{"data":{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40070840", "kind": "science-record", "revision": 4, "content_hash": "sha256:d611f6a76454690c1b01664ecd68db4f411a2bb2b0e6c9cc4502f2ebdfa575b4", "created_at": "2026-11-01T20:30:17Z", "updated_at": "2026-11-01T22:56:13Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis.", "doi": "10.3389/fimmu.2025.1549150", "pmid": "40070840", "authors": ["Hang Chen", "Biao Wu", "Kunyu Guan", "Liang Chen", "Kangjie Chai", "Maoji Ying", "Dazhi Li", "Weicheng Zhao"], "journal": "Frontiers in immunology", "year": 2025, "abstract": "Background: Atherosclerosis is a significant contributor to cardiovascular disease, and conventional diagnostic methods frequently fall short in the timely and accurate detection of early-stage atherosclerosis. Abnormal lipid metabolism plays a critical role in the development of atherosclerosis. Consequently, the identification of new diagnostic markers is essential for the precise diagnosis of this condition.\nMethod: The datasets related to atherosclerosis utilized in this research were obtained from the GEO database (GSE2470, GSE24495, GSE100927 and GSE43292). The ssGSEA technique was first utilized to assess lipid metabolism scores in samples affected by atherosclerosis, thereby aiding in the discovery of important regulatory genes linked to lipid metabolism via WGCNA. Following this, differential expression analysis and functional evaluations were carried out, after which various machine learning approaches were employed to determine significant diagnostic genes for atherosclerosis. A diagnostic model was then developed and validated through several machine learning algorithms. Furthermore, molecular docking studies were conducted to analyze the binding affinity of these key markers with therapeutic agents for atherosclerosis. The ssGSEA technique was also used to measure immune cell scores in atherosclerotic samples, aiding the exploration of the connection between key diagnostic markers and immune cells. Finally, the expression variations of the identified pivotal genes were confirmed through experimental validation.\nResult: WGCNA identified 302 lipid metabolism-related genes in atherosclerotic samples, and functional analysis revealed that these genes are associated with multiple immune pathways. Through further differential analysis and screening using machine learning algorithms, APLNR, PCDH12, PODXL, SLC40A1, TM4SF18, and TNFRSF25 were identified as key diagnostic genes for atherosclerosis. The diagnostic model we constructed was confirmed to predict the occurrence of atherosclerosis with high accuracy, and molecular docking studies indicated that these six key diagnostic genes have potential as drug targets. Additionally, the ssGSEA algorithm further validated the association of these diagnostic genes with various immune cells. Finally, the expression levels of these six genes were experimentally confirmed.\nConclusion: Our study introduces novel lipid metabolism-related diagnostic markers for atherosclerosis and emphasizes their potential as immune-related drug targets. This research provides a valuable approach for the predictive diagnosis and targeted therapy of atherosclerosis.", "claims": [{"id": "claim-method", "statement": "Out of various algorithm combinations, XGBoost proved to be the most effective for constructing the model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40070840", "locator": "sections[15] \"Integration of machine learning algorithms for diagnostic model development\" paragraph 2", "quote": "Out of various algorithm combinations, XGBoost proved to be the most effective for constructing the model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40070840", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The area under the curve (AUC) for the training data from GSE100927 was recorded at 0.982, while the AUC for the validation dataset GSE43292 stood at 0.836.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40070840", "locator": "sections[15] \"Integration of machine learning algorithms for diagnostic model development\" paragraph 2", "quote": "The area under the curve (AUC) for the training data from GSE100927 was recorded at 0.982, while the AUC for the validation dataset GSE43292 stood at 0.836."