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Updated: Jun 8, 2025

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

CellCircLoc: Deep Neural Network for Predicting and Explaining Cell Line-Specific CircRNA Subcellular Localization.

Min Zeng, Jingwei Lu, Yiming Li

IEEE Journal of Biomedical and Health Informatics

|November 4, 2024

Summary

This summary is machine-generated.

Predicting circular RNA (circRNA) location needs cell-specific models. CellCircLoc, a new deep learning tool, accurately forecasts circRNA subcellular localization by training models for individual cell lines.

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Last Updated: Jun 8, 2025

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

Identification of Circular RNAs using RNA Sequencing

Identification of Circular RNAs using RNA Sequencing

Published on: November 14, 2019

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

Area of Science:

Molecular Biology
Bioinformatics
Genomics

Background:

Subcellular localization of circular RNAs (circRNAs) is key to their function.
CircRNA localization varies significantly across different cell lines.
Current prediction methods often overlook cell line specificity, limiting accuracy.

Purpose of the Study:

To develop a cell line-specific computational model for predicting circRNA subcellular localization.
To improve the accuracy of circRNA localization prediction by accounting for cellular context.

Main Methods:

Proposed CellCircLoc, a deep learning model using CNN, Transformer blocks, and BiLSTM.
Incorporated an attentive convolution mechanism within Transformer blocks.
Trained cell line-specific models using sequence data.

Main Results:

CellCircLoc accurately predicts circRNA subcellular localization across diverse cell lines.
The model outperforms existing methods that do not consider cell line specificity.
Demonstrated the interpretability of CellCircLoc for motif discovery.

Conclusions:

Cell line-specific prediction is crucial for accurate circRNA localization.
CellCircLoc offers a powerful and interpretable tool for circRNA research.
Highlights the context-dependent nature of circRNA regulation.