Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis
Background: Periodontitis is a chronic inflammatory disease leading to tooth loss in severe cases, and early diagnosis is essential for periodontitis prevention. This study aimed to construct a diagnostic model for periodontitis using a random forest algorithm and an artificial neural network (ANN)....
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.1041524/full |
| _version_ | 1811180179792330752 |
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| author | Junwei Xiang Wenkai Huang Yaodong He Yunshan Li Yuanyin Wang Ran Chen |
| author_facet | Junwei Xiang Wenkai Huang Yaodong He Yunshan Li Yuanyin Wang Ran Chen |
| author_sort | Junwei Xiang |
| collection | DOAJ |
| description | Background: Periodontitis is a chronic inflammatory disease leading to tooth loss in severe cases, and early diagnosis is essential for periodontitis prevention. This study aimed to construct a diagnostic model for periodontitis using a random forest algorithm and an artificial neural network (ANN).Methods: Gene expression data of two large cohorts of patients with periodontitis, GSE10334 and GSE16134, were downloaded from the Gene Expression Omnibus database. We screened for differentially expressed genes in the GSE10334 cohort, identified key periodontitis biomarkers using a Random Forest algorithm, and constructed a classification artificial neural network model, using receiver operating characteristic curves to evaluate its diagnostic utility. Furthermore, patients with periodontitis were classified using a consensus clustering algorithm. The immune infiltration landscape was assessed using CIBERSOFT and single-sample Gene Set Enrichment Analysis.Results: A total of 153 differentially expressed genes were identified, of which 42 were downregulated. We utilized 13 key biomarkers to establish a periodontitis diagnostic model. The model had good predictive performance, with an area under the receiver operative characteristic curve (AUC) of 0.945. The independent cohort (GSE16134) was used to further validate the model’s accuracy, showing an area under the receiver operative characteristic curve of 0.900. The proportion of plasma cells was highest in samples from patients with period ontitis, and 13 biomarkers were closely related to immunity. Two molecular subgroups were defined in periodontitis, with one cluster suggesting elevated levels of immune infiltration and immune function.Conclusion: We successfully identified key biomarkers of periodontitis using machine learning and developed a satisfactory diagnostic model. Our model may provide a valuable reference for the prevention and early detection of periodontitis. |
| first_indexed | 2024-04-11T06:47:03Z |
| format | Article |
| id | doaj.art-6ec1982c49344bbfbc0fb6d9c0379afc |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-11T06:47:03Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-6ec1982c49344bbfbc0fb6d9c0379afc2022-12-22T04:39:20ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-11-011310.3389/fgene.2022.10415241041524Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitisJunwei XiangWenkai HuangYaodong HeYunshan LiYuanyin WangRan ChenBackground: Periodontitis is a chronic inflammatory disease leading to tooth loss in severe cases, and early diagnosis is essential for periodontitis prevention. This study aimed to construct a diagnostic model for periodontitis using a random forest algorithm and an artificial neural network (ANN).Methods: Gene expression data of two large cohorts of patients with periodontitis, GSE10334 and GSE16134, were downloaded from the Gene Expression Omnibus database. We screened for differentially expressed genes in the GSE10334 cohort, identified key periodontitis biomarkers using a Random Forest algorithm, and constructed a classification artificial neural network model, using receiver operating characteristic curves to evaluate its diagnostic utility. Furthermore, patients with periodontitis were classified using a consensus clustering algorithm. The immune infiltration landscape was assessed using CIBERSOFT and single-sample Gene Set Enrichment Analysis.Results: A total of 153 differentially expressed genes were identified, of which 42 were downregulated. We utilized 13 key biomarkers to establish a periodontitis diagnostic model. The model had good predictive performance, with an area under the receiver operative characteristic curve (AUC) of 0.945. The independent cohort (GSE16134) was used to further validate the model’s accuracy, showing an area under the receiver operative characteristic curve of 0.900. The proportion of plasma cells was highest in samples from patients with period ontitis, and 13 biomarkers were closely related to immunity. Two molecular subgroups were defined in periodontitis, with one cluster suggesting elevated levels of immune infiltration and immune function.Conclusion: We successfully identified key biomarkers of periodontitis using machine learning and developed a satisfactory diagnostic model. Our model may provide a valuable reference for the prevention and early detection of periodontitis.https://www.frontiersin.org/articles/10.3389/fgene.2022.1041524/fullperiodontitisneural networksmachine learninggene expressionbiomarkers |
| spellingShingle | Junwei Xiang Wenkai Huang Yaodong He Yunshan Li Yuanyin Wang Ran Chen Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis Frontiers in Genetics periodontitis neural networks machine learning gene expression biomarkers |
| title | Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis |
| title_full | Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis |
| title_fullStr | Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis |
| title_full_unstemmed | Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis |
| title_short | Construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis |
| title_sort | construction of artificial neural network diagnostic model and analysis of immune infiltration for periodontitis |
| topic | periodontitis neural networks machine learning gene expression biomarkers |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.1041524/full |
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