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Title: Efficacy of different protein descriptors in predicting protein functional families using support vector machine
Keywords: SVM, protein, descriptor, feature, sequence, benchmark
Issue Date: 29-Jan-2008
Citation: ONG AI KIANG, SERENE (2008-01-29). Efficacy of different protein descriptors in predicting protein functional families using support vector machine. ScholarBank@NUS Repository.
Abstract: Sequence-derived structural and physicochemical descriptors have frequently been used in machine learning prediction of protein functional families; there is thus a need to comparatively evaluate the effectiveness of these descriptor-sets by using the same method and parameter optimization algorithm, and to examine whether the combined use of these descriptor-sets help to improve predictive performance. Six individual descriptor-sets and four combination-sets were evaluated in support vector machines (SVM) prediction of six protein functional families. While there is no overwhelmingly favourable choice of descriptor-sets, certain trends were found. The combination-sets tend to give slightly but consistently higher MCC values and thus overall best performance; in particular, three out of four combination-sets show slightly better performance compared to one out of six individual descriptor-sets. This study suggests that currently used descriptor-sets are generally useful for classifying proteins and that prediction performance may be enhanced by exploring combinations of descriptors.
Appears in Collections:Master's Theses (Open)

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