Integrative bioinformatics and deep learning to identify common genetic pathways in Crohn's disease and ischemic cardiomyopathy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40854648", "kind": "science-record", "revision": 3, "content_hash": "sha256:4104ce6b3e00be2185a394bc719614a5b542d309c31b38dca2e72ac5b8c5e200", "created_at": "2026-11-01T20:30:57Z", "updated_at": "2026-11-01T22:14:37Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative bioinformatics and deep learning to identify common genetic pathways in Crohn's disease and ischemic cardiomyopathy.", "doi": "10.1016/j.jgeb.2025.100529", "pmid": "40854648", "authors": ["Reza Maddah", "Zahra Sadat Aghili", "Fahimeh Ghanbari", "Amirhossein Hajialiasgary Najafabadi", "Sedigheh Asgary"], "journal": "Journal, genetic engineering & biotechnology", "year": 2025, "abstract": "Crohn's disease (CD) and ischemic cardiomyopathy (ICM) share inflammatory characteristics, yet their common genetic underpinnings remain underexplored. Using an integrative bioinformatics approach, we analyzed GEO datasets (GSE3365 and GSE9128) to identify shared genetic pathways between CD and ICM. Through differential expression analysis, we identified 60 common differentially expressed genes (CDEGs). Functional enrichment analysis revealed enrichment in inflammatory pathways, including NF-κB and TNF-α signaling, highlighting their role in disease pathogenesis. We conducted microRNA (miRNA), transcription factor (TF), and protein-protein interaction (PPI) analyses to uncover regulatory networks. Notably, hsa-miR-98-5p emerged as a key miRNA, while RELA and NFKB1 were identified as prominent TFs interacting with CDEGs. Six hub genes-IL1B, CXCL8, CXCL2, TLR2, FCGR1A, and FCGR2A-were pinpointed, demonstrating high diagnostic potential via receiver operating characteristic (ROC) analysis. To advance diagnostic precision, we developed AutoClass, a deep learning framework that leverages hub gene regulatory networks to classify CD patients with approximately 95 % accuracy. These hub genes and regulators likely drive neutrophil-mediated inflammation, offering insights into the molecular interplay between CD and ICM. Our findings suggest that the identified CDEGs, miRNAs, and TFs hold promise as therapeutic targets and biomarkers, paving the way for precision medicine approaches in managing CD and its cardiovascular complications. Future experimental validation and cohort expansion could further elucidate these shared mechanisms, enhancing their translational impact.", "claims": [{"id": "claim-method", "statement": "The diagnostic performance of hub genes was assessed through ROC analysis in the primary datasets (GSE3365 and GSE9128) utilizing GraphPad Prism software (v8.0.1).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40854648", "locator": "sections[9] \"Receiver operating characteristic (ROC) curve analysis of hub genes\" paragraph 2", "quote": "The diagnostic performance of hub genes was assessed through ROC analysis in the primary datasets (GSE3365 and GSE9128) utilizing GraphPad Prism software (v8.0.1)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used in this study were obtained from a publicly available dataset. Data availability is corresponding author responsibility. The AutoClass model code and processed data are available in the following GitHub repository (https://github.com/AmirAsgary/AutoClass-Application-in-Crohn-s-Disease/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40854648", "locator": "sections[28] \"Code and Data availability\" paragraph 2", "quote": "The data used in this study were obtained from a publicly available dataset. Data availability is corresponding author responsibility. The AutoClass model code and processed data are available in the following GitHub repository (https://github.com/AmirAsgary/AutoClass-Application-in-Crohn-s-Disease/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "AutoClass, a deep learning framework leveraging hub gene regulatory networks, classified CD patients with approximately 95% accuracy.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "To advance diagnostic precision, we developed AutoClass, a deep learning framework that leverages hub gene regulatory networks to classify CD patients with approximately 95 % accuracy."}, "evidence": [{"asset": "source:pmid:40854648", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "To advance diagnostic precision, we developed AutoClass, a deep learning framework that leverages hub gene regulatory networks to classify CD patients with approximately 95 % accuracy."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "AutoClass, a deep learning framework leveraging hub gene regulatory networks, classified CD patients with approximately 95% 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:40854648"], "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:GSE3365"], "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:GSE3365"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40854648", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.jgeb.2025.100529", "sha256": "sha256:b35165b418e7da91a3f9f12d00b014477cd8ab8bbd7cb8b0814fdde1cc821974", "bytes": 54954, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.jgeb.2025.100529", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12268078", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE3365", "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=GSE3365", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE3365", "version": null, "note": "The data used in this study were obtained from a publicly available dataset. Data availability is corresponding author responsibility. The AutoClass model code and processed data are available in the following GitHub repository (https://github.com/AmirAsgary/AutoClass-Application-in-Crohn-s-Disease/).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data used in this study were obtained from a publicly available dataset. Data availability is corresponding author responsibility. The AutoClass model code and processed data are available in the following GitHub repository (https://github.com/AmirAsgary/AutoClass-Application-in-Crohn-s-Disease/).