Performance improvement for a 2D convolutional neural network by using SSC encoding on protein-protein interaction tasks.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: The interactions of proteins are determined by their sequences and affect the regulation of the cell cycle, signal transduction and metabolism, which is of extraordinary significance to modern proteomics research. Despite advances in experimental technology, it is still expensive, laborious, and time-consuming to determine protein-protein interactions (PPIs), and there is a strong demand for effective bioinformatics approaches to identify potential PPIs. Considering the large amount of PPI data, a high-performance processor can be utilized to enhance the capability of the deep learning method and directly predict protein sequences.

Authors

  • Yang Wang
    Department of General Surgery The First People's Hospital of Yunnan Province, The Affiliated Hospital of Kunming University of Science and Technology Kunming China.
  • Zhanchao Li
    School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, People's Republic of China.
  • Yanfei Zhang
    Genomic Medicine Institute, Geisinger Health System, Danville,Penn.
  • Yingjun Ma
    School of Chemistry, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China.
  • Qixing Huang
    School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, People's Republic of China.
  • Xingyu Chen
    Department of Infectious Diseases, Wenzhou Central Hospital, Wenzhou, China.
  • Zong Dai
    School of Chemistry, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China.
  • Xiaoyong Zou
    School of Chemistry, Sun Yat-Sen University, Guangzhou, 510275, People's Republic of China. ceszxy@mail.sysu.edu.cn.