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Editorial
. 2024 Aug 15;210(4):378-380.
doi: 10.1164/rccm.202403-0603ED.

The Analysis of Proteomics by Machine Learning in Separating Idiopathic Pulmonary Fibrosis from Connective Tissue Disease-Interstitial Lung Disease

Yuben Moodley  1   2   3   4
Affiliations
Editorial

The Analysis of Proteomics by Machine Learning in Separating Idiopathic Pulmonary Fibrosis from Connective Tissue Disease-Interstitial Lung Disease

Yuben Moodley. Am J Respir Crit Care Med. .
No abstract available

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Figures

Figure 1.
Figure 1.
(A) The schematic of the study. The PFF cohort constituted the training set, which underwent random subsampling and recursive feature elimination to obtain a 37-protein signature. These 37 proteins were subjected to ML analysis from the UVA/Chicago classification cohorts and the RECITAL (Rituximab versus Cyclophosphamide in Connective Tissue Disease–ILD)/UC-Davis prediction cohorts. (B) Proportion of patients with different composite diagnosis scores (CDSs). Patients with CDSs of 0 or 1 were classified as CTD–ILD, those with CDSs of 2 were unclassified, and those with CDSs of 3 or 4 were classified as IPF. CTD = connective tissue disease; ILD = interstitial lung disease; IPF = idiopathic pulmonary fibrosis; LASSO = least absolute shrinkage selection operator; PFF = Pulmonary Fibrosis Foundation; RF = random forest; UC-Davis = University of California, Davis; UVA = University of Virginia.
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References

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