NeoMutate: an ensemble machine learning framework for the prediction of somatic mutations in cancer.

ABSTRACT:

Background

The accurate screening of tumor genomic landscapes for somatic mutations using high-throughput sequencing involves a crucial step in precise clinical diagnosis and targeted therapy. However, the complex inherent features of cancer tissue, especially, tumor genetic intra-heterogeneity coupled with the problem of sequencing and alignment artifacts, makes somatic variant calling a challenging task. Current variant filtering strategies, such as rule-based filtering and consensus voting of different algorithms, have previously helped to increase specificity, although comes at the cost of sensitivity.

Methods

In light of this, we have developed the NeoMutate framework which incorporates 7 supervised machine learning (ML) algorithms to exploit the strengths of multiple v

SUBMITTER: Anzar I 

PROVIDER: S-EPMC6524241 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature