Figure 1 | Scientific Reports

Figure 1

From: Prioritization Of Nonsynonymous Single Nucleotide Variants For Exome Sequencing Studies Via Integrative Learning On Multiple Genomic Data

Figure 1

Overview of snvForest.

Taking a query disease and a set of candidate nonsynonymous single nucleotide variants (nsSNVs) as input, snvForest predicts the strength of associations between the candidates and the query disease and produces a ranking list of the candidates as output. We achieve this goal by adopting an ensemble learning method named the random forest to integrate 11 functional scores that assess the functionally damaging effects of the candidate variants and 8 association scores that evaluate the strength of associations between the genes hosting the variants and the query disease.

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