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The area under the curve (AUC) for the training data from GSE100927 was recorded at 0.982, while the AUC for the validation dataset GSE43292 stood at 0.836.", "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:40070840"], "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:GSE100927"], "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:GSE100927"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40070840", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2025.1549150", "sha256": "sha256:b7f8875436d28c2e0d859a21a9a0a47668c621da80a2fec0db8714f319b1814e", "bytes": 40378, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2025.1549150", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11893410", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE100927", "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=GSE100927", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE100927", "version": null, "note": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2696609", "GSM2696610", "GSM2696611", "GSM2696612", "GSM2696613", "GSM2696614", "GSM2696615", "GSM2696616", "GSM2696617", "GSM2696618", "GSM2696619", "GSM2696620", "GSM2696621", "GSM2696622", "GSM2696623", "GSM2696624", "GSM2696625", "GSM2696626", "GSM2696627", "GSM2696628", "GSM2696629", "GSM2696630", "GSM2696631", "GSM2696632", "GSM2696633", "GSM2696634", "GSM2696635", "GSM2696636", "GSM2696637", "GSM2696638", "GSM2696639", "GSM2696640", "GSM2696641", "GSM2696642", "GSM2696643", "GSM2696644", "GSM2696645", "GSM2696646", "GSM2696647", "GSM2696648", "GSM2696649", "GSM2696650", "GSM2696651", "GSM2696652", "GSM2696653", "GSM2696654", "GSM2696655", "GSM2696656", "GSM2696657", "GSM2696658", "GSM2696659", "GSM2696660", "GSM2696661", "GSM2696662", "GSM2696663", "GSM2696664", "GSM2696665", "GSM2696666", "GSM2696667", "GSM2696668", "GSM2696669", "GSM2696670", "GSM2696671", "GSM2696672", "GSM2696673", "GSM2696674", "GSM2696675", "GSM2696676", "GSM2696677", "GSM2696678", "GSM2696679", "GSM2696680", "GSM2696681", "GSM2696682", "GSM2696683", "GSM2696684", "GSM2696685", "GSM2696686", "GSM2696687", "GSM2696688", "GSM2696689", "GSM2696690", "GSM2696691", "GSM2696692", "GSM2696693", "GSM2696694", "GSM2696695", "GSM2696696", "GSM2696697", "GSM2696698", "GSM2696699", "GSM2696700", "GSM2696701", "GSM2696702", "GSM2696703", "GSM2696704", "GSM2696705", "GSM2696706", "GSM2696707", "GSM2696708", "GSM2696709", "GSM2696710", "GSM2696711", "GSM2696712"], "sample_count": 104, "group_sizes": null, "group_sizes_basis": null, "platform": "17077", "taxon": "Homo sapiens", "title": "Atherosclerotic and control peripheral artery gene expression", "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:GSE24495", "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=GSE24495", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE24495", "version": null, "note": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM603107", "GSM603108", "GSM603109", "GSM603110", "GSM603111", "GSM603112", "GSM603113", "GSM603114", "GSM603115", "GSM603116", "GSM603117", "GSM603118", "GSM603119", "GSM603120", "GSM603121", "GSM603122", "GSM603123", "GSM603124", "GSM603125", "GSM603126", "GSM603127", "GSM603128", "GSM603129", "GSM603130", "GSM603131", "GSM603132", "GSM603133", "GSM603134", "GSM603135", "GSM603136", "GSM603137", "GSM603138", "GSM603139", "GSM603140", "GSM603141", "GSM603142", "GSM603143", "GSM603144", "GSM603145", "GSM603146", "GSM603147", "GSM603148", "GSM603149", "GSM603150", "GSM603151", "GSM603152", "GSM603153", "GSM603154", "GSM603155", "GSM603156", "GSM603157", "GSM603158", "GSM603159", "GSM603160", "GSM603161", "GSM603162", "GSM603163", "GSM603164", "GSM603165", "GSM603166", "GSM603167", "GSM603168", "GSM603169", "GSM603170", "GSM603171", "GSM603172", "GSM603173", "GSM603174", "GSM603175", "GSM603176", "GSM603177", "GSM603178", "GSM603179", "GSM603180", "GSM603181", "GSM603182", "GSM603183", "GSM603184", "GSM603185", "GSM603186", "GSM603187", "GSM603188", "GSM603189", "GSM603190", "GSM603191", "GSM603192", "GSM603193", "GSM603194", "GSM603195", "GSM603196", "GSM603197", "GSM603198", "GSM603199", "GSM603200", "GSM603201", "GSM603202", "GSM603203", "GSM603204", "GSM603205", "GSM603206", "GSM603207", "GSM603208", "GSM603209", "GSM603210", "GSM603211", "GSM603212", "GSM603213", "GSM603214", "GSM603215", "GSM603216", "GSM603217", "GSM603218", "GSM603219"], "sample_count": 