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GenomomFF: Cost-Effective Method to Measure Fetal Fraction by Adaptive Multiple Regression Techniques with Optimally Selected Autosomal Chromosome Regionsoa mark
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Publication Year
2020-01-01
Journal
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
IEEE Access, Vol.8, pp.106880-106888
Keyword
cell-free fetal DNAfetal fractionmultiple regressionNon-invasive prenatal testingpersonalized machine learning
Mesh Keyword
Adaptive machine learningAdditional costsCost-effective methodsMultiple regression techniquesPregnant womanPrenatal testingTraining dataTraining sample
All Science Classification Codes (ASJC)
Computer Science (all)Materials Science (all)Engineering (all)
Abstract
The accurate measure of fetal fraction is important to ensure the results of noninvasive prenatal testing. However, measuring fetal fraction could require a substantial amount of data and additional costs. Therefore, this study proposes an alternative method of measuring fetal fraction with a limited sample size and low sequencing reads. Adaptive machine-learning algorithms customized to each laboratory's environment, were used to measure fetal fraction. Pregnant women with female fetuses were tested to exclude the bias caused by training data of women carrying male fetuses. The accuracy of fetal DNA fraction prediction was enhanced by increasing the training sample size. When trained with 1,000 samples (males) and tested with 45 samples (females), the optimal bin sizes using the read count and size features were 300 kb and 800 kb, respectively. Comparing the new 300-kb bin to the 50-kb bin used by SeqFF with 4,000-5,000 training samples, the correlation was approximately 3-5% higher with the 300-kb bin. Therefore, we propose an effective and tailored method to measure fetal fraction in individual laboratories with limited sample collecting conditions and relatively low-coverage sequencing data.
ISSN
2169-3536
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/31378
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85086997343&origin=inward
DOI
https://doi.org/10.1109/access.2020.3000483
Journal URL
http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639
Type
Article
Funding
This work was supported by the Technology Development Program funded by the Ministry of SMEs and Startups (MSS, Korea) under Grant C0566130.
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