ArticleScientific reports2023
Artificial neural network identified the significant genes to distinguish Idiopathic pulmonary fibrosis.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 14 citations in OpenAlex.
- ADAMTS14 is a novel modulator of fibroblast mechanoactivation in pulmonary fibrosis.American journal of respiratory and critical care medicine · 2026Article
- Exposure-informed lung transcriptomic analysis links predicted NNK targets to cell type-specific remodeling programs in idiopathic pulmonary fibrosis.Functional & integrative genomics · 2026Article
- Identification of diagnostic and prognostic phospholipid biomarkers in idiopathic pulmonary fibrosis via machine learning and in vivo validation.Human genomics · 2025Article
- A deep generative model for deciphering cellular dynamics and in silico drug discovery in complex diseases.Nature biomedical engineering · 2025Article
- Genome-wide CRISPR/Cas9 screening identifies key profibrotic regulators of TGF-β1-induced epithelial-mesenchymal transformation and pulmonary fibrosis.Frontiers in molecular biosciences · 2025Article
- Matrix-free human lung organoids derived from induced pluripotent stem cells to model lung injury.Stem cell research & therapy · 2024Article
- Construction of an artificial neural network diagnostic model and investigation of immune cell infiltration characteristics for idiopathic pulmonary fibrosis.BMC pulmonary medicine · 2024Article
- Single-cell RNA sequencing reveals special basal cells and fibroblasts in idiopathic pulmonary fibrosis.Scientific reports · 2024Article
- Identifying a survival-associated cell type based on multi-level transcriptome analysis in idiopathic pulmonary fibrosis.Respiratory research · 2024Article
- Implicating the cholecystokinin B receptor in liver stem cell oncogenesis.American journal of physiology. Gastrointestinal and liver physiology · 2024Article
- Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases.Diagnostics (Basel, Switzerland) · 2023Review
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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