Prediction of the Functional Roles of Small Molecules in Lipid Metabolism Based on Ensemble Learning

Title: Prediction of the Functional Roles of Small Molecules in Lipid Metabolism Based on Ensemble Learning

Volume: 19 Issue: 1

Author(s): Chun-Rong Peng, Wen-Cong Lu, Bing Niu, Ya-Jun Li and Le-Le Hu

Affiliation:

Keywords: small molecules, metabolic pathway, lipid metabolism, KEGG, ensemble learning, Bagging, jackknife cross validation test, KNN, SVM, kernel function, CFS, TM, `Re-substitution test, Independent set test, ANN

Abstract: As many diseases like high cholesterol are referred to lipid metabolism, studying the lipid metabolic pathway has a positive effect on finding the knowledge about interactions between different elements within high complex living systems. Here, we employed a typical ensemble learning method, Bagging learner, to study and predict the possible sub lipid metabolic pathway of small molecules based on physical and chemical features of the compounds. As a result, jackknife cross validation test and independent set test on the model reached 89.85% and 91.46%, respectively. Therefore, our predictor may be used for finding the new compounds which participate in lipid metabolic procedures.

Cite this article as:

Peng Chun-Rong, Lu Wen-Cong, Niu Bing, Li Ya-Jun and Hu Le-Le, Prediction of the Functional Roles of Small Molecules in Lipid Metabolism Based on Ensemble Learning, Protein & Peptide Letters 2012; 19 (1) . https://dx.doi.org/10.2174/092986612798472802