Identification of Diagnostic Biomarkers in Systemic Lupus Erythematosus Based on Bioinformatics Analysis and Machine Learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35495164", "kind": "science-record", "revision": 5, "content_hash": "sha256:4e4548b0c60c493e699477d0ab0e893adda2eba27be5f0c39540c23209e05719", "created_at": "2026-11-01T18:14:22Z", "updated_at": "2026-11-01T22:48:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Diagnostic Biomarkers in Systemic Lupus Erythematosus Based on Bioinformatics Analysis and Machine Learning.", "doi": "10.3389/fgene.2022.865559", "pmid": "35495164", "authors": ["Zhihang Jiang", "Mengting Shao", "Xinzhu Dai", "Zhixin Pan", "Dongmei Liu"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Systemic lupus erythematosus (SLE) is a complex autoimmune disease that affects several organs and causes variable clinical symptoms. Exploring new insights on genetic factors may help reveal SLE etiology and improve the survival of SLE patients. The current study is designed to identify key genes involved in SLE and develop potential diagnostic biomarkers for SLE in clinical practice. Expression data of all genes of SLE and control samples in GSE65391 and GSE72509 datasets were downloaded from the Gene Expression Omnibus (GEO) database. A total of 11 accurate differentially expressed genes (DEGs) were identified by the \"limma\" and \"RobustRankAggreg\" R package. All these genes were functionally associated with several immune-related biological processes and a single KEGG (Kyoto Encyclopedia of Genes and Genome) pathway of necroptosis. The PPI analysis showed that IFI44, IFI44L, EIF2AK2, IFIT3, IFITM3, ZBP1, TRIM22, PRIC285, XAF1, and PARP9 could interact with each other. In addition, the expression patterns of these DEGs were found to be consistent in GSE39088. Moreover, Receiver operating characteristic (ROC) curves analysis indicated that all these DEGs could serve as potential diagnostic biomarkers according to the area under the ROC curve (AUC) values. Furthermore, we constructed the transcription factor (TF)-diagnostic biomarker-microRNA (miRNA) network composed of 278 nodes and 405 edges, and a drug-diagnostic biomarker network consisting of 218 nodes and 459 edges. To investigate the relationship between diagnostic biomarkers and the immune system, we evaluated the immune infiltration landscape of SLE and control samples from GSE6539. Finally, using a variety of machine learning methods, IFI44 was determined to be the optimal diagnostic biomarker of SLE and then verified by quantitative real-time PCR (qRT-PCR) in an independent cohort. Our findings may benefit the diagnosis of patients with SLE and guide in developing novel targeted therapy in treating SLE patients.", "claims": [{"id": "claim-method", "statement": "Machine learning methods, including LR, RF, XGBoost, SVM, and ANN (Li et al., 2021), were performed by the “glmnet”, “randomForest”, “xgboost”, “e1071”, and “neuralnet” R packages to develop classifiers for diagnostic classification, respectively.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35495164", "locator": "sections[7] \"Machine Learning\" paragraph 2", "quote": "Machine learning methods, including LR, RF, XGBoost, SVM, and ANN (Li et al., 2021), were performed by the “glmnet”, “randomForest”, “xgboost”, “e1071”, and “neuralnet” R packages to develop classifiers for diagnostic classification, respectively."