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM76030", "GSM76031", "GSM76032", "GSM76033", "GSM76034", "GSM76035", "GSM76036", "GSM76037", "GSM76038", "GSM76039", "GSM76040", "GSM76041", "GSM76042", "GSM76043", "GSM76044", "GSM76045", "GSM76046", "GSM76047", "GSM76048", "GSM76049", "GSM76050", "GSM76051", "GSM76052", "GSM76053", "GSM76054", "GSM76055", "GSM76056", "GSM76057", "GSM76058", "GSM76059", "GSM76060", "GSM76061", "GSM76062", "GSM76063", "GSM76064", "GSM76065", "GSM76066", "GSM76067", "GSM76068", "GSM76069", "GSM76070", "GSM76071", "GSM76072", "GSM76073", "GSM76074", "GSM76075", "GSM76076", "GSM76077", "GSM76078", "GSM76079", "GSM76080", "GSM76081", "GSM76082", "GSM76083", "GSM76084", "GSM76085", "GSM76086", "GSM76087", "GSM76088", "GSM76089", "GSM76090", "GSM76091", "GSM76092", "GSM76093", "GSM76094", "GSM76095", "GSM76096", "GSM76097", "GSM76098", "GSM76099", "GSM76100", "GSM76101", "GSM76102", "GSM76103", "GSM76104", "GSM76105", "GSM76106", "GSM76107", "GSM76108", "GSM76109", "GSM76110", "GSM76111", "GSM76112", "GSM76113", "GSM76114", "GSM76115", "GSM76116", "GSM76117", "GSM76118", "GSM76119", "GSM76120", "GSM76121", "GSM76122", "GSM76123", "GSM76124", "GSM76125", "GSM76126", "GSM76127", "GSM76128", "GSM76129", "GSM76130", "GSM76131", "GSM76132", "GSM76133", "GSM76134", "GSM76135", "GSM76136", "GSM76137", "GSM76138", "GSM76139", "GSM76140", "GSM76141", "GSM76142", "GSM76143", "GSM76144", "GSM76145", "GSM76146", "GSM76147", "GSM76148", "GSM76149", "GSM76150", "GSM76151", "GSM76152", "GSM76153", "GSM76154", "GSM76155", "GSM76156"], "sample_count": 127, "group_sizes": null, "group_sizes_basis": null, "platform": "96", "taxon": "Homo sapiens", "title": "Comparison of PBMCs in Inflammatory Bowel Disease", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE9128", "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=GSE9128", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE9128", "version": null, "note": "The data used in this study were obtained from a publicly available dataset. Data availability is corresponding author responsibility. The AutoClass model code and processed data are available in the following GitHub repository (https://github.com/AmirAsgary/AutoClass-Application-in-Crohn-s-Disease/).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data used in this study were obtained from a publicly available dataset. Data availability is corresponding author responsibility. The AutoClass model code and processed data are available in the following GitHub repository (https://github.com/AmirAsgary/AutoClass-Application-in-Crohn-s-Disease/).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM231003", "GSM231004", "GSM231005", "GSM231006", "GSM231007", "GSM231008", "GSM231009", "GSM231010", "GSM231011", "GSM231012", "GSM231013"], "sample_count": 11, "group_sizes": null, "group_sizes_basis": null, "platform": "96", "taxon": "Homo sapiens", "title": "Expression data from heart failure vs control peripheral blood mononuclear cells.", "resolution_checked_at": "2026-11-01T18:06:44Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE3365 not downloaded", "asset:data:geo:GSE3365 licence not read", "asset:data:geo:GSE9128 not downloaded", "asset:data:geo:GSE9128 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.8, "band": "high", "quote": "LIMMA"}}, {"id": "method-2", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "protein-protein interaction"}}, {"id": "method-3", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "pathway enrichment"}}, {"id": "method-4", "family": "diagnostic_evaluation", "specific": "roc", "label": "Diagnostic test evaluation", "dsl_operations": ["confusion_counts", "roc_curve", "sensitivity_specificity", "solve_confusion_matrix", "classification_metrics", "published_precision_envelope"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["reference_standard_and_test"], "evidence": {"score": 0.8, "band": "high", "quote": "ROC analysis"}}, {"id": "method-5", "family": null, "specific": null, "label": "receiver operating characteristic (ROC) curve analysis", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The diagnostic performance of hub genes was assessed through ROC analysis in the primary datasets (GSE3365 and GSE9128) utilizing GraphPad Prism software (v8.0.1)."}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "build_graph", "expected_value": 95.0, "unit": "per cent", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none."}], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE3365", "GSE9128"], "resolved_accessions": ["GSE3365", "GSE9128"], "statements": ["The data used in this study were obtained from a publicly available dataset. Data availability is corresponding author responsibility. The AutoClass model code and processed data are available in the following GitHub repository (https://github.com/AmirAsgary/AutoClass-Application-in-Crohn-s-Disease/)."], "basis": "The paper names GSE3365, GSE9128 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:40854648", "data:geo:GSE3365"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE3365"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T20:00:14Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 14808, "completion_tokens": 1044, "cached_tokens": 256}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:40854648", "type": "paper", "label": "pmid:40854648"}, {"id": "source:pmid:40854648", "type": "manuscript", "label": "PMID 40854648 full text"}, {"id": "data:geo:GSE3365", "type": "dataset", "label": "GEO GSE3365"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:40854648", "type": "paper", "label": "pmid:40854648"}, {"id": "source:pmid:40854648", "type": "manuscript", "label": "PMID 40854648 full text"}, {"id": "data:geo:GSE3365", "type": "dataset", "label": "GEO GSE3365"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-40854648", "type": "extraction", "used": ["source:pmid:40854648"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE3365", "type": "accession_resolution", "used": ["data:geo:GSE3365"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:40854648", "parent_hash": "sha256:9c03e50b7aada6fe2fe554189b9a87d6d91efd1ed9b963b13d8515050de5d981", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE3365 licence not read", "asset:data:geo:GSE3365 not downloaded", "asset:data:geo:GSE9128 licence not read", "asset:data:geo:GSE9128 not downloaded", "execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["atasets and DEG screening A linear model for microarray data (LIMMA) package in the R software was p", "tially expressed genes (DEGs) for each given microarray dataset. The screening criteria for DEGs wer"], "order_basis": "paper_text_order:1843", "id": "assay:1"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.8, "evidence": ["LIMMA"], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}