%0 Journal Article
	%A Chun-Wei Tung and  Chyn Liaw and  Shinn-Jang Ho and  Shinn-Ying Ho
	%D 2010
	%J International Journal of Biomedical and Biological Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 41, 2010
	%T Prediction of Protein Subchloroplast Locations using Random Forests
	%U https://publications.waset.org/pdf/12938
	%V 41
	%X 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
	%P 336 - 340