Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample.

ABSTRACT:

Background

Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data.

Results

In this study, we use the first comprehensive and well-characterized somatic reference data sets from the SEQC2 consortium to investigate best practices for using a deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for a cancer cell line by the consortium, we identify the best strategy for building robust models on multiple data sets derived from samples representing real scenarios, for exampl

SUBMITTER: Sahraeian SME 

PROVIDER: S-EPMC8740374 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature