title: Hansa: an automated method for discriminating disease and neutral human nsSNPs.
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.
authors: Laboratory of Computational Biology, Centre for DNA Fingerprinting & Diagnostics, Bldg7, Gruhakalpa, Nampally, Hyderabad, 500001, Andhra Pradesh, India.
authors: Vishal
authors: V
authors: Acharya
authors: Hampapathalu A
authors: HA
authors: Nagarajaram
mesh_terms: D019295:Computational Biology / Q000379:methods*; D056726:Genetic Association Studies; D020022:Genetic Predisposition to Disease; D006801:Humans; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine