Development and validation of immune-based biomarkers and deep learning models for Alzheimer's disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36072674", "kind": "science-record", "revision": 4, "content_hash": "sha256:7b6ae0add4e2fd50740ac44b6bafb99ae5a23b2d10a4b0bebfcd46967e2de90f", "created_at": "2026-11-01T18:15:17Z", "updated_at": "2026-11-01T22:07:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and validation of immune-based biomarkers and deep learning models for Alzheimer's disease.", "doi": "10.3389/fgene.2022.968598", "pmid": "36072674", "authors": ["Yijie He", "Lin Cong", "Qinfei He", "Nianping Feng", "Yun Wu"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: Alzheimer's disease (AD) is the most common form of dementia in old age and poses a severe threat to the health and life of the elderly. However, traditional diagnostic methods and the ATN diagnostic framework have limitations in clinical practice. Developing novel biomarkers and diagnostic models is necessary to complement existing diagnostic procedures. Methods: The AD expression profile dataset GSE63060 was downloaded from the NCBI GEO public database for preprocessing. AD-related differentially expressed genes were screened using a weighted co-expression network and differential expression analysis, and functional enrichment analysis was performed. Subsequently, we screened hub genes by random forest, analyzed the correlation between hub genes and immune cells using ssGSEA, and finally built an AD diagnostic model using an artificial neural network and validated it. Results: Based on the random forest algorithm, we screened a total of seven hub genes from AD-related DEGs, based on which we confirmed that hub genes play an essential role in the immune microenvironment and successfully established a novel diagnostic model for AD using artificial neural networks, and validated its effectiveness in the publicly available datasets GSE63060 and GSE97760. Conclusion: Our study establishes a reliable model for screening and diagnosing AD that provides a theoretical basis for adding diagnostic biomarkers for the AD gene.", "claims": [{"id": "claim-method", "statement": "Random forest models for differentially expressed genes were constructed using the randomForest package (version 4.6–14).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36072674", "locator": "sections[8] \"Screening for hub genes using random forest\" paragraph 2", "quote": "Random forest models for differentially expressed genes were constructed using the randomForest package (version 4.6–14)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36072674", "locator": "sections[22] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material"}], "state": "complete"}, {"id": "claim-target-result", "statement": "The random forest algorithm screened seven hub genes from AD-related DEGs, which were used to establish an artificial-neural-network diagnostic model for AD that was validated in GSE63060 and GSE97760.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36072674", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Based on the random forest algorithm, we screened a total of seven hub genes from AD-related DEGs, based on which we confirmed that hub genes play an essential role in the immune microenvironment and successfully established a novel diagnostic model for AD using artificial neural networks, and validated its effectiveness in the publicly available datasets GSE63060 and GSE97760."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The random forest algorithm screened seven hub genes from AD-related DEGs, which were used to establish an artificial-neural-network diagnostic model for AD that was validated in GSE63060 and GSE97760.", "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:36072674"], "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:GSE63060"], "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:GSE63060"], "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:36072674", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.968598", "sha256": "sha256:7f809e35137c7876a2b46cf05899c7ed53ed05ad9c22b571c2920ad2cc73b986", "bytes": 48833, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2022.968598", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9441688", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE63060", "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=GSE63060", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE63060", "version": null, "note": "Publicly available datasets were analyzed in this study. 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