Self-learning neural network as a prediction model in non-invasive prenatal testing to detect fetal SNVs
Author:
Funder
National Natural Science Foundation of China
Guangdong Medical Research Foundation
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1186/s12967-024-05433-y.pdf
Reference4 articles.
1. Brand H, Whelan CW, Duyzend M, et al. High-resolution and noninvasive fetal exome screening. N Engl J Med. 2023;389(21):2014–6.
2. Miceikaitė I, Hao Q, Brasch-Andersen C, et al. Comprehensive noninvasive fetal screening by Deep Trio-Exome sequencing. N Engl J Med. 2023;389(21):2017–9.
3. Li J, Lu J, Su F, et al. Non-invasive prenatal diagnosis of monogenic disorders through bayesian- and haplotype-based prediction of fetal genotype. Front Genet. 2022;13:911369.
4. Rabinowitz T, Polsky A, Golan D, et al. Bayesian-based noninvasive prenatal diagnosis of single-gene disorders. Genome Res. 2019;29(3):428–38.
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