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Song Y, Yin Z, Zhang C, Hao S, Li H, Wang S, Yang X, Li Q, Zhuang D, Zhang X, Cao Z, Ma X. Random forest classifier improving phenylketonuria screening performance in two Chinese populations. Front Mol Biosci 2022; 9:986556. [PMID: 36304929 PMCID: PMC9592754 DOI: 10.3389/fmolb.2022.986556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 09/26/2022] [Indexed: 11/13/2022] Open
Abstract
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.
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Affiliation(s)
- Yingnan Song
- National Human Genetic Resources Center, National Research Institute for Family Planning, Beijing, China
- 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
- Graduate School of Peking Union Medical College, Beijing, China
- 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
- *Correspondence: Zongfu Cao, ; Xu Ma,
| | - Xu Ma
- National Human Genetic Resources Center, National Research Institute for Family Planning, Beijing, China
- Graduate School of Peking Union Medical College, Beijing, China
- *Correspondence: Zongfu Cao, ; Xu Ma,
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