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Allelic phenotype prediction of phenylketonuria based on the machine learning method

BACKGROUND: Phenylketonuria (PKU) is caused by mutations in the phenylalanine hydroxylase (PAH) gene. Our study aimed to predict the phenotype using the allelic genotype. METHODS: A total of 1291 PKU patients with 623 various variants were used as the training dataset for predicting allelic phenotyp...

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Autores principales: Fang, Yang, Gao, Jinshuang, Guo, Yaqing, Li, Xiaole, Yuan, Enwu, Yuan, Erfeng, Song, Liying, Shi, Qianqian, Yu, Haiyang, Zhao, Dehua, Zhang, Linlin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10064562/
https://www.ncbi.nlm.nih.gov/pubmed/37004080
http://dx.doi.org/10.1186/s40246-023-00481-9
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author Fang, Yang
Gao, Jinshuang
Guo, Yaqing
Li, Xiaole
Yuan, Enwu
Yuan, Erfeng
Song, Liying
Shi, Qianqian
Yu, Haiyang
Zhao, Dehua
Zhang, Linlin
author_facet Fang, Yang
Gao, Jinshuang
Guo, Yaqing
Li, Xiaole
Yuan, Enwu
Yuan, Erfeng
Song, Liying
Shi, Qianqian
Yu, Haiyang
Zhao, Dehua
Zhang, Linlin
author_sort Fang, Yang
collection PubMed
description BACKGROUND: Phenylketonuria (PKU) is caused by mutations in the phenylalanine hydroxylase (PAH) gene. Our study aimed to predict the phenotype using the allelic genotype. METHODS: A total of 1291 PKU patients with 623 various variants were used as the training dataset for predicting allelic phenotypes. We designed a common machine learning framework to predict allelic genotypes associated with the phenotype. RESULTS: We identified 235 different mutations and 623 various allelic genotypes. The features extracted from the structure of mutations and graph properties of the PKU network to predict the phenotype of PKU were named PPML (PKU phenotype predicted by machine learning). The phenotype of PKU was classified into three different categories: classical PKU (cPKU), mild PKU (mPKU) and mild hyperphenylalaninemia (MHP). Three hub nodes (c.728G>A for cPKU, c.721 for mPKU and c.158G>A for HPA) were used as each classification center, and 5 node attributes were extracted from the network graph for machine learning training features. The area under the ROC curve was AUC = 0.832 for cPKU, AUC = 0.678 for mPKU and AUC = 0.874 for MHP. This suggests that PPML is a powerful method to predict allelic phenotypes in PKU and can be used for genetic counseling of PKU families. CONCLUSIONS: The web version of PPML predicts PKU allele classification supported by applicable real cases and prediction results. It is an online database that can be used for PKU phenotype prediction http://www.bioinfogenetics.info/PPML/. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40246-023-00481-9.
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spelling pubmed-100645622023-04-01 Allelic phenotype prediction of phenylketonuria based on the machine learning method Fang, Yang Gao, Jinshuang Guo, Yaqing Li, Xiaole Yuan, Enwu Yuan, Erfeng Song, Liying Shi, Qianqian Yu, Haiyang Zhao, Dehua Zhang, Linlin Hum Genomics Research BACKGROUND: Phenylketonuria (PKU) is caused by mutations in the phenylalanine hydroxylase (PAH) gene. Our study aimed to predict the phenotype using the allelic genotype. METHODS: A total of 1291 PKU patients with 623 various variants were used as the training dataset for predicting allelic phenotypes. We designed a common machine learning framework to predict allelic genotypes associated with the phenotype. RESULTS: We identified 235 different mutations and 623 various allelic genotypes. The features extracted from the structure of mutations and graph properties of the PKU network to predict the phenotype of PKU were named PPML (PKU phenotype predicted by machine learning). The phenotype of PKU was classified into three different categories: classical PKU (cPKU), mild PKU (mPKU) and mild hyperphenylalaninemia (MHP). Three hub nodes (c.728G>A for cPKU, c.721 for mPKU and c.158G>A for HPA) were used as each classification center, and 5 node attributes were extracted from the network graph for machine learning training features. The area under the ROC curve was AUC = 0.832 for cPKU, AUC = 0.678 for mPKU and AUC = 0.874 for MHP. This suggests that PPML is a powerful method to predict allelic phenotypes in PKU and can be used for genetic counseling of PKU families. CONCLUSIONS: The web version of PPML predicts PKU allele classification supported by applicable real cases and prediction results. It is an online database that can be used for PKU phenotype prediction http://www.bioinfogenetics.info/PPML/. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40246-023-00481-9. BioMed Central 2023-03-31 /pmc/articles/PMC10064562/ /pubmed/37004080 http://dx.doi.org/10.1186/s40246-023-00481-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Fang, Yang
Gao, Jinshuang
Guo, Yaqing
Li, Xiaole
Yuan, Enwu
Yuan, Erfeng
Song, Liying
Shi, Qianqian
Yu, Haiyang
Zhao, Dehua
Zhang, Linlin
Allelic phenotype prediction of phenylketonuria based on the machine learning method
title Allelic phenotype prediction of phenylketonuria based on the machine learning method
title_full Allelic phenotype prediction of phenylketonuria based on the machine learning method
title_fullStr Allelic phenotype prediction of phenylketonuria based on the machine learning method
title_full_unstemmed Allelic phenotype prediction of phenylketonuria based on the machine learning method
title_short Allelic phenotype prediction of phenylketonuria based on the machine learning method
title_sort allelic phenotype prediction of phenylketonuria based on the machine learning method
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10064562/
https://www.ncbi.nlm.nih.gov/pubmed/37004080
http://dx.doi.org/10.1186/s40246-023-00481-9
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