Hansa: An Automated Method for Discriminating Disease and Neutral Human nsSNPs

作者：Acharya Vishal; Nagarajaram Hampapathalu A^*

来源：Human Mutation, 2012, 33(2): 332-337.

DOI：10.1002/humu.21642

• nsSNPs
• missense mutation
• pathogenic mutation
• disease mutation
• neutral mutation
• support vector machine

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. Hum Mutat 33: 332-337, 2012.

• 出版日期2012-2