Predicting protein subcellular locations with feature selection and analysis.
In this paper, we propose a strategy to predict the subcellular locations of proteins by combining various feature selection methods. Firstly, proteins are coded by amino-acid composition and physicochemical properties, then these features are arranged by Minimum Redundancy Maximum Relevance method and further filtered by feature selection procedure. Nearest Neighbor Algorithm is used as a prediction model to predict the protein subcellular locations, and gains a correct prediction rate of 70.63%, evaluated by Jackknife cross-validation. Results of feature selection also enable us to identify the most important protein properties. The prediction software is available for public access on the website http://chemdata.shu.edu.cn/sub22/, which may play a important complementary role to a series of web-server predictors summarized recently in a review by Chou and Shen (Chou, K.C., Shen, H.B. Natural Science, 2009, 2, 63-92, http://www.scirp.org/journal/NS/).
Cai
Yudong
Y
State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University, 197 Rui Jin Road II, Shanghai 200025, China. caiyudong@staff.shu.edu.cn
He
Jianfeng
J
Li
Xinlei
X
Feng
Kaiyan
K
Lu
Lin
L
Feng
Kairui
K
Kong
Xiangyin
X
Lu
Wencong
W
D000465:Algorithms; D000596:Amino Acids; D019295:Computational Biology / Q000379:methods*; D057927:Hydrophobic and Hydrophilic Interactions; D008956:Models, Chemical; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D015203:Reproducibility of Results; D013347:Subcellular Fractions / Q000737:chemistry*
D000596:Amino Acids; D011506:Proteins