Integrative multi-omics approach using random forest and artificial neural network models for early diagnosis and immune infiltration characterization in ischemic stroke.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39697434", "kind": "science-record", "revision": 3, "content_hash": "sha256:775ef981d4a2b137afad412a4cb39f68ceefc7c1cd625cd3aa5294c6970164c5", "created_at": "2026-11-01T20:30:00Z", "updated_at": "2026-11-01T22:13:22Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative multi-omics approach using random forest and artificial neural network models for early diagnosis and immune infiltration characterization in ischemic stroke.", "doi": "10.3389/fneur.2024.1475582", "pmid": "39697434", "authors": ["Ling Lin", "Chunmao Guo", "Hanna Jin", "Haixiong Huang", "Fan Luo", "Ying Wang", "Dongqi Li", "Yuanxin Zhang", "Yuqian Xu", "Chanyan Zhu", "Fengshan Zeng", "Huahua He", "Jie Chen", "Wei Zhang", "Wenlin Yu"], "journal": "Frontiers in neurology", "year": 2024, "abstract": "Background: Ischemic stroke (IS) is a significant global health issue, causing high rates of morbidity, mortality, and disability. Since conventional Diagnosis methods for IS have several shortcomings. It is critical to create new Diagnosis models in order to enhance existing Diagnosis approaches.\nMethods: We utilized gene expression data from the Gene Expression Omnibus (GEO) databases GSE16561 and GSE22255 to identify differentially expressed genes (DEGs) associated with IS. DEGs analysis using the Limma package, as well as GO and KEGG enrichment analyses, were performed. Furthermore, PPI networks were constructed using DEGs from the String database, and Random Forest models were utilized to screen key DEGs. Additionally, an artificial neural network model was developed for IS classification. Use the GSE58294 dataset to evaluate the effectiveness of the scoring model on healthy controls and ischemic stroke samples. The effectiveness of the scoring model was evaluated through AUC analysis, and CIBERSORT analysis was conducted to estimate the immune landscape and explore the correlation between gene expression and immune cell infiltration.\nResults: A total of 26 significant DEGs associated with IS were identified. Metascape analysis revealed enriched biological processes and pathways related to IS. 10 key DEGs (ARG1, DUSP1, F13A1, NFIL3, CCR7, ADM, PTGS2, ID3, FAIM3, HLA-DQB1) were selected using Random Forest and artificial neural network models. The area under the ROC curve (AUC) for the IS classification model was found to be near 1, indicating its high accuracy. Additionally, the analysis of the immune landscape demonstrated elevated immune-related networks in IS patients compared to healthy controls.\nConclusion: The study uncovers the involvement of specific genes and immune cells in the pathogenesis of IS, suggesting their importance in understanding and potentially targeting the disease.", "claims": [{"id": "claim-method", "statement": "Random Forest models were utilized to screen key DEGs. Additionally, an artificial neural network model was developed for IS classification.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39697434", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Random Forest models were utilized to screen key DEGs. Additionally, an artificial neural network model was developed for IS classification."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. 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:39697434", "locator": "sections[22] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. 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 area under the ROC curve (AUC) for the IS classification model was found to be near 1, indicating its high accuracy.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39697434", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The area under the ROC curve (AUC) for the IS classification model was found to be near 1, indicating its high accuracy."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The area under the ROC curve (AUC) for the IS classification model was found to be near 1, indicating its high accuracy.", "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:39697434"], "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:GSE16561"], "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:GSE16561"], "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:39697434", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fneur.2024.1475582", "sha256": "sha256:ca4a5d3a502a5f3e0cc84426bd49f0e43a0e3ae34821b0a1e666ad0710059929", "bytes": 50336, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fneur.2024.1475582", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11652371", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE16561", "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=GSE16561", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE16561", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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