Constructing an artificial neural network model.

Constructing an artificial neural network model.

<p>(A) ANN model for IPF. (B) The training group verifies the ROC curve findings. (C) The testing group verifies the ROC curve findings.</p>

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Bibliographic Details
Main Author:	Yali Guo (1686325) (author)
Other Authors:	Qian Jin (112422) (author), Yi Kang (387134) (author), Wenwen Jin (9201734) (author), Ying Liu (18461) (author), Qian Chen (18883) (author), Jian Liu (33711) (author), Yu guang Wang (21175289) (author)
Published:	2025
Subjects:	Medicine Cell Biology Genetics Molecular Biology Biotechnology Immunology Cancer Infectious Diseases Virology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified unpredictable clinical course transcriptomic sequencing data targeted drug prediction present new pathways new diagnostic approaches interstitial lung disease integrating machine learning idiopathic pulmonary fibrosis screen potential biomarkers potential diagnostic genes gene expression omnibus expression network analysis candidate biomarker genes xlink "> aspn xlink "> candidate biomarkers wgcna ). utilized gse32537 training set tools available test set study aims significant role rapid progression quite limited normal tissues neural networks including samples identified aspn healthy controls gsea ), fatal outcome exhibited area closely related choosed datasets
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Summary:	<p>(A) ANN model for IPF. (B) The training group verifies the ROC curve findings. (C) The testing group verifies the ROC curve findings.</p>

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