Identification of apoptosis-related gene signatures as potential biomarkers for differentiating active from latent tuberculosis via bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38312744", "kind": "science-record", "revision": 4, "content_hash": "sha256:4f855810a0d5daba1201aeddc411f8993440f359ac9b6870df25fd6e749f6696", "created_at": "2026-11-01T19:20:15Z", "updated_at": "2026-11-01T22:53:37Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of apoptosis-related gene signatures as potential biomarkers for differentiating active from latent tuberculosis via bioinformatics analysis.", "doi": "10.3389/fcimb.2024.1285493", "pmid": "38312744", "authors": ["Xiaoting Dai", "Litian Zhou", "Xiaopu He", "Jie Hua", "Liang Chen", "Yingying Lu"], "journal": "Frontiers in cellular and infection microbiology", "year": 2024, "abstract": "Background: Apoptosis is associated with the pathogenesis of Mycobacterium tuberculosis infection. This study aims to identify apoptosis-related genes as biomarkers for differentiating active tuberculosis (ATB) from latent tuberculosis infection (LTBI).\nMethods: The tuberculosis (TB) datasets (GSE19491, GSE62525, and GSE28623) were downloaded from the Gene Expression Omnibus (GEO) database. The diagnostic biomarkers differentiating ATB from LTBI were identified by combining the data of protein-protein interaction network, differentially expressed gene, Weighted Gene Co-Expression Network Analysis (WGCNA), and receiver operating characteristic (ROC) analyses. Machine learning algorithms were employed to validate the diagnostic ability of the biomarkers. Enrichment analysis for biomarkers was established, and potential drugs were predicted. The association between biomarkers and N6-methyladenosine (m6A) or 5-methylated cytosine (m5C) regulators was evaluated.\nResults: Six biomarkers including CASP1, TNFSF10, CASP4, CASP5, IFI16, and ATF3 were obtained for differentiating ATB from LTBI. They showed strong diagnostic performances, with area under ROC (AUC) values > 0.7. Enrichment analysis demonstrated that the biomarkers were involved in immune and inflammation responses. Furthermore, 24 drugs, including progesterone and emricasan, were predicted. The correlation analysis revealed that biomarkers were positively correlated with most m6A or m5C regulators.\nConclusion: The six ARGs can serve as effective biomarkers differentiating ATB from LTBI and provide insight into the pathogenesis of Mycobacterium tuberculosis infection.", "claims": [{"id": "claim-method", "statement": "The diagnostic ability of the biomarkers was assessed via the ROC, and validation sets were validated.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38312744", "locator": "sections[7] \"Assessment of the diagnostic value of biomarkers for ATB and qRT-PCR\" paragraph 2", "quote": "The diagnostic ability of the biomarkers was assessed via the ROC, and validation sets were validated."}], "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:38312744", "locator": "sections[21] \"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 six ARGs can serve as effective biomarkers differentiating ATB from LTBI and provide insight into the pathogenesis of Mycobacterium tuberculosis infection.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38312744", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "The six ARGs can serve as effective biomarkers differentiating ATB from LTBI and provide insight into the pathogenesis of Mycobacterium tuberculosis infection."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The six ARGs can serve as effective biomarkers differentiating ATB from LTBI and provide insight into the pathogenesis of Mycobacterium tuberculosis infection.", "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:38312744"], "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:GSE19491"], "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:GSE19491"], "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:38312744", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcimb.2024.1285493", "sha256": "sha256:ea39a8b74f0e049ff46ef971088167c815754af90134ac5e90d918933871a532", "bytes": 34498, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcimb.2024.1285493", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10834671", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE19491", "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=GSE19491", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE19491", "version": null, "note": "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 .", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "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 .", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM484368", "GSM484369", "GSM484370", "GSM484371", "GSM484372", "GSM484373", "GSM484374", "GSM484375", "GSM484376", "GSM484377", "GSM484378", "GSM484379", "GSM484380", "GSM484381", "GSM484382", "GSM484383", "GSM484384", "GSM484385", "GSM484386", "GSM484387", "GSM484388", "GSM484389", "GSM484390", "GSM484391", "GSM484392", "GSM484393", "GSM484394", "GSM484395", "GSM484396", "GSM484397", "GSM484398", "GSM484399", "GSM484400", "GSM484448", "GSM484449", "GSM484450", "GSM484451", "GSM484452", "GSM484453", "GSM484454", "GSM484455", "GSM484456", "GSM484457", "GSM484458", "GSM484459", "GSM484460", "GSM484461", "GSM484462", "GSM484463", "GSM484464", "GSM484465", "GSM484466", "GSM484467", "GSM484468", "GSM484469", "GSM484470", "GSM484471", "GSM484472", "GSM484473", "GSM484474", "GSM484475", "GSM484476", "GSM484477", "GSM484478", "GSM484479", "GSM484480", "GSM484481", "GSM484482", "GSM484483", "GSM484484", "GSM484485", "GSM484486", "GSM484487", "GSM484488", "GSM484489", "GSM484500", "GSM484501", "GSM484502", "GSM484503", "GSM484504", "GSM484505", "GSM484506", "GSM484507", "GSM484508", "GSM484509", "GSM484510", "GSM484511", "GSM484512", "GSM484513", "GSM484514", "GSM484515", "GSM484516", "GSM484517", "GSM484518", "GSM484519", "GSM484520", "GSM484521", "GSM484522", "GSM484523", "GSM484524", "GSM484525", "GSM484526", "GSM484527", "GSM484528", "GSM484529", "GSM484530", "GSM484531", "GSM484532", "GSM484533", "GSM484534", "GSM484535", "GSM484536", "GSM484537", "GSM484538", "GSM484539", "GSM484540", "GSM484541", "GSM484542", "GSM484543", "GSM484544", "GSM484545", "GSM484546", "GSM484547", "GSM484548", "GSM484549", "GSM484550", "GSM484551", "GSM484552", "GSM484553", "GSM484554", "GSM484555", "GSM484556", "GSM484557", "GSM484558", "GSM484559", "GSM484560", "GSM484561", "GSM484562", "GSM484563", "GSM484564", "GSM484565", "GSM484566", "GSM484567", "GSM484568", "GSM484569", "GSM484570", "GSM484571", "GSM484572", "GSM484573", "GSM484574", "GSM484575", "GSM484576", "GSM484577", "GSM484578", "GSM484579", "GSM484580", "GSM484581", "GSM484582", "GSM484583", "GSM484584", "GSM484585", "GSM484586", "GSM484587", "GSM484588", "GSM484589", "GSM484590", "GSM484591", "GSM484592", "GSM484593", "GSM484594", "GSM484595", "GSM484596", "GSM484597", "GSM484598", "GSM484599", "GSM484600", "GSM484601", "GSM484602", "GSM484603", "GSM484604", "GSM484605", "GSM484606", "GSM484607", "GSM484608", "GSM484609", "GSM484610", "GSM484611", "GSM484612", "GSM484613", "GSM484614", "GSM484615", "GSM484616", "GSM484617", "GSM484618", "GSM484619", "GSM484620", "GSM484621", "GSM484622", "GSM484623", "GSM484624", "GSM484625", "GSM484626", "GSM484627", "GSM484628", "GSM484629", "GSM484630", "GSM484631", "GSM484632", "GSM484633", "GSM484634", "GSM484635", "GSM484636", "GSM484637", "GSM484638", "GSM484639", "GSM484640", "GSM484641", "GSM484642", "GSM484643", "GSM484644", "GSM484645", "GSM484646", "GSM484647", "GSM484648", "GSM549324", "GSM549325", "GSM549326", "GSM549327", "GSM549328", "GSM549329", "GSM549331", "GSM549333", "GSM549335", "GSM549336", "GSM549338", "GSM549340", "GSM549342", "GSM549344", "GSM549345", "GSM549346", "GSM549347", "GSM549348", "GSM549349", "GSM549350", "GSM549351", "GSM549352", "GSM549353", "GSM549354", "GSM549355", "GSM549356", "GSM549357", "GSM549358", "GSM549359", "GSM549360", "GSM549361", "GSM549362", "GSM549363", "GSM549364", "GSM549365", "GSM549366", "GSM549367", "GSM549368", "GSM549369", "GSM549370", "GSM549371", "GSM549372", "GSM549373", "GSM549374", "GSM549375", "GSM549376", "GSM549377", "GSM549378", "GSM549379", "GSM549380", "GSM549381", "GSM549382", "GSM549383", "GSM549384", "GSM549385", "GSM549386", "GSM549387", "GSM549388", "GSM549389", "GSM549390", "GSM549391", "GSM549392", "GSM549393", "GSM549394", "GSM549395", "GSM549396", "GSM549397", "GSM549398", "GSM549399", "GSM549400", "GSM549401", "GSM549402", "GSM549403", "GSM549404", "GSM549405", "GSM549406", "GSM549407", "GSM549408", "GSM549409", "GSM549410", "GSM549411", "GSM549412", "GSM549413", "GSM549414", "GSM549415", "GSM549416", "GSM549417", "GSM549418", "GSM549419", "GSM549420", "GSM549421", "GSM549422", "GSM549423", "GSM549424", "GSM549425", "GSM549426", "GSM549427", "GSM549428", "GSM549429", "GSM549430", "GSM549431", "GSM549432", "GSM549433", "GSM549434", "GSM549435", "GSM549436", "GSM549437", "GSM549438", "GSM549439", "GSM549440", "GSM549441", "GSM549442", "GSM549443", "GSM549444", "