Adaptive learning embedding features to improve the predictive performance of SARS-CoV-2 phosphorylation sites.

Journal: Bioinformatics (Oxford, England)
PMID: 37847658

Abstract

MOTIVATION: The rapid and extensive transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to an unprecedented global health emergency, affecting millions of people and causing an immense socioeconomic impact. The identification of SARS-CoV-2 phosphorylation sites plays an important role in unraveling the complex molecular mechanisms behind infection and the resulting alterations in host cell pathways. However, currently available prediction tools for identifying these sites lack accuracy and efficiency.

Authors

  • Shihu Jiao
    Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan.
  • Xiucai Ye
    Department of Computer Science, University of Tsukuba, Tsukuba, Science City, Japan.
  • Chunyan Ao
    Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, P. R. China.
  • Tetsuya Sakurai
    Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan.
  • Quan Zou
  • Lei Xu
    Key Laboratory of Biomedical Information Engineering of the Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China.

Keywords

External Resources

Popular Topics
Recent Journals