Acharya, V. and Nagarajaram, H.A. (2012) Hansa: An automated method for discriminating disease and neutral human nsSNPs. Human Mutation, 33 (2). pp. 332-337. ISSN 1059-7794

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Abstract

Variations are mostly due to nonsynonymous single nucleotide polymorphisms (nsSNPs), some of which are associated with certain diseases. Phenotypic effects of a large number of nsSNPs have not been characterized. Although several methods have been developed to predict the effects of nsSNPs as "disease" or "neutral," there is still a need for development of methods with improved prediction accuracies. We, therefore, developed a support vector machine (SVM) based method named Hansa which uses a novel set of discriminatory features to classify nsSNPs into disease (pathogenic) and benign (neutral) types. Validation studies on a benchmark dataset and further on an independent dataset of well-characterized known disease and neutral mutations show that Hansa outperforms the other known methods. For example, fivefold cross-validation studies using the benchmark HumVar dataset reveal that at the false positive rate (FPR) of 20% Hansa yields a true positive rate (TPR) of 82% that is about 10% higher than the best-known method. Hansa is available in the form of a web server at http://hansa.cdfd.org.in:8080. © 2011 Wiley Periodicals, Inc

Item Type:	Article
Additional Information:	[Open Access from the Publisher]
Depositing User:	Users 2 not found.
Date Deposited:	04 Sep 2015 07:40
Last Modified:	01 Nov 2015 18:40
URI:	http://cdfd.sciencecentral.in/id/eprint/471

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