Establishment and analysis of a novel diagnostic model for systemic juvenile idiopathic arthritis based on machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38243323", "kind": "science-record", "revision": 4, "content_hash": "sha256:02879f02de5fd2fc90108f935dc18905d894b48931fd5d2769f9a2ae67c8c060", "created_at": "2026-11-01T19:20:09Z", "updated_at": "2026-11-01T22:53:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Establishment and analysis of a novel diagnostic model for systemic juvenile idiopathic arthritis based on machine learning.", "doi": "10.1186/s12969-023-00949-x", "pmid": "38243323", "authors": ["Pan Ding", "Yi Du", "Xinyue Jiang", "Huajian Chen", "Li Huang"], "journal": "Pediatric rheumatology online journal", "year": 2024, "abstract": "BACKGROUND: Systemic juvenile idiopathic arthritis (SJIA) is a form of childhood arthritis with clinical features such as fever, lymphadenopathy, arthritis, rash, and serositis. It seriously affects the growth and development of children and has a high rate of disability and mortality. SJIA may result from genetic, infectious, or autoimmune factors since the precise source of the disease is unknown. Our study aims to develop a genetic-based diagnostic model to explore the identification of SJIA at the genetic level.\nMETHODS: The gene expression dataset of peripheral blood mononuclear cell (PBMC) samples from SJIA was collected from the Gene Expression Omnibus (GEO) database. Then, three GEO datasets (GSE11907-GPL96, GSE8650-GPL96 and GSE13501) were merged and used as a training dataset, which included 125 SJIA samples and 92 health samples. GSE7753 was used as a validation dataset. The limma method was used to screen differentially expressed genes (DEGs). Feature selection was performed using Lasso, random forest (RF)-recursive feature elimination (RFE) and RF classifier.\nRESULTS: We finally identified 4 key genes (ALDH1A1, CEACAM1, YBX3 and SLC6A8) that were essential to distinguish SJIA from healthy samples. And we combined the 4 key genes and performed a grid search as well as 10-fold cross-validation with 5 repetitions to finally identify the RF model with optimal mtry. The mean area under the curve (AUC) value for 5-fold cross-validation was greater than 0.95. The model's performance was then assessed once more using the validation dataset, and an AUC value of 0.990 was obtained. All of the above AUC values demonstrated the strong robustness of the SJIA diagnostic model.\nCONCLUSIONS: We successfully developed a new SJIA diagnostic model that can be used for a novel aid in the identification of SJIA. In addition, the identification of 4 key genes that may serve as potential biomarkers for SJIA provides new insights to further understand the mechanisms of SJIA.", "claims": [{"id": "claim-method", "statement": "To screen out DEGs, we performed differential expression analysis in the training dataset using the traditional Bayesian approach of the R package limma (version 3.50.1).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38243323", "locator": "sections[4] \"Screening for differentially expressed genes (DEGs)\" paragraph 2", "quote": "To screen out DEGs, we performed differential expression analysis in the training dataset using the traditional Bayesian approach of the R package limma (version 3.50.1)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets supporting the conclusions of this article are available in the GEO (https://www.ncbi.nlm.nih.gov/geo/). GEO accession numbers: GSE13501, GSE11907, GSE20307, GSE21521, GSE7753, GSE8650, GSE6269 and GSE207633.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38243323", "locator": "sections[29] \"notes\" paragraph 1", "quote": "The datasets supporting the conclusions of this article are available in the GEO (https://www.ncbi.nlm.nih.gov/geo/). GEO accession numbers: GSE13501, GSE11907, GSE20307, GSE21521, GSE7753, GSE8650, GSE6269 and GSE207633."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study developed a four-gene random-forest diagnostic model for distinguishing SJIA from healthy samples, achieving an external-validation AUC of 0.990.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38243323", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We finally identified 4 key genes (ALDH1A1, CEACAM1, YBX3 and SLC6A8) that were essential to distinguish SJIA from healthy samples."}, {"asset": "source:pmid:38243323", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The model’s performance was then assessed once more using the validation dataset, and an AUC value of 0.990 was obtained."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study developed a four-gene random-forest diagnostic model for distinguishing SJIA from healthy samples, achieving an external-validation AUC of 0.990.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:38243323"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE13501"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE13501"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38243323", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12969-023-00949-x", "sha256": "sha256:13267010f02e968f53c1eb11433201c8d032bb84f5ff65447e208bb8faa3b936", "bytes": 42650, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12969-023-00949-x", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10797915", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE13501", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13501", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE13501", "version": null, "note": "The datasets supporting the conclusions of this article are available in the GEO (https://www.ncbi.nlm.nih.gov/geo/). 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