Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/31645
Title: Investigation into the use of support vector machine for -omics applications
Authors: GUO YANGFAN
Keywords: Support Vector Machine, -Omics, MHC, Metabonomics, SVM-RFE, Metabolite Biomarker
Issue Date: 1-Aug-2011
Source: GUO YANGFAN (2011-08-01). Investigation into the use of support vector machine for -omics applications. ScholarBank@NUS Repository.
Abstract: Machine learning methods have frequently been used in early stage diagnosis at the proteomic level, such as the MHC binding peptides prediction and biomarkers selection for metabonomics. Although many computational methods have been designed for such studies, it is necessary to develop more stable and smart system to improve predictive performance. Support vector machine, an artificial intelligence technique, demonstrates remarkable generalization performance. Two groups of MHC binding peptides and two bladder cancer metabonomics datasets with different number of metabolites has been investigated by support vector machine and other machine learning methods. Recursive feature elimination, an effective feature selection algorithm, has also been applied to investigate the metabonomics data. The results of MHC binding peptide study showed that the prediction system can achieve satisfactory performance by constructing the model with sufficient generated non-binding peptides. The second study on metabonomics prediction suggested that metabolites biomarkers can be effectively selected from the metabonomics dataset by support vector machine-recursive feature elimination method.
URI: http://scholarbank.nus.edu.sg/handle/10635/31645
Appears in Collections:Master's Theses (Open)

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