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

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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.





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

Document Type: Research Article

DOI: http://dx.doi.org/10.2174/092986612798472802

Publication date: January 1, 2012

More about this publication?
  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
ben/ppl/2012/00000019/00000001/art00015
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