UNASSIGNED: SNPs_GO is a machine learning method for predicting the association of single @entity3689 variations (SAVs) to disease, considering protein functional annotation. The method is a binary classifier that implements a Support Vector Machine algorithm to discriminate between disease-related and neutral SAVs. SNPs_GO combines information from protein sequence with functional annotation encoded by Gene Ontology terms. Tested in sequence mode on more than 38,000 SAVs from the SwissVar dataset, our method reached 81% overall accuracy and an area under the receiving operating characteristic curve (AUC) of 0.88 with low false positive rate. In almost all the editions of the Critical Assessment of Genome Interpretation (CAGI) experiments, SNPs_GO ranked among the most accurate algorithms for predicting the effect of SAVs. In this paper we summarize the best results obtained by SNPs_GO on disease related variations of four CAGI challenges relative to the following genes: @entity9516 (CAGI 2010), @entity15125 (CAGI 2011), @entity986 -INK (CAGI 2013) and @entity11895 (CAGI 2016). Result evaluation provides insights about the accuracy of our algorithm and the relevance of GO terms in annotating the effect of the variants. It also helps to define good practices for the detection of deleterious SAVs. AVAILABILITY: SNPs_GO is accessible at http://snps.biofold.org/snps-and-go or http://snps-and-go.biocomp.unibo.it This article is protected by copyright. All rights reserved.

Blind Prediction of Deleterious XXXX Variations with SNPs_GO.

@entity15125 :: ('10111', 'Gene') :: ['RAD50'] @entity11895 :: ('4669', 'Gene') :: ['NAGLU'] @entity986 :: ('1029', 'Gene') :: ['p16'] @entity3689 :: ('D000596', 'Chemical') :: ['amino acid'] @entity9516 :: ('11200', 'Gene') :: ['CHEK2']
@entity3689 :: (D000596,Chemical) :: ['Amino Acid']
