Abstract
Recent advances in next-generation sequencing (NGS) technology have led to the discovery of a large number of variants of unknown significance (VUS), for which there is no clear classification of their significance to cancer risk. Various in silico mutation prediction tools have been developed to categorize such variants by pathogenicity and shed insight into their clinical actionability. Given differences in their algorithms, however, many of these in silico tools fail to generate the same predictions for pathogenicity.
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