AMPpred-EL: An effective antimicrobial peptide prediction model based on ensemble learning.

Journal: Computers in biology and medicine
Published Date: May 4, 2022

Abstract

Antimicrobial peptides (AMPs) are important for the human immune system and are currently applied in clinical trials. AMPs have been received much attention for accurate recognition. Recently, several computational methods for identifying AMPs have been proposed. However, existing methods have difficulty in accurately predicting AMPs. In this paper, we propose a novel AMP prediction method called AMPpred-EL based on an ensemble learning strategy. AMPred-EL is constructed based on ensemble learning combined with LightGBM and logistic regression. Experimental results demonstrate that AMPpred-EL outperforms several state-of-the-art methods on the benchmark datasets and then improves the efficiency performance.

Authors

  • Hongwu Lv
    School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
  • Ke Yan
    Department of Biostatistics, Medical College of Wisconsin, Milwaukee, Wis.
  • Yichen Guo
    School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China. Electronic address: ycguo@bliulab.net.
  • Quan Zou
  • Abd El-Latif Hesham
    Genetics Department, Faculty of Agriculture, Beni-Suef University, Beni-Suef, 62511, Egypt. Electronic address: hesham_egypt5@aun.edu.eg.
  • Bin Liu
    Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China; Department of Endocrinology, Neijiang First People's Hospital, Chongqing, China.

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