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Using Machine Learning to Facilitate Classification of Somatic Variants from Next-Generation Sequencing
[article]
2019
bioRxiv
pre-print
AbstractBackgroundMolecular profiling has become essential for tumor risk stratification and treatment selection. However, cancer genome complexity and technical artifacts make identification of real variants a challenge. Currently, clinical laboratories rely on manual screening, which is costly, subjective, and not scalable. Here we present a machine learning-based method to distinguish artifacts from bona fide Single Nucleotide Variants (SNVs) detected by NGS from tumor specimens.MethodsA
doi:10.1101/670687
fatcat:7agyw6pyb5fnjirvmoltdfblo4