SiRNA silencing efficacy prediction based on a deep architecture.

Journal: BMC genomics
Published Date: Sep 24, 2018

Abstract

BACKGROUND: Small interfering RNA (siRNA) can be used to post-transcriptional gene regulation by knocking down targeted genes. In functional genomics, biomedical research and cancer therapeutics, siRNA design is a critical research topic. Various computational algorithms have been developed to select the most effective siRNA, whereas the efficacy prediction accuracy is not so satisfactory. Many existing computational methods are based on feature engineering, which may lead to biased and incomplete features. Deep learning utilizes non-linear mapping operations to detect potential feature pattern and has been considered perform better than existing machine learning method.

Authors

  • Ye Han
    School of Information Technology, Jilin Agricultural University, Changchun, China.
  • Fei He
    Biology Department, Brookhaven National Laboratory, Upton, New York, USA.
  • Yongbing Chen
    School of Information Science and Technology, Northeast Normal University, Changchun, China.
  • Yuanning Liu
    Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China.
  • Helong Yu
    Smart Agriculture Research Institute, Jilin Agricultural University, Changchun, China.

Keywords

External Resources

Popular Topics
Recent Journals