113, "group_sizes": null, "group_sizes_basis": null, "platform": "10687", "taxon": "Homo sapiens", "title": "Gene expression profiling of human atherosclerotic plaque: Carotid plaque", "resolution_checked_at": "2026-11-01T20:16:08Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE24702", "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=GSE24702", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE24702", "version": null, "note": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM607104", "GSM607105", "GSM607106", "GSM607107", "GSM607108", "GSM607109", "GSM607110", "GSM607111", "GSM607112", "GSM607113", "GSM607114", "GSM607115", "GSM607116", "GSM607117", "GSM607118", "GSM607119", "GSM607120", "GSM607121", "GSM607122", "GSM607123", "GSM607124", "GSM607125", "GSM607126", "GSM607127", "GSM607128", "GSM607129", "GSM607130", "GSM607131", "GSM607132", "GSM607133", "GSM607134", "GSM607135", "GSM607136", "GSM607137", "GSM607138", "GSM607139", "GSM607140", "GSM607141", "GSM607142", "GSM607143", "GSM607144", "GSM607145", "GSM607146", "GSM607147", "GSM607148", "GSM607149", "GSM607150", "GSM607151", "GSM607152", "GSM607153", "GSM607154", "GSM607155", "GSM607156", "GSM607157", "GSM607158", "GSM607159", "GSM607160", "GSM607161", "GSM607162", "GSM607163", "GSM607164", "GSM607165", "GSM607166", "GSM607167", "GSM607168", "GSM607169", "GSM607170", "GSM607171", "GSM607172", "GSM607173", "GSM607174", "GSM607175", "GSM607176", "GSM607177", "GSM607178", "GSM607179", "GSM607180", "GSM607181", "GSM607182", "GSM607183", "GSM607184", "GSM607185", "GSM607186", "GSM607187", "GSM607188", "GSM607189", "GSM607190", "GSM607191", "GSM607192", "GSM607193", "GSM607194", "GSM607195", "GSM607196", "GSM607197", "GSM607198", "GSM607199", "GSM607200", "GSM607201", "GSM607202", "GSM607203", "GSM607204", "GSM607205", "GSM607206", "GSM607207", "GSM607208", "GSM607209", "GSM607210", "GSM607211", "GSM607212", "GSM607213", "GSM607214", "GSM607215", "GSM607216", "GSM607217", "GSM607218", "GSM607219", "GSM607220", "GSM607221", "GSM607222", "GSM607223", "GSM607224", "GSM607225", "GSM607226", "GSM607227", "GSM607228", "GSM607229", "GSM607230", "GSM607231", "GSM607232", "GSM607233", "GSM607234", "GSM607235", "GSM607236", "GSM607237", "GSM607238", "GSM607239", "GSM607240", "GSM607241", "GSM607242", "GSM607243", "GSM607244", "GSM607245", "GSM607246", "GSM607247", "GSM607248", "GSM607249", "GSM607250", "GSM607251", "GSM607252", "GSM607253", "GSM607254", "GSM607255", "GSM607256", "GSM607257", "GSM607258", "GSM607259", "GSM607260", "GSM607261", "GSM607262", "GSM607263", "GSM607264", "GSM607265", "GSM607266", "GSM607267", "GSM607268", "GSM607269", "GSM607270", "GSM607271", "GSM607272", "GSM607273", "GSM607274", "GSM607275", "GSM607276", "GSM607277", "GSM607278", "GSM607279", "GSM607280", "GSM607281", "GSM607282", "GSM607283", "GSM607284", "GSM607285", "GSM607286", "GSM607287", "GSM607288", "GSM607289", "GSM607290", "GSM607291", "GSM607292", "GSM607293", "GSM607294", "GSM607295", "GSM607296", "GSM607297", "GSM607298", "GSM607299", "GSM607300", "GSM607301", "GSM607302", "GSM607303", "GSM607304", "GSM607305", "GSM607306", "GSM607307", "GSM607308", "GSM607309", "GSM607310", "GSM607311", "GSM607312", "GSM607313", "GSM607314", "GSM607315", "GSM607316", "GSM607317", "GSM607318", "GSM607319", "GSM607320", "GSM607321", "GSM607322", "GSM607323", "GSM607324", "GSM607325", "GSM607326", "GSM607327", "GSM607328", "GSM607329", "GSM607330", "GSM607331", "GSM607332", "GSM607333", "GSM607334", "GSM607335", "GSM607336", "GSM607337", "GSM607338", "GSM607339", "GSM607340", "GSM607341", "GSM607342", "GSM607343", "GSM607344", "GSM607345", "GSM607346", "GSM607347", "GSM607348", "GSM607349", "GSM607350", "GSM607351", "GSM607352", "GSM607353", "GSM607354", "GSM607355", "GSM607356", "GSM607357", "GSM607358", "GSM607359", "GSM607360", "GSM607361", "GSM607362", "GSM607363", "GSM607364", "GSM607365", "GSM607366", "GSM607367", "GSM607368", "GSM607369", "GSM607370", "GSM607371", "GSM607372", "GSM607373", "GSM607374", "GSM607375", "GSM607376", "GSM607377", "GSM607378", "GSM607379", "GSM607380", "GSM607381", "GSM607382", "GSM607383", "GSM607384", "GSM607385", "GSM607386", "GSM607387", "GSM607388", "GSM607389", "GSM607390", "GSM607391", "GSM607392", "GSM607393"], "sample_count": 