Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/15918
Title: Application of support vector machine to prediction of cross-reactive allergens and recognition of viral sequences
Authors: MUH HON CHENG
Keywords: support vector machine, allergen, prediction, codon usage indices, virus, pairwise
Issue Date: 6-May-2009
Source: MUH HON CHENG (2009-05-06). Application of support vector machine to prediction of cross-reactive allergens and recognition of viral sequences. ScholarBank@NUS Repository.
Abstract: The generalization property of support vector machine and the choice of feature vectors are important in determining the success of a classifier. Described in this paper are two novel prediction methods utilizing support vector machines: 1. AllerHunter, a cross-reactive allergen prediction based on pairwise sequence similarity, and 2. VIPR, a viral sequence prediction method based on codon usage indices. AllerHunter performs significantly better (sensitivity=88.4%; specificity to allergen-like putative non-allergen=91.4%; specificity to divergent putative non-allergen=100%) than leading cross-reactive allergen prediction methods to date. VIPR is a unique tool that lacks comparison to date(sensitivity=90.5%; specificity=94.1%), which complements existing identification methods by suggesting a potential viral origin of sequences in samples.
URI: http://scholarbank.nus.edu.sg/handle/10635/15918
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