Please use this identifier to cite or link to this item:
Title: Investigation into the use of support vector machine for -omics applications
Keywords: Support Vector Machine, -Omics, MHC, Metabonomics, SVM-RFE, Metabolite Biomarker
Issue Date: 1-Aug-2011
Citation: 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.
Appears in Collections:Master's Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
GuoYF.pdf1.75 MBAdobe PDF



Page view(s)

checked on May 22, 2019


checked on May 22, 2019

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.