Predicting the metabolic pathways of small molecules based on their physicochemical properties.
How to correctly and efficiently map small molecule to its possible metabolic pathway is a meaningful topic to metabonomics research. In this work, a novel approach to address this problem was introduced to encode physicochemical properties of small molecules. Based on this encoding method, a two stage feature selection method called mRMR-FFSAdaBoost was adopted to map small molecules to their corresponding metabolic pathways possible. As a result, the accuracies of 10-folds cross-validation test and independent set test for predicting the metabolic pathways of small molecules reached 83.88% and 85.23%, respectively. An online server for predicting metabolic pathways of unknown small molecules as described in this paper is accessible at http://chemdata.shu.edu.cn:8080/PathwayPrediction/.
School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China.
Chun-Rong
CR
Peng
Wen-Cong
WC
Lu
Bing
B
Niu
Min-Jie
MJ
Li
Xiao-Yan
XY
Yang
Mi-Lin
ML
Wu
D000465:Algorithms; D001185:Artificial Intelligence; D055598:Chemical Phenomena; D016208:Databases, Factual; D020407:Internet; D053858:Metabolic Networks and Pathways; D055432:Metabolomics / Q000379:methods*; D008970:Molecular Weight; D015203:Reproducibility of Results