290, "group_sizes": null, "group_sizes_basis": null, "platform": "10687", "taxon": "Homo sapiens", "title": "Gene expression profiling of human atherosclerotic plaque: 290 peripheral plaques", "resolution_checked_at": "2026-11-01T20:16:08Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE43292", "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=GSE43292", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE43292", "version": null, "note": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1060117", "GSM1060118", "GSM1060119", "GSM1060120", "GSM1060121", "GSM1060122", "GSM1060123", "GSM1060124", "GSM1060125", "GSM1060126", "GSM1060127", "GSM1060128", "GSM1060129", "GSM1060130", "GSM1060131", "GSM1060132", "GSM1060133", "GSM1060134", "GSM1060135", "GSM1060136", "GSM1060137", "GSM1060138", "GSM1060139", "GSM1060140", "GSM1060141", "GSM1060142", "GSM1060143", "GSM1060144", "GSM1060145", "GSM1060146", "GSM1060147", "GSM1060148", "GSM1060149", "GSM1060150", "GSM1060151", "GSM1060152", "GSM1060153", "GSM1060154", "GSM1060155", "GSM1060156", "GSM1060157", "GSM1060158", "GSM1060159", "GSM1060160", "GSM1060161", "GSM1060162", "GSM1060163", "GSM1060164", "GSM1060165", "GSM1060166", "GSM1060167", "GSM1060168", "GSM1060169", "GSM1060170", "GSM1060171", "GSM1060172", "GSM1060173", "GSM1060174", "GSM1060175", "GSM1060176", "GSM1060177", "GSM1060178", "GSM1060179", "GSM1060180"], "sample_count": 64, "group_sizes": null, "group_sizes_basis": null, "platform": "6244", "taxon": "Homo sapiens", "title": "Genome-wide expression study of human carotid atheroma", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE100927 not downloaded", "asset:data:geo:GSE100927 licence not read", "asset:data:geo:GSE24495 not downloaded", "asset:data:geo:GSE24495 licence not read", "asset:data:geo:GSE24702 not downloaded", "asset:data:geo:GSE24702 licence not read", "asset:data:geo:GSE43292 not downloaded", "asset:data:geo:GSE43292 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "machine_learning", "specific": "gradient_boosting", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Out of various algorithm combinations, XGBoost proved to be the most effective for constructing the model."}}, {"id": "method-2", "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 curve"}}], "targets": [], "gaps": []}, "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": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: gradient_boosting."}, "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": ["GSE24702", "GSE24495", "GSE100927", "GSE43292"], "resolved_accessions": ["GSE24702", "GSE24495", "GSE100927", "GSE43292"], "statements": ["The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors."], "basis": "The paper names GSE24702, GSE24495, GSE100927, GSE43292 in GEO database and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "assets": ["source:pmid:40070840", "data:geo:GSE100927"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE100927"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:48:11Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 10096, "completion_tokens": 1994, "cached_tokens": 128}}, "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:40070840", "type": "paper", "label": "pmid:40070840"}, {"id": "source:pmid:40070840", "type": "manuscript", "label": "PMID 40070840 full text"}, {"id": "data:geo:GSE100927", "type": "dataset", "label": "GEO GSE100927"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:40070840", "type": "paper", "label": "pmid:40070840"}, {"id": "source:pmid:40070840", "type": "manuscript", "label": "PMID 