Subcellular localization prediction of apoptosis proteins based on evolutionary information and support vector machine.

Journal: Artificial intelligence in medicine
PMID: 28764871

Abstract

OBJECTIVES: In this paper, a high-quality sequence encoding scheme is proposed for predicting subcellular location of apoptosis proteins.

Authors

  • Qilin Xiang
    School of Information Science and Engineering, Hunan University, Changsha 410082, China.
  • Bo Liao
  • Xianhong Li
    College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China.
  • Huimin Xu
    College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China.
  • Jing Chen
    Department of Vascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.
  • Zhuoxing Shi
    College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China.
  • Qi Dai
    The First Clinical Medical College, Guangxi University of Chinese Medicine, Nanning 530001, China.
  • Yuhua Yao
    College of Life Sciences, Zhejiang Sci-Tech University, Hangzhou 310018, China; School of Mathematics and Statistics, Hainan Normal University, Haikou 571158, China. Electronic address: yaoyuhua2288@163.com.

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