Artificial neural network identified the significant genes to distinguish Idiopathic pulmonary fibrosis

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 mult...

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Bibliographic Details
Published in:Scientific reports 2023-01, Vol.13 (1), p.1225-1225, Article 1225
Main Authors: Li, Zhongzheng, Wang, Shenghui, Zhao, Huabin, Yan, Peishuo, Yuan, Hongmei, Zhao, Mengxia, Wan, Ruyan, Yu, Guoying, Wang, Lan
Format: Article
Language:English
Subjects:
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631/114/1305
631/114/2164
Decision making
Diagnosis
Extracellular matrix
Fibrosis
Gene expression
Humanities and Social Sciences
Humans
Idiopathic Pulmonary Fibrosis - diagnosis
Idiopathic Pulmonary Fibrosis - genetics
Interferon
Lung
Lung diseases
Lung Diseases, Interstitial
multidisciplinary
Neural networks
Parenchyma
Pulmonary fibrosis
Respiratory function
Science
Science (multidisciplinary)
Sulfotransferases
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-28536-w