Synthetic data-driven AI approach for fetal chromosomal aneuploidies detection

All formulas and procedures required for synthetic data generation and model development are implemented in Python and are available at https://github.com/genomecare-rnd/SyntheticData-NIPT.

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Publication Details

Journal
Bioinformatics Advances
Published
2024-12-26
DOI
https://doi.org/10.1093/bioadv/vbaf244
Primary Topic
Prenatal Screening and Diagnostics
Type
article
Field-Weighted Citation Impact
0.00

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article

Synthetic data-driven AI approach for fetal chromosomal aneuploidies detection

C.C. Hwang, Kaustubh Adhikari, Sunshin Kim, G. Oh
Bioinformatics Advances
Prenatal Screening and Diagnostics
article

Synthetic data-driven AI approach for fetal chromosomal aneuploidies detection

C.C. Hwang, Kaustubh Adhikari, Sunshin Kim, G. Oh
article en

Abstract

All formulas and procedures required for synthetic data generation and model development are implemented in Python and are available at https://github.com/genomecare-rnd/SyntheticData-NIPT.

Bioinformatics AdvancesVol. 5(1)
Genome Research Foundation (KR)
Ministry of SMEs and Startups
Openalex Percentile: Top 54%
Prenatal Screening and Diagnostics
DOIOpen Access PDFOpenAlex
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Synthetic data-driven AI approach for fetal chromosomal aneuploidies detection — C.C. Hwang, Kaustubh Adhikari, et al. · Bioinformatics Advances (2024) | TGRS Research Map | TGRS