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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

Predicting the potential associations between circRNA and drug sensitivity using a multisource feature-based

Shuaidong Yin¹, Peng Xu², Yefeng Jiang¹

¹School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, China.

Journal of Cellular and Molecular Medicine

|September 30, 2024

Summary

This study introduces SNMGCDA, a deep learning model predicting circular RNA (circRNA) and drug sensitivity relationships. SNMGCDA accurately identifies novel circRNA-drug interactions, advancing personalized medicine.

Keywords:

circRNA drug graph neural network non‐negative matrix factorization sparse autoencoder

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

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Area of Science:

Genomics
Computational Biology
Pharmacology

Background:

Circular RNAs (circRNAs) are unique non-coding RNA molecules with significant implications for cellular drug sensitivity and therapeutic outcomes.
Traditional methods for identifying circRNA-drug sensitivity correlations are time-consuming and expensive, necessitating advanced computational approaches.

Purpose of the Study:

To develop an efficient deep learning model, SNMGCDA, for predicting potential relationships between circRNAs and drug sensitivity.
To overcome the limitations of classical biological research by providing a faster and more cost-effective computational solution.

Main Methods:

SNMGCDA integrates diverse similarity networks and employs a sparse autoencoder for drug feature extraction.
Non-negative matrix factorization (NMF) and a multi-head graph attention network are utilized for circRNA and drug feature identification and relationship analysis.
Combined feature vectors are fed into a multilayer perceptron (MLP) for prediction.

Main Results:

SNMGCDA demonstrated superior performance compared to five state-of-the-art methods, validated by 5-fold and 10-fold cross-validation.
The model successfully predicted novel circRNA-drug sensitivity correlations, confirmed through case studies.
Experimental results highlight SNMGCDA's reliability and effectiveness in uncovering potential circRNA-drug interactions.

Conclusions:

SNMGCDA offers a powerful computational tool for predicting circRNA-drug sensitivity relationships.
The model's accuracy and ability to identify novel correlations contribute to advancing drug discovery and personalized treatment strategies.
This work underscores the potential of deep learning in unraveling complex biological interactions for therapeutic applications.