Early prediction of alopecia areata using machine learning modeling of neuro stress immune signatures from multi datasets.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41454067", "kind": "science-record", "revision": 3, "content_hash": "sha256:c879c8124a320e9bb169b57d2a4f532a2f6a9ff8711d7e495a6c5c7d61511645", "created_at": "2026-11-01T20:31:26Z", "updated_at": "2026-11-01T22:15:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Early prediction of alopecia areata using machine learning modeling of neuro stress immune signatures from multi datasets.", "doi": "10.1038/s41598-025-33927-2", "pmid": "41454067", "authors": ["Anxin Chen", "Lin Shang", "Yingjiao Ju", "Fenglin Zhuo"], "journal": "Scientific reports", "year": 2025, "abstract": "Alopecia areata (AA) is an easy-recurring disease that presents huge challenges globally. An efficient clinical tool to predict AA onset would be valuable for timely intervention. We extracted six AA-related datasets from Gene Expression Omnibus (GEO). GO, KEGG, GSEA, GSVA and CIBERSORT algorithm were performed to elucidate the characteristics of AA. Feature genes were identified using LASSO regression and Random Forest algorithms. Five machine learning algorithms (Logistic Regression, K-nearest neighbors, Elastic Net, XGBoost and LightGBM) were employed to construct predictive models, with internal and external validation conducted to determine the optimal model. Additionally, SHapley Additive exPlanations (SHAP) analysis was applied to interpret the best-performing model and shiny framework was applied to establish an online predictive website. Five datasets (GSE45512, GSE68801, GSE80342, GSE58573, GSE74761) were integrated as train set and GSE148346 was defined as test set. Tissue regeneration and immune dysregulation were the key factors in AA pathogenesis. Three feature genes (KRT83, PPP1R1C, PIRT) were selected for model construction, with innate immune response, neural inflammatory and stress being a potential regulator for AA. The XGBoost model outperformed other algorithms, SHAP provided explanations for predictions and an online predictive website was established. Our study provides a potential \"neuro-stress-immune\" interplay insight into the pathogenesis of AA and establishes a clinically applicable predictive model for AA onset.", "claims": [{"id": "claim-method", "statement": "To rule out the case of overfitting, we conducted an internal five-fold cross-validation on the train set of the XGBoost model (mean AUC = 0.8819; SD = 0.0647) (Fig. 8g), which denoted that the model had good discrimination ability and maintained consistent performance across different data.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41454067", "locator": "sections[7] \"XGBoost model exhibited the best predictive performance\" paragraph 6", "quote": "To rule out the case of overfitting, we conducted an internal five-fold cross-validation on the train set of the XGBoost model (mean AUC = 0.8819; SD = 0.0647) (Fig. 8g), which denoted that the model had good discrimination ability and maintained consistent performance across different data."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Original datasets used to conduct this study could all be downloaded from GEO. The codes used in this study are available from GitHub (https://github.com/Ann0613xyz/Prediction-for-AA.git).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41454067", "locator": "sections[30] \"Data availability\" paragraph 2", "quote": "Original datasets used to conduct this study could all be downloaded from GEO. The codes used in this study are available from GitHub (https://github.com/Ann0613xyz/Prediction-for-AA.git)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The XGBoost model exhibited the most superior predictive performance, with internal five-fold cross-validation yielding a mean AUC of 0.8819 and SD of 0.0647.", "kind": "quantitative", "target": {"value": 0.8819, "unit": "auc", "tolerance": null, "source": "To rule out the case of overfitting, we conducted an internal five-fold cross-validation on the train set of the XGBoost model (mean AUC = 0.8819; SD = 0.0647) (Fig. 8g), which denoted that the model had good discrimination ability and maintained consistent performance across different data."}, "evidence": [{"asset": "source:pmid:41454067", "locator": "sections[7] \"XGBoost model exhibited the best predictive performance\" paragraph 4", "quote": "All in all, we finally ascertained that the XGBoost model exhibited the most superior performance."}, {"asset": "source:pmid:41454067", "locator": "sections[7] \"XGBoost model exhibited the best predictive performance\" paragraph 6", "quote": "To rule out the case of overfitting, we conducted an internal five-fold cross-validation on the train set of the XGBoost model (mean AUC = 0.8819; SD = 0.0647) (Fig. 8g), which denoted that the model had good discrimination ability and maintained consistent performance across different data."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The XGBoost model exhibited the most superior predictive performance, with internal five-fold cross-validation yielding a mean AUC of 0.8819 and SD of 0.0647.", "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:41454067"], "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. 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