40070840 full text"}, {"id": "data:geo:GSE100927", "type": "dataset", "label": "GEO GSE100927"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-40070840", "type": "extraction", "used": ["source:pmid:40070840"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE100927", "type": "accession_resolution", "used": ["data:geo:GSE100927"], "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:40070840", "parent_hash": "sha256:f97401dd9e58eba7f233b4ec571e52ef9db743949fac6e817c0b35df6109de74", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE100927 licence not read", "asset:data:geo:GSE100927 not downloaded", "asset:data:geo:GSE24495 licence not read", "asset:data:geo:GSE24495 not downloaded", "asset:data:geo:GSE24702 licence not read", "asset:data:geo:GSE24702 not downloaded", "asset:data:geo:GSE43292 licence not read", "asset:data:geo:GSE43292 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["train_classifier", "cross_validate", "roc_auc"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Out of various algorithm combinations, XGBoost proved to be the most effective for constructing the model."], "order_basis": "analysis_only", "id": "assay:1"}]},"meta":{"api_version":"v1","automation_ladder_snapshot":"ba34b49490d9bfaf","automation_ladder_caveats":["Method extraction is rule-based and has no hand-judged accuracy sample. Evidence bands order textual support; method agreement intervals are null (n=0), never probabilities. Work-type validation does not validate methods.","Operation coverage describes only explicitly named methods. It does not prove a complete protocol, compatible parameters, successful compilation or re-execution. Unrecognised or ambiguous methods require review.","Data reachability is a textual accession or public-data statement. No dataset was downloaded or checked for access, completeness or licence.","Capabilities are planning states seeded from the dated equipment register. No bench ownership, completed quote or partner booking is inferred. Procurement counts are dependencies, not guaranteed replications or financial returns.","Tier and blocker counts are deterministic assessments of this corpus, not measured agreement. R3 is reserved for procurement; unresolved methods and data require human work (R4). The most restrictive axis wins, with capability, method, data as tie-breaks. Resolving one blocker can leave another at the same tier."],"engine":"corpus_engine","engine_provider":"rule_based_v1+lex99e1195c7d6f","run_id":"run-20261023T122449232913","source":"esearch","source_detail":"PubMed mirror biomed_pubmed (read-only)","run_status":"complete","imported_at":"2026-11-03T20:16:52+00:00","generated_at":"2026-11-04T01:24:09+00:00","total_papers":5529,"total_assays_tracked":29,"automatable_assay_families":6,"validated":true,"caveat_count":9,"blocking_caveats":5,"caveats":[{"code":"low_evidence_dominates","basis":["4,152 of 5,529 classifications (75.1%) matched no cue evidence and sit at the 0.05 floor.","34 of 230 data_only calls (14.8%) are in that group."],"claim":"Work type and confidence bands","gloss":"Most records were classified from their title and framing alone, with assay-cue coverage at zero.","detail":"Most classifications in this run rest on no matched evidence at all. At the evidence floor the work type comes from title framing and a fixed prior, not from anything in the paper, so the published work-type split should be read as a rough triage signal rather than a measured distribution. The impact is bounded but not removed: since 2026-10-23 the engine excludes floor-confidence records from the shortlist entirely and publishes them separately as held out, so the recommendation is unaffected even though the census is.","status":"unverified","severity":"high"},{"code":"runtime_is_modelled","basis":["Runtime is summed from published per-assay hour figures. Timing awaits the first executed replication.","4,407 of 5,529 records (79.7%) model to 0.00 hours because no assay was matched, which the ranking then reads as \"cheapest\".","0 of 10 shortlisted records carry a 0.00-hour estimate."],"claim":"Estimated