Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy.

Journal: BMC systems biology
Published Date:

Abstract

BACKGROUND: It is necessary and essential to discovery protein function from the novel primary sequences. Wet lab experimental procedures are not only time-consuming, but also costly, so predicting protein structure and function reliably based only on amino acid sequence has significant value. TATA-binding protein (TBP) is a kind of DNA binding protein, which plays a key role in the transcription regulation. Our study proposed an automatic approach for identifying TATA-binding proteins efficiently, accurately, and conveniently. This method would guide for the special protein identification with computational intelligence strategies.

Authors

  • Quan Zou
  • Shixiang Wan
    School of Computer Science and Technology, Tianjin University, Tianjin, China.
  • Ying Ju
    School of Information Science and Engineering, Xiamen University, Xiamen, China.
  • Jijun Tang
    School of Computer Science and Engineering, Tianjin University, Tianjin, 300072, China. jtang@cse.sc.edu.
  • Xiangxiang Zeng
    Department of Computer Science, Hunan University, Changsha, China.