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Updated: Jul 27, 2025

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

ASCRB: Multi-view based attentional feature selection for CircRNA-binding site prediction.

Lei Li¹, Zhigang Xue², Xiuquan Du³

¹Department of Neurology, Shuyang Hospital Affiliated to Yangzhou University School of Medicine (Shuyang Hospital of Traditional Chinese Medicine, Suqian, China.

Computers in Biology and Medicine

|June 8, 2023

Summary

This summary is machine-generated.

This study introduces a novel channel attention mechanism to accurately identify circular RNA binding sites by filtering redundant multi-view features. The method significantly improves detection performance, offering a new tool for gene regulation research.

Keywords:

CircRNA binding site Feature selection Multi-view feature

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Last Updated: Jul 27, 2025

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

In Silico Identification and Characterization of circRNAs During Host-Pathogen Interactions

Published on: October 21, 2022

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Bioinformatics
Molecular Biology
Genomics

Background:

Circular RNAs (CircRNAs) are crucial non-coding RNAs involved in gene regulation via interactions with RNA-binding proteins (RBPs).
Accurate identification of CircRNA-RBP binding sites is essential for understanding gene regulatory mechanisms.
Existing multi-view methods often suffer from redundant information, hindering accurate binding site detection.

Purpose of the Study:

To develop an effective method for identifying CircRNA-RBP binding sites by addressing the redundancy issue in multi-view features.
To leverage channel attention mechanisms for filtering irrelevant information and enhancing feature representation.

Main Methods:

Constructed multi-view features using five distinct encoding schemes.
Employed a channel attention mechanism to calibrate features by generating global representations and filtering redundant information.
Fused multi-view features for the final detection of CircRNA binding sites.

Main Results:

The proposed method achieved an average AUC of 93.85% across 37 CircRNA-RBP datasets.
Demonstrated superior performance compared to existing state-of-the-art methods.
Source code is publicly available at https://github.com/dxqllp/ASCRB.

Conclusions:

The channel attention-based approach effectively filters redundant information, leading to improved CircRNA binding site identification.
This method offers a significant advancement in the field of CircRNA research and gene regulation analysis.