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2000
Volume 25, Issue 4
  • ISSN: 1566-5232
  • E-ISSN: 1875-5631

Abstract

Introduction

Lung cancer stands as one of the most prevalent malignant neoplasms, with microRNAs (miRNAs) playing a pivotal role in the modulation of gene expression, impacting cancer cell proliferation, invasion, metastasis, immune escape, and resistance to therapy.

Methods

The intricate role of miRNAs in lung cancer underscores their significance as biomarkers for early detection and as novel targets for therapeutic intervention. Traditional approaches for the identification of miRNAs related to lung cancer, however, are impeded by inefficiencies and complexities.

Results

In response to these challenges, this study introduced an innovative deep-learning strategy designed for the efficient and precise identification of lung cancer-associated miRNAs. Through comprehensive benchmark tests, our method exhibited superior performance relative to existing technologies.

Conclusion

Further case studies have also confirmed the ability of our model to identify lung cancer-associated miRNAs that have undergone biological validation.

© 2025 Bentham Science Publishers
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2024-09-13
2025-09-01

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DeepLCRmiRNA: A Hybrid Neural Network Approach for Identifying Lung Cancer-Associated miRNAs
Curr. Gene Ther. 25, 559 (2025); https://doi.org/10.2174/0115665232312364240902060458
/content/journals/cgt/10.2174/0115665232312364240902060458
/content/journals/cgt/10.2174/0115665232312364240902060458
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  • Article Type:     Research Article
Keyword(s): cell proliferation; CNN; deep learning; Lung cancer; metastasis; miRNA

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