replication runtime","gloss":"The hour figures are estimates built from assay mentions; timing awaits the first executed replication.","detail":"The hour figures are a cost model derived from the bench steps implied by each matched assay family. The first executed replication will supply measured timing. Records without an assay match currently receive a zero-hour model value.","status":"unverified","severity":"high"},{"code":"signal_score_without_citations","basis":["5,529 of 5,529 records (100.0%) have no citation count from the source metadata."],"claim":"Signal score","gloss":"The signal score uses publication-type weight because the mirror supplied zero citation counts.","detail":"The source supplied no citation counts for any record in this run, so the signal score is publication-type weight only. The citation term of the ranking is inert here, and the shortlist is ordered by publication type alone.","status":"unverified","severity":"high"},{"code":"abstract_absent","basis":["1,667 of 5,529 records (30.2%) carry no abstract at all."],"claim":"Evidence base for classification","gloss":"Some records contain a title and publication type only, defining the available classification evidence.","detail":"Where the mirror contains a title and publication types only, those fields define the classifier evidence. A methods section is required for fuller automated and human review.","status":"unverified","severity":"medium"},{"code":"classifier_agreement_measured","basis":["Published two-way decision agreement 77.7% (310 of 399 decidable records) from a stratified hand-check of 438 real records.","Three-way diagnostic agreement 73.9% (288 of 390 decidable records).","data_only agreement 75.2% (100 of 133 decidable).","48 record(s) could not be decided from title and abstract and were excluded from the denominator.","56 analysis-only records were published under another work type, so the data_only share is not an upper bound."],"claim":"Published automatable decision","gloss":"This figure has been checked against a human reading the same papers, so we know how often it is right.","detail":"The published automatable / not-automatable distribution comes from a rule-based classifier that was hand-checked against a fixed ground-truth definition. The agreement rates below are measured, not estimated. The three-way work type remains a diagnostic and is reported separately.","status":"measured","severity":"high"},{"code":"llm_comparison_not_measured","basis":["rule_based_v1+lex99e1195c7d6f produced 5,529 of 5,529 classifications.","Measured on 438 labelled records; these are RULE-BASED figures, not model results."],"claim":"Rule-based versus LLM agreement","gloss":"Every number here is the rule engine’s own. Model comparison begins when a working API credential is connected and measured.","detail":"The published two-way decision agreed with a human reading on 310 of 399 decidable records (77.7%). The diagnostic three-way label agreed on 288 of 390 decidable records (73.9%). Every percentage in this run belongs to the rule engine. Model comparison begins when a working API credential is connected and measured.","status":"not_measured","severity":"high"},{"code":"confidence_is_a_band","basis":["low band: 135 of 210 hand-judged records were correct, 64.3% [57.6, 70.5] (95% CI).","medium band: 80 of 104 hand-judged records were correct, 76.9% [68.0, 84.0] (95% CI).","high band: 73 of 76 hand-judged records were correct, 96.0% [89.0, 98.7] (95% CI).","A fitted calibrated probability was built, measured and withdrawn: it overstated held-out accuracy and reorders nothing."],"claim":"Confidence band and evidence score","gloss":"The confidence band carries a measured accuracy interval; 0.85 represents relative rank strength.","detail":"Confidence on this run is a BAND plus its measured accuracy interval. The number stored alongside each record is a monotone evidence score that orders records; 0.85 represents relative rank strength. A fitted probability was withdrawn after it overstated held-out accuracy. Threshold on the band.","status":"measured","severity":"medium"},{"code":"three_way_split_is_diagnostic","basis":["The