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,
Qiuzhen Lin,
Yanni Sun,
Ka-Chun Wong
Affiliations
Weidun Xie
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
Xingjian Chen
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
Zetian Zheng
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
Fuzhou Wang
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
Xiaowei Zhu
Department of Neuroscience, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
Qiuzhen Lin
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
Yanni Sun
Department of Electrical Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong SAR
Ka-Chun Wong
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR; Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China; Hong Kong Institute for Data Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR; Corresponding author
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.