GSM549445", "GSM549446", "GSM549447", "GSM549448", "GSM549449", "GSM549450", "GSM549451", "GSM549452", "GSM549453", "GSM549454", "GSM549455", "GSM549456", "GSM549457", "GSM549458", "GSM549459", "GSM549460", "GSM549461", "GSM549462", "GSM549463", "GSM549464", "GSM549465", "GSM549466", "GSM549467", "GSM549468", "GSM549469", "GSM549470", "GSM549471", "GSM549472", "GSM549473", "GSM549474", "GSM549475", "GSM549476", "GSM549477", "GSM549478", "GSM549479", "GSM549480", "GSM549481", "GSM549482", "GSM549483", "GSM549484", "GSM549485", "GSM549486", "GSM549487", "GSM549488", "GSM549489", "GSM549490", "GSM549491", "GSM549492", "GSM549493", "GSM549494", "GSM549495", "GSM549496", "GSM549497", "GSM549498", "GSM549499", "GSM549500", "GSM549501", "GSM549502", "GSM549503", "GSM549504", "GSM549505", "GSM549506", "GSM549507", "GSM549508", "GSM549509", "GSM549510", "GSM549511", "GSM549512", "GSM549513", "GSM549514", "GSM549515", "GSM549516", "GSM549517", "GSM549518", "GSM549519", "GSM549520", "GSM549521", "GSM549522", "GSM549523", "GSM550320", "GSM550321", "GSM550322", "GSM550323", "GSM550324", "GSM550325", "GSM550326", "GSM550327", "GSM550328", "GSM550329", "GSM550330", "GSM550331", "GSM550332", "GSM550333", "GSM550334", "GSM550335", "GSM550336", "GSM550337", "GSM550338", "GSM550339", "GSM550340", "GSM550341", "GSM550342", "GSM550343", "GSM550344", "GSM550345", "GSM550346", "GSM550347", "GSM550348", "GSM550349", "GSM550350", "GSM550351", "GSM550352", "GSM550353", "GSM550354", "GSM550355", "GSM550356", "GSM550357", "GSM550358", "GSM550359", "GSM550360", "GSM550361", "GSM550362", "GSM550363", "GSM550364", "GSM550365", "GSM550366", "GSM550367", "GSM550368", "GSM550369", "GSM550370", "GSM550371", "GSM550372", "GSM550373", "GSM550374", "GSM550375", "GSM550376", "GSM550377", "GSM550378", "GSM550379", "GSM550380", "GSM550381", "GSM550382", "GSM550383", "GSM550384", "GSM550385", "GSM550386", "GSM550387", "GSM550388", "GSM550389", "GSM550390", "GSM550391", "GSM550392", "GSM550393", "GSM550394", "GSM550395", "GSM550396", "GSM550397", "GSM550398", "GSM550399", "GSM550400"], "sample_count": 498, "group_sizes": null, "group_sizes_basis": null, "platform": "6947", "taxon": "Homo sapiens", "title": "Blood Transcriptional Profiles in Human Active and Latent Tuberculosis", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE28623", "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=GSE28623", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE28623", "version": null, "note": "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 .", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "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 .", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM709413", "GSM709414", "GSM709415", "GSM709416", "GSM709417", "GSM709418", "GSM709419", "GSM709420", "GSM709421", "GSM709422", "GSM709423", "GSM709424", "GSM709425", "GSM709426", "GSM709427", "GSM709428", "GSM709429", "GSM709430", "GSM709431", "GSM709432", "GSM709433", "GSM709434", "GSM709435", "GSM709436", "GSM709437", "GSM709438", "GSM709439", "GSM709440", "GSM709441", "GSM709442", "GSM709443", "GSM709444", "GSM709445", "GSM709446", "GSM709447", "GSM709448", "GSM709449", "GSM709450", "GSM709451", "GSM709452", "GSM709453", "GSM709454", "GSM709455", "GSM709456", "GSM709457", "GSM709458", "GSM709459", "GSM709460", "GSM709461", "GSM709462", "GSM709463", "GSM709464", "GSM709465", "GSM709466", "GSM709467", "GSM709468", "GSM709469", "GSM709470", "GSM709471", "GSM709472", "GSM709473", "GSM709474", "GSM709475", "GSM709476", "GSM709477", "GSM709478", "GSM709479", "GSM709480", "GSM709481", "GSM709482", "GSM709483", "GSM709484", "GSM709485", "GSM709486", "GSM709487", "GSM709488", "GSM709489", "GSM709490", "GSM709491", "GSM709492", "GSM709493", "GSM709494", "GSM709495", "GSM709496", "GSM709497", "GSM709498", "GSM709499", "GSM709500", "GSM709501", "GSM709502", "GSM709503", "GSM709504", "GSM709505", "GSM709506", "GSM709507", "GSM709508", "GSM709509", "GSM709510", "GSM709511", "GSM709512", "GSM709513", "GSM709514", "GSM709515", "GSM709516", "GSM709517", "GSM709518", "GSM709519", "GSM709520"], "sample_count": 108, "group_sizes": null, "group_sizes_basis": null, "platform": "4133", "taxon": "Homo sapiens", "title": "Pathway and functional association analysis of whole blood gene expression profiles reveal functional networks underlying TB pathogenesis. [Agilent-014850]", "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:GSE62525", "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=GSE62525", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE62525", "version": null, "note": "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 .", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "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 .", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1528336", "GSM1528337", "GSM1528338", "GSM1528339", "GSM1528340", "GSM1528341", "GSM1528342", "GSM1528343", "GSM1528344", "GSM1528345", "GSM1528346", "GSM1528347", "GSM1528348", "GSM1528349", "GSM1528350", "GSM1528351", "GSM1528352", "GSM1528353", "GSM1528354", "GSM1528355", "GSM1528356", "GSM1528357", "GSM1528358", "GSM1528359", "GSM1528360", "GSM1528361", "GSM1528362", "GSM1528363", "GSM1528364", "GSM1528365", "GSM1528366", "GSM1528367", "GSM1528368", "GSM1528369", "GSM1528370", "GSM1528371", "GSM1528372", "GSM1528373", "GSM1528374", "GSM1528375", "GSM1528376", "GSM1528377"], "sample_count": 42, "group_sizes": null, "group_sizes_basis": null, "platform": "16951", "taxon": "Homo sapiens", "title": "Gene expression profiling identifies candidate biomarkers for active and latent tuberculosis", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE19491 not downloaded", "asset:data:geo:GSE19491 licence not read", "asset:data:geo:GSE28623 not downloaded", "asset:data:geo:GSE28623 licence not read", "asset:data:geo:GSE62525 not downloaded", "asset:data:geo:GSE62525 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The diagnostic ability of the biomarkers was assessed via the ROC, and validation sets were validated."}}, {"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"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor", "method.machine_learning_random_forest"]}, "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": false, "required_operations": [], "missing_operations": ["machine_learning_random_forest"], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: random_forest."}, "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": ["GSE19491", "GSE62525", "GSE28623"], "resolved_accessions": ["GSE19491", "GSE62525", "GSE28623"], "statements": ["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 ."], "basis": "The paper names GSE19491, GSE62525, GSE28623 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": [], "assets": ["source:pmid:38312744", "data:geo:GSE19491"], "steps": []}, "must_build": {"dsl_operations": [{"operation": "machine_learning_random_forest", "unlocks_papers": null, "priority": null}], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE19491"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:07:34Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 10434, "completion_tokens": 2065, "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:38312744", "type": "paper", "label": "pmid:38312744"}, {"id": "source:pmid:38312744", "type": "manuscript", "label": "PMID 38312744 full text"}, {"id": "data:geo:GSE19491", "type": "dataset", "label": "GEO GSE19491"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:38312744", "type": "paper", "label": "pmid:38312744"}, {"id": "source:pmid:38312744", "type": "manuscript", "label": "PMID 38312744 full text"}, {"id": "data:geo:GSE19491", "type": "dataset", "label": "GEO GSE19491"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-38312744", "type": "extraction", "used": ["source:pmid:38312744"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE19491", "type": "accession_resolution", "used": ["data:geo:GSE19491"], "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:38312744", "parent_hash": "sha256:fdc282342f80c19ca9ef3b0a221c711fbf984d47c5c1a1e54d08f457a18713ec", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE19491 licence not read", "asset:data:geo:GSE19491 not downloaded", "asset:data:geo:GSE28623 licence not read", "asset:data:geo:GSE28623 not downloaded", "asset:data:geo:GSE62525 licence not read", "asset:data:geo:GSE62525 not downloaded", "execution.not_run", "method.machine_learning_random_forest", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:run_analysis blocked on method:machine_learning_random_forest"], "assays": [{"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["Identification of apoptosis-related gene signatures as potential biomark", "Background: Apoptosis is associated with the pathogenesis of Mycob", "losis infection. This study aims to identify apoptosis-related genes as biomarkers for differentiat"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The diagnostic ability of the biomarkers was assessed via the ROC, and validation sets were validated."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}