Validation of genetic variants from NGS data using Deep Convolutional Neural Networks

Development of a bioinformatics tool for validation of genetic variants from NGS WES data with machine learning using deep convolutional neural networks. This tool can be used to distinguish genuine mutations from sequencing artifacts after the process of variant calling and could thereby accelerate and automate the process of time-consuming manual validation of variants.