ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning.

Journal: Bioinformatics (Oxford, England)
Published Date:

Abstract

MOTIVATION: Recently, peptides have emerged as a promising class of pharmaceuticals for various diseases treatment poised between traditional small molecule drugs and therapeutic proteins. However, one of the key bottlenecks preventing them from therapeutic peptides is their toxicity toward human cells, and few available algorithms for predicting toxicity are specially designed for short-length peptides.

Authors

  • Lesong Wei
    Department of Computer Science, University of Tsukuba, Tsukuba, Japan, 3058577.
  • Xiucai Ye
    Department of Computer Science, University of Tsukuba, Tsukuba, Science City, Japan.
  • Tetsuya Sakurai
    Department of Computer Science, University of Tsukuba, Tsukuba 3058577, Japan.
  • Zengchao Mu
    School of Mathematics and Statistics, Shandong University, Weihai, China.
  • Leyi Wei
    School of Computer Science and Technology, Tianjin University, Tianjin, 30050, China.