Artificial neural network identified the significant genes to distinguish Idiopathic pulmonary fibrosis
Abstract Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease that causes irreversible damage to lung tissue characterized by excessive deposition of extracellular matrix (ECM) and remodeling of lung parenchyma. The current diagnosis of IPF is complex and usually completed...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-28536-w |
| _version_ | 1797945974688055296 |
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| author | Zhongzheng Li Shenghui Wang Huabin Zhao Peishuo Yan Hongmei Yuan Mengxia Zhao Ruyan Wan Guoying Yu Lan Wang |
| author_facet | Zhongzheng Li Shenghui Wang Huabin Zhao Peishuo Yan Hongmei Yuan Mengxia Zhao Ruyan Wan Guoying Yu Lan Wang |
| author_sort | Zhongzheng Li |
| collection | DOAJ |
| description | Abstract Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease that causes irreversible damage to lung tissue characterized by excessive deposition of extracellular matrix (ECM) and remodeling of lung parenchyma. The current diagnosis of IPF is complex and usually completed by a multidisciplinary team including clinicians, radiologists and pathologists they work together and make decision for an effective treatment, it is imperative to introduce novel practical methods for IPF diagnosis. This study provided a new diagnostic model of idiopathic pulmonary fibrosis based on machine learning. Six genes including CDH3, DIO2, ADAMTS14, HS6ST2, IL13RA2, and IGFL2 were identified based on the differentially expressed genes in IPF patients compare to healthy subjects through a random forest classifier with the existing gene expression databases. An artificial neural network model was constructed for IPF diagnosis based these genes, and this model was validated by the distinctive public datasets with a satisfactory diagnostic accuracy. These six genes identified were significant correlated with lung function, and among them, CDH3 and DIO2 were further determined to be significantly associated with the survival. Putting together, artificial neural network model identified the significant genes to distinguish idiopathic pulmonary fibrosis from healthy people and it is potential for molecular diagnosis of IPF. |
| first_indexed | 2024-04-10T21:03:32Z |
| format | Article |
| id | doaj.art-c50a12c7de0a48dd9a65669a2ac30ca2 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-10T21:03:32Z |
| publishDate | 2023-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-c50a12c7de0a48dd9a65669a2ac30ca22023-01-22T12:10:42ZengNature PortfolioScientific Reports2045-23222023-01-0113111510.1038/s41598-023-28536-wArtificial neural network identified the significant genes to distinguish Idiopathic pulmonary fibrosisZhongzheng Li0Shenghui Wang1Huabin Zhao2Peishuo Yan3Hongmei Yuan4Mengxia Zhao5Ruyan Wan6Guoying Yu7Lan Wang8State Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan Center for Outstanding Overseas Scientists of Pulmonary Fibrosis, College of Life Science, Henan Normal UniversityState Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan Center for Outstanding Overseas Scientists of Pulmonary Fibrosis, College of Life Science, Henan Normal UniversityState Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan Center for Outstanding Overseas Scientists of Pulmonary Fibrosis, College of Life Science, Henan Normal UniversityState Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan Center for Outstanding Overseas Scientists of Pulmonary Fibrosis, College of Life Science, Henan Normal UniversityState Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan Center for Outstanding Overseas Scientists of Pulmonary Fibrosis, College of Life Science, Henan Normal UniversityState Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan Center for Outstanding Overseas Scientists of Pulmonary Fibrosis, College of Life Science, Henan Normal UniversityState Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan Center for Outstanding Overseas Scientists of Pulmonary Fibrosis, College of Life Science, Henan Normal UniversityState Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan Center for Outstanding Overseas Scientists of Pulmonary Fibrosis, College of Life Science, Henan Normal UniversityState Key Laboratory of Cell Differentiation and Regulation, Henan International Joint Laboratory of Pulmonary Fibrosis, Henan Center for Outstanding Overseas Scientists of Pulmonary Fibrosis, College of Life Science, Henan Normal UniversityAbstract Idiopathic pulmonary fibrosis (IPF) is a progressive interstitial lung disease that causes irreversible damage to lung tissue characterized by excessive deposition of extracellular matrix (ECM) and remodeling of lung parenchyma. The current diagnosis of IPF is complex and usually completed by a multidisciplinary team including clinicians, radiologists and pathologists they work together and make decision for an effective treatment, it is imperative to introduce novel practical methods for IPF diagnosis. This study provided a new diagnostic model of idiopathic pulmonary fibrosis based on machine learning. Six genes including CDH3, DIO2, ADAMTS14, HS6ST2, IL13RA2, and IGFL2 were identified based on the differentially expressed genes in IPF patients compare to healthy subjects through a random forest classifier with the existing gene expression databases. An artificial neural network model was constructed for IPF diagnosis based these genes, and this model was validated by the distinctive public datasets with a satisfactory diagnostic accuracy. These six genes identified were significant correlated with lung function, and among them, CDH3 and DIO2 were further determined to be significantly associated with the survival. Putting together, artificial neural network model identified the significant genes to distinguish idiopathic pulmonary fibrosis from healthy people and it is potential for molecular diagnosis of IPF.https://doi.org/10.1038/s41598-023-28536-w |
| spellingShingle | Zhongzheng Li Shenghui Wang Huabin Zhao Peishuo Yan Hongmei Yuan Mengxia Zhao Ruyan Wan Guoying Yu Lan Wang Artificial neural network identified the significant genes to distinguish Idiopathic pulmonary fibrosis Scientific Reports |
| title | Artificial neural network identified the significant genes to distinguish Idiopathic pulmonary fibrosis |
| title_full | Artificial neural network identified the significant genes to distinguish Idiopathic pulmonary fibrosis |
| title_fullStr | Artificial neural network identified the significant genes to distinguish Idiopathic pulmonary fibrosis |
| title_full_unstemmed | Artificial neural network identified the significant genes to distinguish Idiopathic pulmonary fibrosis |
| title_short | Artificial neural network identified the significant genes to distinguish Idiopathic pulmonary fibrosis |
| title_sort | artificial neural network identified the significant genes to distinguish idiopathic pulmonary fibrosis |
| url | https://doi.org/10.1038/s41598-023-28536-w |
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