DOAJ Open Access 2023

LncRNA-Top: Controlled deep learning approaches for lncRNA gene regulatory relationship annotations across different platforms

Weidun Xie Xingjian Chen Zetian Zheng Fuzhou Wang Xiaowei Zhu +3 lainnya

Abstrak

Summary: By soaking microRNAs (miRNAs), long non-coding RNAs (lncRNAs) have the potential to regulate gene expression. Few methods have been created based on this mechanism to anticipate the lncRNA-gene relationship prediction. Hence, we present lncRNA-Top to forecast potential lncRNA-gene regulation relationships. Specifically, we constructed controlled deep-learning methods using 12417 lncRNAs and 16127 genes. We have provided retrospective and innovative views among negative sampling, random seeds, cross-validation, metrics, and independent datasets. The AUC, AUPR, and our defined precision@k were leveraged to evaluate performance. In-depth case studies demonstrate that 47 out of 100 projected top unknown pairings were recorded in publications, supporting the predictive power. Our additional software can annotate the scores with target candidates. The lncRNA-Top will be a helpful tool to uncover prospective lncRNA targets and better comprehend the regulatory processes of lncRNAs.

Topik & Kata Kunci

Science

Penulis (8)

Weidun Xie

Xingjian Chen

Zetian Zheng

Fuzhou Wang

Xiaowei Zhu

Qiuzhen Lin

Yanni Sun

Ka-Chun Wong

Format Sitasi

APA MLA BibTeX

Xie, W., Chen, X., Zheng, Z., Wang, F., Zhu, X., Lin, Q. et al. (2023). LncRNA-Top: Controlled deep learning approaches for lncRNA gene regulatory relationship annotations across different platforms. https://doi.org/10.1016/j.isci.2023.108197

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Informasi Jurnal

Tahun Terbit: 2023
Sumber Database: DOAJ
DOI: 10.1016/j.isci.2023.108197
Akses: Open Access ✓