A Methylation Diagnostic Model Based on Random Forests and Neural Networks for Asthma Identification.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36213574", "kind": "science-record", "revision": 4, "content_hash": "sha256:7071889ef1874304146ddb0731f8342524d170170c46fb9e39aafd6777d737df", "created_at": "2026-11-01T18:15:33Z", "updated_at": "2026-11-01T22:07:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Methylation Diagnostic Model Based on Random Forests and Neural Networks for Asthma Identification.", "doi": "10.1155/2022/2679050", "pmid": "36213574", "authors": ["Dong-Dong Li", "Ting Chen", "You-Liang Ling", "YongAn Jiang", "Qiu-Gen Li"], "journal": "Computational and mathematical methods in medicine", "year": 2022, "abstract": "Background: Asthma significantly impacts human life and health as a chronic disease. Traditional treatments for asthma have several limitations. Artificial intelligence aids in cancer treatment and may also accelerate our understanding of asthma mechanisms. We aimed to develop a new clinical diagnosis model for asthma using artificial neural networks (ANN).\nMethods: Datasets (GSE85566, GSE40576, and GSE13716) were downloaded from Gene Expression Omnibus (GEO) and identified differentially expressed CpGs (DECs) enriched by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Random forest (RF) and ANN algorithms further identified gene characteristics and built clinical models. In addition, two external validation datasets (GSE40576 and GSE137716) were used to validate the diagnostic ability of the model.\nResults: The methylation analysis tool (ChAMP) considered DECs that were up-regulated (n =121) and down-regulated (n =20). GO results showed enrichment of actin cytoskeleton organization and cell-substrate adhesion, shigellosis, and serotonergic synapses. RF (random forest) analysis identified 10 crucial DECs (cg05075579, cg20434422, cg03907390, cg00712106, cg05696969, cg22862094, cg11733958, cg00328720, and cg13570822). ANN constructed the clinical model according to 10 DECs. In two external validation datasets (GSE40576 and GSE137716), the Area Under Curve (AUC) for GSE137716 was 1.000, and AUC for GSE40576 was 0.950, confirming the reliability of the model.\nConclusion: Our findings provide new methylation markers and clinical diagnostic models for asthma diagnosis and treatment.", "claims": [{"id": "claim-method", "statement": "Random forest (RF) and ANN algorithms further identified gene characteristics and built clinical models.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36213574", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Random forest (RF) and ANN algorithms further identified gene characteristics and built clinical models."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data of this study were downloaded and compiled from the GEO database (https://www.ncbi.nlm.nih.gov/gds/?term=); data used to support the results of this study were obtained from the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36213574", "locator": "sections[17] \"Data Availability\" paragraph 2", "quote": "The data of this study were downloaded and compiled from the GEO database (https://www.ncbi.nlm.nih.gov/gds/?term=); data used to support the results of this study were obtained from the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The ANN model achieved an AUC of 1.000 in GSE137716 and 0.950 in GSE40576, confirming the reliability of the model.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36213574", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "In two external validation datasets (GSE40576 and GSE137716), the Area Under Curve (AUC) for GSE137716 was 1.000, and AUC for GSE40576 was 0.950, confirming the reliability of the model."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The ANN model achieved an AUC of 1.000 in GSE137716 and 0.950 in GSE40576, confirming the reliability of the model.", "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:36213574"], "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:GSE13716"], "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": {"auc": "ristic (ROC) curves and area under the curve (AUC)"}, "inputs": ["data:geo:GSE13716"], "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:36213574", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/2679050", "sha256": "sha256:f2e75e12952c4c59001006393d5564d3644aa321663533ba5f0a9e58dee1a7fa", "bytes": 28331, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/2022/2679050", "version": null, "note": "Full article fetched from PMC; 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