Prediction of protein subchloroplast locations using random forests

Chun Wei Tung, Chyn Liaw, Shinn Jang Ho, Shinn Ying Ho

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Protein subchloroplast locations are correlated with its functions. In contrast to the large amount of available protein sequences, the information of their locations and functions is less known. The experiment works for identification of protein locations and functions are costly and time consuming. The accurate prediction of protein subchloroplast locations can accelerate the study of functions of proteins in chloroplast. This study proposes a Random Forest based method, ChloroRF, to predict protein subchloroplast locations using interpretable physicochemical properties. In addition to high prediction accuracy, the ChloroRF is able to select important physicochemical properties. The important physicochemical properties are also analyzed to provide insights into the underlying mechanism.

Original languageEnglish
Pages (from-to)903-907
Number of pages5
JournalWorld Academy of Science, Engineering and Technology
Volume65
Publication statusPublished - May 1 2010
Externally publishedYes

    Fingerprint

Keywords

  • Chloroplast
  • Physicochemical properties
  • Protein locations
  • Random Forests

ASJC Scopus subject areas

  • Engineering(all)

Cite this