Integrating machine learning and neural networks for new diagnostic approaches to idiopathic pulmonary fibrosis and immune infiltration research.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40273141", "kind": "science-record", "revision": 3, "content_hash": "sha256:164a93285c39e9ac61c95a309475353a0a51b19111ffcd2a16e8534113caf88f", "created_at": "2026-11-01T20:30:25Z", "updated_at": "2026-11-01T22:13:56Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrating machine learning and neural networks for new diagnostic approaches to idiopathic pulmonary fibrosis and immune infiltration research.", "doi": "10.1371/journal.pone.0320242", "pmid": "40273141", "authors": ["Yali Guo", "Qian Jin", "Yi Kang", "Wenwen Jin", "Ying Liu", "Qian Chen", "Jian Liu", "Yu Guang Wang"], "journal": "PloS one", "year": 2025, "abstract": "BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease with a fatal outcome, known for its rapid progression and unpredictable clinical course. However, the tools available for diagnosing and treating IPF are quite limited. This study aims to identify and screen potential biomarkers for IPF diagnosis, thereby providing new diagnostic approaches.\nMETHODS: We choosed datasets from the Gene Expression Omnibus (GEO) database, including samples from both IPF patients and healthy controls. For the training set, we combined two gene array datasets (GSE24206 and GSE10667) and utilized GSE32537 as the test set. We identified differentially expressed genes (DEGs) between IPF and normal tissues and determined IPF-related modules using Weighted Gene Co-expression Network Analysis (WGCNA). Subsequently, we employed two machine learning strategies to screen potential diagnostic biomarkers. Candidate biomarkers were quantitatively evaluated using Receiver Operating Characteristic (ROC) curves to identify key diagnostic genes, followed by the construction of a nomogram. Further validation of the expression of these genes through transcriptomic sequencing data from IPF and normal group animal models. Next, we conducted immune infiltration analysis, single-gene Gene Set Enrichment Analysis (GSEA), and targeted drug prediction. Finally, we created an artificial neural network model specifically for IPF.\nRESULTS: We identified ASPN, COMP, and GPX8 as candidate biomarker genes for IPF, all of which exhibited Area Under the Curve (AUC) above 0.90. These genes were validated by RT-qPCR. Immune infiltration analysis revealed that specific immune cell types are closely related to IPF, suggesting that these immune cells may play a significant role in the pathogenesis of IPF.\nCONCLUSION: ASPN, COMP, and GPX8 have been identified as potential diagnostic genes for IPF, and the most relevant immune cell types have been determined. Our research results propose potential biomarkers for diagnosing IPF and present new pathways for investigating its pathogenesis and devising novel therapeutic approaches.", "claims": [{"id": "claim-method", "statement": "Utilizing two sophisticated machine learning algorithms, namely Lasso regression and the RF classifier, we endeavored to identify a panel of diagnostic biomarkers for IPF.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40273141", "locator": "sections[19] \"Identification of the diagnostic markers for IPF\" paragraph 2", "quote": "Utilizing two sophisticated machine learning algorithms, namely Lasso regression and the RF classifier, we endeavored to identify a panel of diagnostic biomarkers for IPF."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We utilized datasets GSE24206 and GSE10667 as train set, which were combined using the R package “ComBat” to eliminate batch effects [9]. These datasets comprise 21 control samples and 48 IPF samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40273141", "locator": "sections[3] \"Data acquisition and preprocessing\" paragraph 2", "quote": "We utilized datasets GSE24206 and GSE10667 as train set, which were combined using the R package “ComBat” to eliminate batch effects [9]. These datasets comprise 21 control samples and 48 IPF samples."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We identified ASPN, COMP, and GPX8 as candidate biomarker genes for IPF, all of which exhibited Area Under the Curve (AUC) above 0.90.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40273141", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We identified ASPN, COMP, and GPX8 as candidate biomarker genes for IPF, all of which exhibited Area Under the Curve (AUC) above 0.90."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "We identified ASPN, COMP, and GPX8 as candidate biomarker genes for IPF, all of which exhibited Area Under the Curve (AUC) above 0.90.", "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:40273141"], "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:GSE10667"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "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:GSE10667"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40273141", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1371/journal.pone.0320242", "sha256": "sha256:c3ade85fcae91f1e2737d1ccecf086fbe0d398c921b56177bc58964c5427d24a", "bytes": 37453, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1371/journal.pone.0320242", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12021136", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE10667", "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=GSE10667", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE10667", "version": null, "note": "We utilized datasets GSE24206 and GSE10667 as train set, which were combined using the R package “ComBat” to eliminate batch effects [9]. 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