{"doi":"10.1164/rccm.202403-0603ed","title":"The Analysis of Proteomics by Machine Learning in Separating Idiopathic Pulmonary Fibrosis from Connective Tissue Disease–Interstitial Lung Disease","abstract":"Machine learning (ML) is a branch of artificial intelligence in which a model or set of rules is derived on the basis of an initial training set.This model is then used to evaluate a new dataset (1).ML has made important contributions to the analysis of high-resolution computed tomography scans in predicting progression of idiopathic pulmonary fibrosis (IPF) (2, 3).The work by reported in this issue of the Journal has now extended the role of ML to proteomic analysis in pulmonary fibrosis, to enhance diagnostic ability and increase our understanding of the disease process of interstitial lung disease (ILD) (4).The objective of the present study was to identify proteins that separate and classify patients with connective tissue disease (CTD)-associated ILD from those with IPF.The study cohort was drawn from four registries-the Pulmonary Fibrosis Foundation Patient Registry at the University of Virginia, at the University of Chicago, and at the University of California, Davis (5), and the United Kingdom RECITAL (Rituximab versus Cyclophosphamide in Connective Tissue Disease-ILD) clinical trial (6)-providing both patients with IPF (n = 1,247) and those with CTD-ILD (n = 352), with matched proteomic and clinical data.Olink (Proteomics), an unsupervised proteomics platform, had an output 2,912 proteins between CTD-ILD and IPF.The model was derived from the Pulmonary Fibrosis Foundation as the training cohort.For the appropriate downstream analysis, patient numbers and gender for included diseases such as IPF, rheumatoid arthritis (RA)-associated ILD, and scleroderma-associated ILD had to be balanced against each other.This was done using a technique called random subsampling, in which subjects from the cohorts are randomly allocated but balanced for diagnosis and gender.Once balancing was complete, the authors developed their classifier, their model of the proteomic signature that differentiates CTD-ILD from IPF.They used recursive feature elimination (RFE).In this selection method, the weakest features are progressively removed in an iterative process until a specific number of strong(er) features is reached (7).In addition, RFE removes multicollinearity when two presumed independent variables (proteins) correlate with each other.RFE ranked 37 proteins as a single classifier between CTD-ILD and IPF.The 37-protein classifier was then subjected to several ML techniques, including support vector machine, which helps solves binary problems, in this case the proteomic separation of CTD-ILD","journal":"American Journal of Respiratory and Critical Care Medicine","year":2024,"id":496377,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9585,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":846726,"name":"Yuben Moodley","orcid":"0000-0002-0777-1196","position":0,"is_corresponding":true}],"reference_count":16,"raw_metadata":null,"created_at":"2026-07-19T02:09:27.134858Z","pmid":"38593003","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}