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Genetic Risk Assessment of Nonsyndromic Cleft Lip with or without Cleft Palate by Linking Genetic Networks and Deep Learning Models
Recent deep learning algorithms have further improved risk classification capabilities. However, an appropriate feature selection method is required to overcome dimensionality issues in population-based genetic studies. In this Korean case–control study of nonsyndromic cleft lip with or without clef...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10003462/ https://www.ncbi.nlm.nih.gov/pubmed/36901988 http://dx.doi.org/10.3390/ijms24054557 |
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author | Kang, Geon Baek, Seung-Hak Kim, Young Ho Kim, Dong-Hyun Park, Ji Wan |
author_facet | Kang, Geon Baek, Seung-Hak Kim, Young Ho Kim, Dong-Hyun Park, Ji Wan |
author_sort | Kang, Geon |
collection | PubMed |
description | Recent deep learning algorithms have further improved risk classification capabilities. However, an appropriate feature selection method is required to overcome dimensionality issues in population-based genetic studies. In this Korean case–control study of nonsyndromic cleft lip with or without cleft palate (NSCL/P), we compared the predictive performance of models that were developed by using the genetic-algorithm-optimized neural networks ensemble (GANNE) technique with those models that were generated by eight conventional risk classification methods, including polygenic risk score (PRS), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and deep-learning-based artificial neural network (ANN). GANNE, which is capable of automatic input SNP selection, exhibited the highest predictive power, especially in the 10-SNP model (AUC of 88.2%), thus improving the AUC by 23% and 17% compared to PRS and ANN, respectively. Genes mapped with input SNPs that were selected by using a genetic algorithm (GA) were functionally validated for risks of developing NSCL/P in gene ontology and protein–protein interaction (PPI) network analyses. The IRF6 gene, which is most frequently selected via GA, was also a major hub gene in the PPI network. Genes such as RUNX2, MTHFR, PVRL1, TGFB3, and TBX22 significantly contributed to predicting NSCL/P risk. GANNE is an efficient disease risk classification method using a minimum optimal set of SNPs; however, further validation studies are needed to ensure the clinical utility of the model for predicting NSCL/P risk. |
format | Online Article Text |
id | pubmed-10003462 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100034622023-03-11 Genetic Risk Assessment of Nonsyndromic Cleft Lip with or without Cleft Palate by Linking Genetic Networks and Deep Learning Models Kang, Geon Baek, Seung-Hak Kim, Young Ho Kim, Dong-Hyun Park, Ji Wan Int J Mol Sci Article Recent deep learning algorithms have further improved risk classification capabilities. However, an appropriate feature selection method is required to overcome dimensionality issues in population-based genetic studies. In this Korean case–control study of nonsyndromic cleft lip with or without cleft palate (NSCL/P), we compared the predictive performance of models that were developed by using the genetic-algorithm-optimized neural networks ensemble (GANNE) technique with those models that were generated by eight conventional risk classification methods, including polygenic risk score (PRS), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and deep-learning-based artificial neural network (ANN). GANNE, which is capable of automatic input SNP selection, exhibited the highest predictive power, especially in the 10-SNP model (AUC of 88.2%), thus improving the AUC by 23% and 17% compared to PRS and ANN, respectively. Genes mapped with input SNPs that were selected by using a genetic algorithm (GA) were functionally validated for risks of developing NSCL/P in gene ontology and protein–protein interaction (PPI) network analyses. The IRF6 gene, which is most frequently selected via GA, was also a major hub gene in the PPI network. Genes such as RUNX2, MTHFR, PVRL1, TGFB3, and TBX22 significantly contributed to predicting NSCL/P risk. GANNE is an efficient disease risk classification method using a minimum optimal set of SNPs; however, further validation studies are needed to ensure the clinical utility of the model for predicting NSCL/P risk. MDPI 2023-02-25 /pmc/articles/PMC10003462/ /pubmed/36901988 http://dx.doi.org/10.3390/ijms24054557 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kang, Geon Baek, Seung-Hak Kim, Young Ho Kim, Dong-Hyun Park, Ji Wan Genetic Risk Assessment of Nonsyndromic Cleft Lip with or without Cleft Palate by Linking Genetic Networks and Deep Learning Models |
title | Genetic Risk Assessment of Nonsyndromic Cleft Lip with or without Cleft Palate by Linking Genetic Networks and Deep Learning Models |
title_full | Genetic Risk Assessment of Nonsyndromic Cleft Lip with or without Cleft Palate by Linking Genetic Networks and Deep Learning Models |
title_fullStr | Genetic Risk Assessment of Nonsyndromic Cleft Lip with or without Cleft Palate by Linking Genetic Networks and Deep Learning Models |
title_full_unstemmed | Genetic Risk Assessment of Nonsyndromic Cleft Lip with or without Cleft Palate by Linking Genetic Networks and Deep Learning Models |
title_short | Genetic Risk Assessment of Nonsyndromic Cleft Lip with or without Cleft Palate by Linking Genetic Networks and Deep Learning Models |
title_sort | genetic risk assessment of nonsyndromic cleft lip with or without cleft palate by linking genetic networks and deep learning models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10003462/ https://www.ncbi.nlm.nih.gov/pubmed/36901988 http://dx.doi.org/10.3390/ijms24054557 |
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