Identifying True Variants in Somatic Cancer Using Machine Learning
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We presented a computational classifier to identify true positive SNVs from tumor sequencing. The classifier demonstrated high sensitivity/specificity, and utility on a wide range of tumor samples. Overall, 96% of the SNVs detected will receive a definite label and thus be exempt from manual review. Implementing the model can greatly reduce the hands-on time and hence improve the efficiency without compromising the quality of the clinical tests. Presented at AMP Global Congress 2019. Read more