Construction of a neural network diagnostic model and investigation of immune infiltration characteristics for Crohn's disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36186439", "kind": "science-record", "revision": 5, "content_hash": "sha256:34761be162b2d7270e102a09fc5cdcb289c1dbc7adf32b4d1783eaeb62823af1", "created_at": "2026-11-01T18:15:28Z", "updated_at": "2026-11-01T22:50:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of a neural network diagnostic model and investigation of immune infiltration characteristics for Crohn's disease.", "doi": "10.3389/fgene.2022.976578", "pmid": "36186439", "authors": ["Yufei Yang", "Lijun Xu", "Yuqi Qiao", "Tianrong Wang", "Qing Zheng"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Objective: Crohn's disease (CD), a chronic recurrent illness, is a type of inflammatory bowel disease whose incidence and prevalence rates are gradually increasing. However, there is no universally accepted criterion for CD diagnosis. The aim of this study was to create a diagnostic prediction model for CD and identify immune cell infiltration features in CD. Methods: In this study, gene expression microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. Then, we identified differentially expressed genes (DEGs) between 178 CD and 38 control cases. Enrichment analysis of DEGs was also performed to explore the biological role of DEGs. Moreover, the \"randomForest\" package was applied to select core genes that were used to create a neural network model. Finally, in the training cohort, we used CIBERSORT to evaluate the immune landscape between the CD and normal groups. Results: The results of enrichment analysis revealed that these DEGs may be involved in biological processes associated with immunity and inflammatory responses. Moreover, the top 3 hub genes in the protein-protein interaction network were IL-1β, CCL2, and CXCR2. The diagnostic model allowed significant discrimination with an area under the ROC curve of 0.984 [95% confidence interval: 0.971-0.993]. A validation cohort (GSE36807) was utilized to ensure the reliability and applicability of the model. In addition, the immune infiltration analysis indicated nine different immune cell types were significantly different between the CD and healthy control groups. Conclusion: In summary, this study offers a novel insight into the diagnosis of CD and provides potential biomarkers for the precise treatment of CD.", "claims": [{"id": "claim-method", "statement": "We selected DEGs in the CD and control groups using the “limma” package in R software.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36186439", "locator": "sections[4] \"Screening of DEGs and enrichment analysis\" paragraph 2", "quote": "We selected DEGs in the CD and control groups using the “limma” package in R software."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary material, and further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36186439", "locator": "sections[19] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary material, and further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "According to the foregoing criteria, 102 DEGs were determined by using the “limma” package from 16,933 genes in the training set.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36186439", "locator": "sections[10] \"Identification of DEGs\" paragraph 2", "quote": "According to the foregoing criteria, 102 DEGs were determined by using the “limma” package from 16,933 genes in the training set (Figure 2A, Figure 2B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "According to the foregoing criteria, 102 DEGs were determined by using the “limma” package from 16,933 genes in the training set.", "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:36186439"], "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:GSE112366"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "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:GSE112366"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36186439", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.976578", "sha256": 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