}], "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:35495164", "locator": "sections[19] \"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": "IFI44 was identified as the optimal diagnostic biomarker for SLE and validated in an independent cohort, with an AUC of 0.850, diagnostic sensitivity of 0.923, and diagnostic specificity of 0.850.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35495164", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Finally, using a variety of machine learning methods, IFI44 was determined to be the optimal diagnostic biomarker of SLE and then verified by quantitative real-time PCR (qRT-PCR) in an independent cohort."}, {"asset": "source:pmid:35495164", "locator": "sections[16] \"Identification of Optimal Biomarkers in SLE\" paragraph 5", "quote": "ROC curve analysis showed that AUC for IFI44 was 0.850 when distinguishing SLE patients from healthy controls, and the diagnostic sensitivity and specificity were 0.923 and 0.850, respectively (Figure 9C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "IFI44 was identified as the optimal diagnostic biomarker for SLE and validated in an independent cohort, with an AUC of 0.850, diagnostic sensitivity of 0.923, and diagnostic specificity of 0.850.", "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:35495164"], "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:GSE39088"], "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:GSE39088"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35495164", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.865559", "sha256": "sha256:3743b849d47a748951fb7ae12c45327a806709d1a6ffd8a0c4f9702f7288d417", "bytes": 30914, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2022.865559", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9047905", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE39088", "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=GSE39088", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE39088", "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": ["GSM955680", "GSM955681", "GSM955682", "GSM955683", "GSM955684", "GSM955685", "GSM955686", "GSM955687", "GSM955688", "GSM955689", "GSM955690", "GSM955691", "GSM955692", "GSM955693", "GSM955694", "GSM955695", "GSM955696", "GSM955697", "GSM955698", "GSM955699", "GSM955700", "GSM955701", "GSM955702", "GSM955703", "GSM955704", "GSM955705", "GSM955706", "GSM955707", "GSM955708", "GSM955709", "GSM955710", "GSM955711", "GSM955712", "GSM955713", "GSM955714", "GSM955715", "GSM955716", "GSM955717", "GSM955718", "GSM955719", "GSM955720", "GSM955721", "GSM955722", "GSM955723", "GSM955724", "GSM955725", "GSM955726", "GSM955727", "GSM955728", "GSM955729", "GSM955730", "GSM955731", "GSM955732", "GSM955733", "GSM955734", "GSM955735", "GSM955736", "GSM955737", "GSM955738", "GSM955739", "GSM955740", "GSM955741", "GSM955742", "GSM955743", "GSM955744", "GSM955745", "GSM955746", "GSM955747", "GSM955748", "GSM955749", "GSM955750", "GSM955751", "GSM955752", "GSM955753", "GSM955754", "GSM955755", "GSM955756", "GSM955757", "GSM955758", "GSM955759", "GSM955760", "GSM955761", "GSM955762", "GSM955763", "GSM955764", "GSM955765", "GSM955766", "GSM955767", "GSM955768", "GSM955769", "GSM955770", "GSM955771", "GSM955772", "GSM955773", "GSM955774", "GSM955775", "GSM955776", "GSM955777", "GSM955778", "GSM955779", "GSM955780", "GSM955781", "GSM955782", "GSM955783", "GSM955784", "GSM955785", "GSM955786", "GSM955787", "GSM955788", "GSM955789", "GSM955790", "GSM955791", "GSM955792", "GSM955793", "GSM955794", "GSM955795", "GSM955796", "GSM955797", "GSM955798", "GSM955799", "GSM955800", "GSM955801", "GSM955802", "GSM955803", "GSM955804", "GSM955805", "GSM955806", "GSM955807", "GSM955808", "GSM955809", "GSM955810", "GSM955811", "GSM955812", "GSM955813", "GSM955814", "GSM955815", "GSM955816", "GSM955817", "GSM955818", "GSM955819", "GSM955820", "GSM955821"], "sample_count": 142, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Down-regulation of Interferon signature in systemic lupus erythematosus patients by active immunization with Interferon alpha-Kinoid", "resolution_checked_at": "2026-11-01T18:06:44Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE65391", "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=GSE65391", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE65391", "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": ["GSM1594219", "GSM1594220", "GSM1594221", "GSM1594222", "GSM1594223", "GSM1594224", "GSM1594225", "GSM1594226", "GSM1594227", "GSM1594228", "GSM1594229", "GSM1594230", "GSM1594231", "GSM1594232", "GSM1594233", "GSM1594234", "GSM1594235", "GSM1594236", "GSM1594237", "GSM1594238", "GSM1594239", "GSM1594240", "GSM1594241", "GSM1594244", "GSM1594247", "GSM1594250", "GSM1594253", "GSM1594254", "GSM1594255", "GSM1594256", "GSM1594257", "GSM1594258", "GSM1594259", "GSM1594260", "GSM1594261", "GSM1594262", "GSM1594263", "GSM1594264", "GSM1594265", "GSM1594266", "GSM1594267", "GSM1594268", "GSM1594269", "GSM1594270", "GSM1594271", "GSM1594272", "GSM1594273", "GSM1594274", "GSM1594275", "GSM1594276", "GSM1594277", "GSM1594278", "GSM1594279", "GSM1594280", "GSM1594281", "GSM1594282", "GSM1594283", "GSM1594284", "GSM1594285", "GSM1594286", "GSM1594287", "GSM1594288", "GSM1594289", "GSM1594290", "GSM1594291", "GSM1594292", "GSM1594293", "GSM1594294", "GSM1594295", "GSM1594296", "GSM1594297", "GSM1594298", "GSM1594299", "GSM1594300", "GSM1594301", "GSM1594302", "GSM1594303", "GSM1594304", "GSM1594305", "GSM1594306", "GSM1594307", "GSM1594308", "GSM1594309", "GSM1594310", "GSM1594311", "GSM1594312", "GSM1594313", "GSM1594314", "GSM1594315", "GSM1594316", "GSM1594317", "GSM1594318", "GSM1594319", "GSM1594320", "GSM1594321", "GSM1594322", "GSM1594323", "GSM1594324", "GSM1594325", "GSM1594326", "GSM1594327", "GSM1594328", "GSM1594329", "GSM1594330", "GSM1594331", "GSM1594332", "GSM1594333", "GSM1594334", "GSM1594335", "GSM1594336", "GSM1594337", "GSM1594338", "GSM1594339", "GSM1594340", "GSM1594341", "GSM1594342", "GSM1594343", "GSM1594344", "GSM1594345", "GSM1594346", "GSM1594347", "GSM1594348", "GSM1594349", "GSM1594350", "GSM1594351", "GSM1594352", "GSM1594353", "GSM1594354", "GSM1594355", "GSM1594356", "GSM1594357", "GSM1594358", "GSM1594359", "GSM1594360", "GSM1594361", "GSM1594362", "GSM1594363", "GSM1594364", "GSM1594365", "GSM1594366", "GSM1594367", "GSM1594368", "GSM1594369", "GSM1594370", "GSM1594371", "GSM1594372", "GSM1594373", "GSM1594374", "GSM1594375", "GSM1594376", "GSM1594377", "GSM1594378", "GSM1594379", "GSM1594380", "GSM1594381", "GSM1594382", "GSM1594383", "GSM1594384", "GSM1594385", "GSM1594386", "GSM1594387", "GSM1594388", "GSM1594389", "GSM1594390", "GSM1594391", "GSM1594392", "GSM1594393", "GSM1594394", "GSM1594395", "GSM1594396", "GSM1594397", "GSM1594398", "GSM1594399", "GSM1594400", "GSM1594401", "GSM1594402", "GSM1594403", "GSM1594404", "GSM1594405", "GSM1594406", "GSM1594407", "GSM1594408", "GSM1594409", "GSM1594410", "GSM1594411", "GSM1594412", "GSM1594413", "GSM1594414", "GSM1594415", "GSM1594416", "GSM1594417", "GSM1594418", "GSM1594419", "GSM1594420", "GSM1594421", "GSM1594422", "GSM1594423", "GSM1594424", "GSM1594425", "GSM1594426", "GSM1594427", "GSM1594428", "GSM1594429", "GSM1594430", "GSM1594431", "GSM1594432", "GSM1594433", "GSM1594434", "GSM1594435", "GSM1594436", "GSM1594437", "GSM1594438", "GSM1594439", "GSM1594440", "GSM1594441", "GSM1594442", "GSM1594443", "GSM1594444", "GSM1594445", "GSM1594446", "GSM1594447", "GSM1594448", "GSM1594449", "GSM1594450", "GSM1594451", "GSM1594452", "GSM1594453", "GSM1594454", "GSM1594455", "GSM1594456", "GSM1594457", "GSM1594458", "GSM1594459", "GSM1594460", "GSM1594461", "GSM1594462", "GSM1594463", "GSM1594464", "GSM1594465", "GSM1594466", "GSM1594467", "GSM1594468", "GSM1594469", "GSM1594470", "GSM1594471", "GSM1594472", "GSM1594473", "GSM1594474", "GSM1594475", "GSM1594476", "GSM1594477", "GSM1594478", "GSM1594479", "GSM1594480", "GSM1594481", "GSM1594482", "GSM1594483", "GSM1594484", "GSM1594485", "GSM1594486", "GSM1594487", "GSM1594488", "GSM1594489", "GSM1594490", "GSM1594491", "GSM1594492", "GSM1594493", "GSM1594494", "GSM1594495", "GSM1594496", "GSM1594497", "GSM1594498", "GSM1594499", "GSM1594500", "GSM1594501", "GSM1594502", "GSM1594503", "GSM1594504", "GSM1594505", "GSM1594506", "GSM1594507", "GSM1594508", "GSM1594509", "GSM1594510", "GSM1594511", "GSM1594512", "GSM1594513", "GSM1594514", "GSM1594515", "GSM1594516", "GSM1594517", "GSM1594518", "GSM1594519", "GSM1594520", "GSM1594521", "GSM1594522", "GSM1594523", "GSM1594524", "GSM1594525", "GSM1594526", "GSM1594527", "GSM1594528", "GSM1594529", "GSM1594530", "GSM1594531", "GSM1594532", "GSM1594533", "GSM1594534", "GSM1594535", "GSM1594536", "GSM1594537", "GSM1594538", "GSM1594539", "GSM1594540", "GSM1594541", "GSM1594542", "GSM1594543", "GSM1594544", "GSM1594545", "GSM1594546", "GSM1594547", "GSM1594548", "GSM1594549", "GSM1594550", "GSM1594551", "GSM1594552", "GSM1594553", "GSM1594554", "GSM1594555", "GSM1594556", "GSM1594557", "GSM1594558", "GSM1594559", "GSM1594560", "GSM1594561", "GSM1594562", "GSM1594563", "GSM1594564", "GSM1594565", "GSM1594566", "GSM1594567", "GSM1594568", "GSM1594569", "GSM1594570", "GSM1594571", "GSM1594572", "GSM1594573", "GSM1594574", "GSM1594575", "GSM1594576", "GSM1594577", "GSM1594578", "GSM1594579", "GSM1594580", "GSM1594581", "GSM1594582", "GSM1594583", "GSM1594584", "GSM1594585", "GSM1594586", "GSM1594587", "GSM1594588", "GSM1594589", "GSM1594590", "GSM1594591", "GSM1594592", "GSM1594593", "GSM1594594", "GSM1594595", "GSM1594596", "GSM1594597", "GSM1594598", "GSM1594599", "GSM1594600", "GSM1594601", "GSM1594602", "GSM1594603", "GSM1594604", "GSM1594605", "GSM1594606", "GSM1594607", "GSM1594608", "GSM1594609", "GSM1594610", "GSM1594611", "GSM1594612", "GSM1594613", "GSM1594614", "GSM1594615", "GSM1594616", "GSM1594617", "GSM1594618", "GSM1594619", "GSM1594620", "GSM1594621", "GSM1594622", "GSM1594623", "GSM1594624", "GSM1594625", "GSM1594626", "GSM1594627", "GSM1594628", "GSM1594629", "GSM1594630", "GSM1594631", "GSM1594632", "GSM1594633", "GSM1594634", "GSM1594635", "GSM1594636", "GSM1594637", "GSM1594638", "GSM1594639", "GSM1594640", "GSM1594641", "GSM1594642", "GSM1594643", "GSM1594644", "GSM1594645", "GSM1594646", "GSM1594647", "GSM1594648", "GSM1594649", "GSM1594650", "GSM1594651", "GSM1594652", "GSM1594653", "GSM1594654", "GSM1594655", "GSM1594656", "GSM1594657", "GSM1594658", "GSM1594659", "GSM1594660", "GSM1594661", "GSM1594662", "GSM1594663", "GSM1594664", "GSM1594665", "GSM1594666", "GSM1594667", "GSM1594668", "GSM1594669", "GSM1594670", "GSM1594671", "GSM1594672", "GSM1594673", "GSM1594674", "GSM1594675", "GSM1594676", "GSM1594677", "GSM1594678", "GSM1594679", "GSM1594680", "GSM1594681", "GSM1594682", "GSM1594683", "GSM1594684", "GSM1594685", "GSM1594686", "GSM1594687", "GSM1594688", "GSM1594689", "GSM1594690", "GSM1594691", "GSM1594692", "GSM1594693", "GSM1594694", "GSM1594695", "GSM1594696", "GSM1594697", "GSM1594698", "GSM1594699", "GSM1594700", "GSM1594701", "GSM1594702", "GSM1594703", "GSM1594704", "GSM1594705", "GSM1594706", "GSM1594707", "GSM1594708", "GSM1594709", "GSM1594710", "GSM1594711", "GSM1594712", "GSM1594713", "GSM1594714", "GSM1594715", "GSM1594716", "GSM1594717", "GSM1594718", "GSM1594719", "GSM1594720", "GSM1594721", "GSM1594722", "GSM1594723", "GSM1594724", "GSM1594725", "GSM1594726", "GSM1594727", "GSM1594728", "GSM1594729", "GSM1594730", "GSM1594731", "GSM1594732", "GSM1594733", "GSM1594734", "GSM1594735", "GSM1594736", "GSM1594737", "GSM1594738", "GSM1594739", "GSM1594740", "GSM1594741", "GSM1594742", "GSM1594743", "GSM1594744", "GSM1594745", "GSM1594746", "GSM1594747", "GSM1594748", "GSM1594749", "GSM1594750", "GSM1594751", "GSM1594752", "GSM1594753", "GSM1594754", "GSM1594755", "GSM1594756", "GSM1594757", "GSM1594758", "GSM1594759", "GSM1594760", "GSM1594761", "GSM1594762", "GSM1594763", "GSM1594764", "GSM1594765", "GSM1594766", "GSM1594767", "GSM1594768", "GSM1594769", "GSM1594770", "GSM1594771", "GSM1594772", "GSM1594773", "GSM1594774", "GSM1594775", "GSM1594776", "GSM1594777", "GSM1594778", "GSM1594779", "GSM1594780", "GSM1594781", "GSM1594782", "GSM1594783", "GSM1594784", "GSM1594785", "GSM1594786", "GSM1594787", "GSM1594788", "GSM1594789", "GSM1594790", "GSM1594791", "GSM1594792", "GSM1594793", "GSM1594794", "GSM1594795", "GSM1594796", "GSM1594797", "GSM1594798", "GSM1594799", "GSM1594800", "GSM1594801", "GSM1594802", "GSM1594803", "GSM1594804", "GSM1594805", "GSM1594806", "GSM1594807", "GSM1594808", "GSM1594809", "GSM1594810", "GSM1594811", "GSM1594812", "GSM1594813", "GSM1594814", "GSM1594815", "GSM1594816", "GSM1594817", "GSM1594818", "GSM1594819", "GSM1594820", "GSM1594821", "GSM1594822", "GSM1594823", "GSM1594824", "GSM1594825", "GSM1594826", "GSM1594827", "GSM1594828", "GSM1594829", "GSM1594830", "GSM1594831", "GSM1594832", "GSM1594833", "GSM1594834", "GSM1594835", "GSM1594836", "GSM1594837", "GSM1594838", "GSM1594839", "GSM1594840", "GSM1594841", "GSM1594842", "GSM1594843", "GSM1594844", "GSM1594845", "GSM1594846", "GSM1594847", "GSM1594848", "GSM1594849", "GSM1594850", "GSM1594851", "GSM1594852", "GSM1594853", "GSM1594854", "GSM1594855", "GSM1594856", "GSM1594857", "GSM1594858", "GSM1594859", "GSM1594860", "GSM1594861", "GSM1594862", "GSM1594863", "GSM1594864", "GSM1594865", "GSM1594866", "GSM1594867", "GSM1594868", "GSM1594869", "GSM1594870", "GSM1594871", "GSM1594872", "GSM1594873", "GSM1594874", "GSM1594875", "GSM1594876", "GSM1594877", "GSM1594878", "GSM1594879", "GSM1594880", "GSM1594881", "GSM1594882", "GSM1594883", "GSM1594884", "GSM1594885", "GSM1594886", "GSM1594887", "GSM1594888", "GSM1594889", "GSM1594890", "GSM1594891", "GSM1594892", "GSM1594893", "GSM1594894", "GSM1594895", "GSM1594896", "GSM1594897", "GSM1594898", "GSM1594899", "GSM1594900", "GSM1594901", "GSM1594902", "GSM1594903", "GSM1594904", "GSM1594905", "GSM1594906", "GSM1594907", "GSM1594908", "GSM1594909", "GSM1594910", "GSM1594911", "GSM1594912", "GSM1594913", "GSM1594914", "GSM1594915", "GSM1594916", "GSM1594917", "GSM1594918", "GSM1594919", "GSM1594920", "GSM1594921", "GSM1594922", "GSM1594923", "GSM1594924", "GSM1594925", "GSM1594926", "GSM1594927", "GSM1594928", "GSM1594929", "GSM1594930", "GSM1594931", "GSM1594932", "GSM1594933", "GSM1594934", "GSM1594935", "GSM1594936", "GSM1594937", "GSM1594938", "GSM1594939", "GSM1594940", "GSM1594941", "GSM1594942", "GSM1594943", "GSM1594944", "GSM1594945", "GSM1594946", "GSM1594947", "GSM1594948", "GSM1594949", "GSM1594950", "GSM1594951", "GSM1594952", "GSM1594953", "GSM1594954", "GSM1594955", "GSM1594956", "GSM1594957", "GSM1594958", "GSM1594959", "GSM1594960", "GSM1594961", "GSM1594962", "GSM1594963", "GSM1594964", "GSM1594965", "GSM1594966", "GSM1594967", "GSM1594968", "GSM1594969", "GSM1594970", "GSM1594971", "GSM1594972", "GSM1594973", "GSM1594974", "GSM1594975", "GSM1594976", "GSM1594977", "GSM1594978", "GSM1594979", "GSM1594980", "GSM1594981", "GSM1594982", "GSM1594983", "GSM1594984", "GSM1594985", "GSM1594986", "GSM1594987", "GSM1594988", "GSM1594989", "GSM1594990", "GSM1594991", "GSM1594992", "GSM1594993", "GSM1594994", "GSM1594995", "GSM1594996", "GSM1594997", "GSM1594998", "GSM1594999", "GSM1595000", "GSM1595001", "GSM1595002", "GSM1595003", "GSM1595004", "GSM1595005", "GSM1595006", "GSM1595007", "GSM1595008", "GSM1595009", "GSM1595010", "GSM1595011", "GSM1595012", "GSM1595013", "GSM1595014", "GSM1595015", "GSM1595016", "GSM1595017", "GSM1595018", "GSM1595019", "GSM1595020", "GSM1595021", "GSM1595022", "GSM1595023", "GSM1595024", "GSM1595025", "GSM1595026", "GSM1595027", "GSM1595028", "GSM1595029", "GSM1595030", "GSM1595031", "GSM1595032", "GSM1595033", "GSM1595034", "GSM1595035", "GSM1595036", "GSM1595037", "GSM1595038", "GSM1595039", "GSM1595040", "GSM1595041", "GSM1595042", "GSM1595043", "GSM1595044", "GSM1595045", "GSM1595046", "GSM1595047", "GSM1595048", "GSM1595049", "GSM1595050", "GSM1595051", "GSM1595052", "GSM1595053", "GSM1595054", "GSM1595055", "GSM1595056", "GSM1595057", "GSM1595058", "GSM1595059", "GSM1595060", "GSM1595061", "GSM1595062", "GSM1595063", "GSM1595064", "GSM1595065", "GSM1595066", "GSM1595067", "GSM1595068", "GSM1595069", "GSM1595070", "GSM1595071", "GSM1595072", "GSM1595073", "GSM1595074", "GSM1595075", "GSM1595076", "GSM1595077", "GSM1595078", "GSM1595079", "GSM1595080", "GSM1595081", "GSM1595082", "GSM1595083", "GSM1595084", "GSM1595085", "GSM1595086", "GSM1595087", "GSM1595088", "GSM1595089", "GSM1595090", "GSM1595091", "GSM1595092", "GSM1595093", "GSM1595094", "GSM1595095", "GSM1595096", "GSM1595097", "GSM1595098", "GSM1595099", "GSM1595100", "GSM1595101", "GSM1595102", "GSM1595103", "GSM1595104", "GSM1595105", "GSM1595106", "GSM1595107", "GSM1595108", "GSM1595109", "GSM1595110", "GSM1595111", "GSM1595112", "GSM1595113", "GSM1595114", "GSM1595115", "GSM1595116", "GSM1595117", "GSM1595118", "GSM1595119", "GSM1595120", "GSM1595121", "GSM1595122", "GSM1595123", "GSM1595124", "GSM1595125", "GSM1595126", "GSM1595127", "GSM1595128", "GSM1595129", "GSM1595130", "GSM1595131", "GSM1595132", "GSM1595133", "GSM1595134", "GSM1595135", "GSM1595136", "GSM1595137", "GSM1595138", "GSM1595139", "GSM1595140", "GSM1595141", "GSM1595142", "GSM1595143", "GSM1595144", "GSM1595145", "GSM1595146", "GSM1595147", "GSM1595148", "GSM1595149", "GSM1595150", "GSM1595151", "GSM1595152", "GSM1595153", "GSM1595154", "GSM1595155", "GSM1595156", "GSM1595157", "GSM1595158", "GSM1595159", "GSM1595160", "GSM1595161", "GSM1595162", "GSM1595163", "GSM1595164", "GSM1595165", "GSM1595166", "GSM1595167", "GSM1595168", "GSM1595169", "GSM1595170", "GSM1595171", "GSM1595172", "GSM1595173", "GSM1595174", "GSM1595175", "GSM1595176", "GSM1595177", "GSM1595178", "GSM1595179", "GSM1595180", "GSM1595181", "GSM1595182", "GSM1595183", "GSM1595184", "GSM1595185", "GSM1595186", "GSM1595187", "GSM1595188", "GSM1595189", "GSM1595190", "GSM1595191", "GSM1595192", "GSM1595193", "GSM1595194", "GSM1595195", "GSM1595196", "GSM1595197", "GSM1595198", "GSM1595199", "GSM1595200", "GSM1595201", "GSM1595202", "GSM1595203", "GSM1595204", "GSM1595205", "GSM1595206", "GSM1595207", "GSM1595208", "GSM1595209", "GSM1595210", "GSM1595211", "GSM1595212", "GSM1595213", "GSM1595214", "GSM1595215", "GSM1595216", "GSM1595217", "GSM1595218", "GSM1595219", "GSM1595220", "GSM1595221", "GSM1595222"], "sample_count": 996, "group_sizes": null, "group_sizes_basis": null, "platform": "10558", "taxon": "Homo sapiens", "title": "Longitudinal transcriptional pediatric SLE study with clinical parameters", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE72509", "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=GSE72509", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE72509", "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": ["GSM1863633", "GSM1863634", "GSM1863635", "GSM1863636", "GSM1863637", "GSM1863638", "GSM1863639", "GSM1863640", "GSM1863641", "GSM1863642", "GSM1863643", "GSM1863644", "GSM1863645", "GSM1863646", "GSM1863647", "GSM1863648", "GSM1863649", "GSM1863650", "GSM1863651", "GSM1863652", "GSM1863653", "GSM1863654", "GSM1863655", "GSM1863656", "GSM1863657", "GSM1863658", "GSM1863659", "GSM1863660", "GSM1863661", "GSM1863662", "GSM1863663", "GSM1863664", "GSM1863665", "GSM1863666", "GSM1863667", "GSM1863668", "GSM1863669", "GSM1863670", "GSM1863671", "GSM1863672", "GSM1863673", "GSM1863674", "GSM1863675", "GSM1863676", "GSM1863677", "GSM1863678", "GSM1863679", "GSM1863680", "GSM1863681", "GSM1863682", "GSM1863683", "GSM1863684", "GSM1863685", "GSM1863686", "GSM1863687", "GSM1863688", "GSM1863689", "GSM1863690", "GSM1863691", "GSM1863692", "GSM1863693", "GSM1863694", "GSM1863695", "GSM1863696", "GSM1863697", "GSM1863698", "GSM1863699", "GSM1863700", "GSM1863701", "GSM1863702", "GSM1863703", "GSM1863704", "GSM1863705", "GSM1863706", "GSM1863707", "GSM1863708", "GSM1863709", "GSM1863710", "GSM1863711", "GSM1863712", "GSM1863713", "GSM1863714", "GSM1863715", "GSM1863716", "GSM1863717", "GSM1863718", "GSM1863719", "GSM1863720", "GSM1863721", "GSM1863722", "GSM1863723", "GSM1863724", "GSM1863725", "GSM1863726", "GSM1863727", "GSM1863728", "GSM1863729", "GSM1863730", "GSM1863731", "GSM1863732", "GSM1863733", "GSM1863734", "GSM1863735", "GSM1863736", "GSM1863737", "GSM1863738", "GSM1863739", "GSM1863740", "GSM1863741", "GSM1863742", "GSM1863743", "GSM1863744", "GSM1863745", "GSM1863746", "GSM1863747", "GSM1863748", "GSM1863749"], "sample_count": 117, "group_sizes": null, "group_sizes_basis": null, "platform": "16791", "taxon": "Homo sapiens", "title": "SLE lupus RNA-seq", "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:GSE39088 not downloaded", "asset:data:geo:GSE39088 