title: Prediction of the functional roles of small molecules in lipid metabolism based on ensemble learning.
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.
authors.affiliation: School of Materials Science and Engineering, Shanghai University, Shanghai, China.
authors.forename: Chun-Rong
authors.initials: CR
authors.lastname: Peng
authors.forename: Wen-Cong
authors.initials: WC
authors.lastname: Lu
authors.forename: Bing
authors.initials: B
authors.lastname: Niu
authors.forename: Ya-Jun
authors.initials: YJ
authors.lastname: Li
authors.forename: Le-Le
authors.initials: LL
authors.lastname: Hu
mesh_terms: D001185:Artificial Intelligence; D019295:Computational Biology; D016208:Databases, Factual; D050356:Lipid Metabolism; D053858:Metabolic Networks and Pathways; D011237:Predictive Value of Tests; D054852:Small Molecule Libraries / Q000737:chemistry* / Q000378:metabolism
chemical_list: D054852:Small Molecule Libraries