corpus is published at the two-way decision: automatable or not-automatable. The three-way split (data_only / physical / mixed) is published as a diagnostic with its measured reliability attached, not as a claim, because the rule engine's `mixed` call cannot be defended.","The engine called 22 record(s) mixed; 10 matched the human judgement (45.5%, 95% CI 26.9-65.3).","The engine's `mixed` call is right on 10/22 hand-judged records (45.5%, 95% CI 26.9%-65.3%); a constant `physical` guess scores 36.4%.","The engine's `mixed` call does not reliably beat a constant `physical` guess on the hand-judged records it labels mixed.","The sign of that difference flips between two independent samples, so the three-way refinement is not demonstrably informative.","The mixed stratum contributes a disproportionate share of errors relative to its size.","At title-only depth the physical/mixed split collapses: a title says that work was done, not whether it was also analysed."],"claim":"The published decision is two-way","gloss":"The three-way split is shown for diagnosis only. The published split is two ways.","detail":"Count and filter the corpus on automatable versus not-automatable. The data_only / physical / mixed split remains an internal engine diagnostic because the mixed call has lower measured reliability.","status":"measured","severity":"medium"},{"code":"automatable_flag_unproven","basis":["6 of 29 assay families are marked automatable now: meta_analysis, machine_learning, survival_analysis, pca, molecular_docking, differential_expression.","The flag is a rule over assay family and work type; validation begins with the first executed replication.","It inherits the 75.2% data_only agreement rate, because the flag requires the record to be analysis-only."],"claim":"Automatable now","gloss":"This flag marks assay families the rules classify as data-only. The first executed replication will validate it.","detail":"This flag marks families the rules consider replicable from data alone. Its validity depends on the analysis-only classification and will be measured through an executed replication.","status":"unverified","severity":"medium"}],"confidence_policy":"band_with_measured_interval","confidence_contract":{"policy":"band_with_measured_interval","summary":"Confidence is reported as a BAND with a measured accuracy interval. The raw evidence score supplies monotone ordering. A fitted point estimate was withdrawn after it overstated held-out accuracy.","evidence_score":"A monotone evidence score in [0,1] built from cue mass, axis separation and text coverage. Use it to order records. Interpret 0.85 as relative rank strength.","confidence_band":"low | medium | high. Consumers threshold on this value.","band_accuracy":"Wilson 95% interval on the measured agreement of the hand-judged records in this band.","calibrated_confidence":"WITHDRAWN. Reintroduction requires held-out evidence demonstrating reliable estimates across every bin."},"band_accuracy":{"low":{"n":210,"low":0.576,"band":"low","high":0.7046,"agree":135,"accuracy":0.6429},"high":{"n":76,"low":0.8903,"band":"high","high":0.9865,"agree":73,"accuracy":0.9605},"medium":{"n":104,"low":0.6795,"band":"medium","high":0.8397,"agree":80,"accuracy":0.7692}},"provider":"rule_based_v1+lex99e1195c7d6f","lexicon_id":"99e1195c7d6f","contract_published_at":"2026-10-23T12:25:29.022232+00:00","confidence_is_band":true,"confidence_is_probability":false,"decision_taxonomy":"two_way","decidable_fraction_two_way":0.8854,"decidable_fraction_two_way_low":0.8565,"decidable_fraction_two_way_high":0.9143,"decidable_fraction_three_way":0.8586,"decidable_fraction_three_way_low":0.8287,"decidable_fraction_three_way_high":0.8884,"decidable_population":5529,"validation_sample_size":438,"three_way_cost_fraction":0.0268,"three_way_cost_records":148,"decision_counts":{"automatable":230,"not_automatable":5299},"decision_split":{"automatable":{"count":230,"share":0.04159884246699222},"not_automatable":{"count":5299,"share":0.9584011575330078}},"diagnostic_work_type_counts":{"mixed":24,"physical":5275,"data_only":230}}}