licence not read", "asset:data:geo:GSE65391 not downloaded", "asset:data:geo:GSE65391 licence not read", "asset:data:geo:GSE72509 not downloaded", "asset:data:geo:GSE72509 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "machine_learning", "specific": "support_vector_machine", "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": "Machine learning methods, including LR, RF, XGBoost, SVM, and ANN (Li et al., 2021), were performed by the “glmnet”, “randomForest”, “xgboost”, “e1071”, and “neuralnet” R packages to develop classifiers for diagnostic classification, respectively."}}, {"id": "method-2", "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": 0.7, "band": "medium", "quote": "limma"}}, {"id": "method-3", "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": 0.8, "band": "high", "quote": "XGBoost"}}, {"id": "method-4", "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": "PPI"}}, {"id": "method-5", "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": ["method.reader_differs_from_extractor"]}, "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: support_vector_machine."}, "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": ["GSE65391", "GSE72509", "GSE39088"], "resolved_accessions": ["GSE65391", "GSE72509", "GSE39088"], "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 GSE65391, GSE72509, GSE39088 in GEO 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:35495164", "data:geo:GSE39088"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE39088"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:44:12Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 9092, "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:35495164", "type": "paper", "label": "pmid:35495164"}, {"id": "source:pmid:35495164", "type": "manuscript", "label": "PMID 35495164 full text"}, {"id": "data:geo:GSE39088", "type": "dataset", "label": "GEO GSE39088"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35495164", "type": "paper", "label": "pmid:35495164"}, {"id": "source:pmid:35495164", "type": "manuscript", "label": "PMID 35495164 full text"}, {"id": "data:geo:GSE39088", "type": "dataset", "label": "GEO GSE39088"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35495164", "type": "extraction", "used": ["source:pmid:35495164"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE39088", "type": "accession_resolution", "used": ["data:geo:GSE39088"], "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:35495164", "parent_hash": "sha256:c20d2a33fc21a16d2c277b12b6017343dddfd153df8042bcc00f43798a4aa5fa", "fork_of": null, "generation": 4}, "gaps": ["asset:data:geo:GSE39088 licence not read", "asset:data:geo:GSE39088 not downloaded", "asset:data:geo:GSE65391 licence not read", "asset:data:geo:GSE65391 not downloaded", "asset:data:geo:GSE72509 licence not read", "asset:data:geo:GSE72509 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["ker of SLE and then verified by quantitative real-time PCR (qRT-PCR) in an independent cohort. Our find"], "order_basis": "paper_text_order:1907", "id": "assay:1"}, {"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": ["Machine learning methods, including LR, RF, XGBoost, SVM, and ANN (Li et al., 2021), were performed by the “glmnet”, “randomForest”, “xgboost”, “e1071”, and “neuralnet” R packages to develop classifiers for diagnostic classification, respectively."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}