LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism.

Journal: Bioinformatics (Oxford, England)
Published Date: Dec 1, 2023

Abstract

MOTIVATION: There is mounting evidence that the subcellular localization of lncRNAs can provide valuable insights into their biological functions. In the real world of transcriptomes, lncRNAs are usually localized in multiple subcellular localizations. Furthermore, lncRNAs have specific localization patterns for different subcellular localizations. Although several computational methods have been developed to predict the subcellular localization of lncRNAs, few of them are designed for lncRNAs that have multiple subcellular localizations, and none of them take motif specificity into consideration.

Authors

  • Min Zeng
    Nephrology Department, Affiliated Hospital of Southern Medical University: Shenzhen Longhua New District People's Hospital, Shenzhen, China.
  • Yifan Wu
    Department of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China.
  • Yiming Li
    Department of Cardiology, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Rui Yin
    Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, College of Medicine, FL, USA. Electronic address: ruiyin@ufl.edu.
  • Chengqian Lu
    School of Computer Science and Engineering, Central South University, Changsha 410083, China.
  • Junwen Duan
    Hunan Provincial Key Laboratory on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, P.R. China.
  • Min Li
    Hubei Provincial Institute for Food Supervision and Test, Hubei Provincial Engineering and Technology Research Center for Food Quality and Safety Test, Wuhan 430075, China.

Keywords

External Resources

Popular Topics
Recent Journals