Noninvasive fetal genotyping using deep neural networks.

ABSTRACT: Circulating cell-free DNA (cfDNA) is a powerful diagnostics tool that is widely studied in the context of liquid biopsy in oncology and other fields. In obstetrics, maternal plasma cfDNA have already proven its utility, enabling noninvasive prenatal testing (NIPT), which has become a standard for detecting chromosomal aberrations. However, identification of point mutations responsible for monogenic diseases (NIPT-M) remains limited, even when accounting to fragment specific characteristics (i.e. fragmentomics). While genotyping of individual genomes is performed today using deep learning (DL) algorithms, cfDNA-based noninvasive fetal genotyping is performed only using traditional statistical and machine-learning methods. This study introduces the first DL-based framework for cfDNA based ge

SUBMITTER: Schwammenthal Y 

PROVIDER: S-EPMC11848515 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature