Random forest classifier improving phenylketonuria screening performance in two Chinese populations

Yingnan Song; Yingnan Song; Zhe Yin; Chuan Zhang; Chuan Zhang; Chuan Zhang; Shengju Hao; Haibo Li; Shifan Wang; Xiangchun Yang; Qiong Li; Danyan Zhuang; Xinyuan Zhang; Zongfu Cao; Xu Ma; Xu Ma

doi:10.3389/fmolb.2022.986556

Frontiers in Molecular Biosciences (Oct 2022)

Random forest classifier improving phenylketonuria screening performance in two Chinese populations

Yingnan Song,
Yingnan Song,
Zhe Yin,
Chuan Zhang,
Chuan Zhang,
Chuan Zhang,
Shengju Hao,
Haibo Li,
Shifan Wang,
Xiangchun Yang,
Qiong Li,
Danyan Zhuang,
Xinyuan Zhang,
Zongfu Cao,
Xu Ma,
Xu Ma

Affiliations

Yingnan Song: National Human Genetic Resources Center, National Research Institute for Family Planning, Beijing, China
Yingnan Song: Graduate School of Peking Union Medical College, Beijing, China
Zhe Yin: National Human Genetic Resources Center, National Research Institute for Family Planning, Beijing, China
Chuan Zhang: National Human Genetic Resources Center, National Research Institute for Family Planning, Beijing, China
Chuan Zhang: Graduate School of Peking Union Medical College, Beijing, China
Chuan Zhang: Gansu Province Medical Genetics Center, Gansu Provincial Clinical Research Center for Birth Defects and Rare Diseases, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
Shengju Hao: Gansu Province Medical Genetics Center, Gansu Provincial Clinical Research Center for Birth Defects and Rare Diseases, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
Haibo Li: The Central Laboratory of Birth Defects Prevention and Control, Ningbo Women and Children’s Hospital, Ningbo, China
Shifan Wang: Gansu Province Medical Genetics Center, Gansu Provincial Clinical Research Center for Birth Defects and Rare Diseases, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
Xiangchun Yang: The Central Laboratory of Birth Defects Prevention and Control, Ningbo Women and Children’s Hospital, Ningbo, China
Qiong Li: The Central Laboratory of Birth Defects Prevention and Control, Ningbo Women and Children’s Hospital, Ningbo, China
Danyan Zhuang: The Central Laboratory of Birth Defects Prevention and Control, Ningbo Women and Children’s Hospital, Ningbo, China
Xinyuan Zhang: National Human Genetic Resources Center, National Research Institute for Family Planning, Beijing, China
Zongfu Cao: National Human Genetic Resources Center, National Research Institute for Family Planning, Beijing, China
Xu Ma: National Human Genetic Resources Center, National Research Institute for Family Planning, Beijing, China
Xu Ma: Graduate School of Peking Union Medical College, Beijing, China

DOI: https://doi.org/10.3389/fmolb.2022.986556
Journal volume & issue: Vol. 9

Abstract

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Phenylketonuria (PKU) is a genetic disorder with amino acid metabolic defect, which does great harms to the development of newborns and children. Early diagnosis and treatment can effectively prevent the disease progression. Here we developed a PKU screening model using random forest classifier (RFC) to improve PKU screening performance with excellent sensitivity, false positive rate (FPR) and positive predictive value (PPV) in all the validation dataset and two testing Chinese populations. RFC represented outstanding advantages comparing several different classification models based on machine learning and the traditional logistic regression model. RFC is promising to be applied to neonatal PKU screening.

Published in Frontiers in Molecular Biosciences

ISSN: 2296-889X (Online)
Publisher: Frontiers Media S.A.
Country of publisher: Switzerland
LCC subjects: Science: Biology (General)
Website: https://www.frontiersin.org/journals/molecular-biosciences

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