SomaticSeq: An Ensemble Approach with Machine Learning to Detect Cancer Variants

June 16, 2016 | 1 pm to 2 pm EDTSponsored by Accurate detection of somatic mutations has proven to be challenging in cancer NGS analysis, due to tumor heterogeneity and cross-contamination between tumor and matched normal samples. Oftentimes, a somatic caller that performs well for one tumor may not for another. In this webinar we will introduce SomaticSeq, a tool within the Bina Genomic Management Solution (Bina GMS) designed to boost the accuracy of somatic mutation detection with a machine learning approach. Benchmarking of leading somatic callers, namely MuTect, SomaticSniper, VarScan2, JointSNVMix2, and VarDict Integration of such tools and how accuracy is achieved using a machine learning classifier that incorporates over 70 features with SomaticSeq Accuracy validation including results from the ICGC-TCGA DREAM Somatic Mutation Calling Challenge, in which Bina…


Link to Full Article: SomaticSeq: An Ensemble Approach with Machine Learning to Detect Cancer Variants

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