networksSemantic proximity Read

DeepciRGO: functional prediction of circular RNAs through hierarchical deep neural networks using heterogeneous network featuresSemantic proximity Read

JEDI: Circular RNA Prediction based on Junction Encoders and Deep Interaction among Splice SitesSemantic proximity Read

Inferring Potential CircRNA-Disease Associations via Deep Autoencoder-Based Classification.Semantic proximity Read

Predicting dynamic cellular protein-RNA interactions using deep learning and <i>in vivo</i> RNA structureSemantic proximity Read

DeepIRES: a hybrid deep learning model for accurate identification of internal ribosome entry sites in cellular and viral mRNAsSemantic proximity Read

CircWalk: A novel approach to predict CircRNA- Disease association based on heterogeneous network representation learningSemantic proximity Read

An ensemble approach for CircRNA-disease association prediction based on autoencoder and deep neural network.Semantic proximity Read

Deep learning predicts non-coding RNA functions from only raw sequence dataSemantic proximity Read

SDNN-PPI: Self-attention with deep neural networks effect on protein-protein interaction predictionSemantic proximity Read

MSGTrans: circRNA-Disease Association Prediction With Cluster-Based Negative Sampling and Multi-Scale Graph Transformer.Semantic proximity Read

HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequenceSemantic proximity Read