Construction of the model for predicting prognosis by key genes regulating EGFR-TKI resistance.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36506325", "kind": "science-record", "revision": 3, "content_hash": "sha256:5ab799a3ac8261da06794961d019d34f71babdf3ae55cb3834263eaaeeb624eb", "created_at": "2026-11-01T19:01:00Z", "updated_at": "2026-11-01T22:08:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of the model for predicting prognosis by key genes regulating EGFR-TKI resistance.", "doi": "10.3389/fgene.2022.968376", "pmid": "36506325", "authors": ["Jinke Zhuge", "Xiuqing Wang", "Jingtai Li", "Tongyuan Wang", "Hongkang Wang", "Mingxing Yang", "Wen Dong", "Yong Gao"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: Previous studies have suggested that patients with lung adenocarcinoma (LUAD) will significantly benefit from epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI). However, many LUAD patients will develop resistance to EGFR-TKI. Thus, our study aims to develop models to predict EGFR-TKI resistance and the LUAD prognosis. Methods: Two Gene Expression Omnibus (GEO) datasets (GSE31625 and GSE34228) were used as the discovery datasets to find the common differentially expressed genes (DEGs) in EGFR-TKI resistant LUAD profiles. The association of these common DEGs with LUAD prognosis was investigated in The Cancer Genome Atlas (TCGA) database. Moreover, we constructed the risk score for prognosis prediction of LUAD by LASSO analysis. The performance of the risk score for predicting LUAD prognosis was calculated using an independent dataset (GSE37745). A random forest model by risk score genes was trained in the training dataset, and the diagnostic ability for distinguishing sensitive and EGFR-TKI resistant samples was validated in the internal testing dataset and external testing datasets (GSE122005, GSE80344, and GSE123066). Results: From the discovery datasets, 267 common upregulated genes and 374 common downregulated genes were identified. Among these common DEGs, there were 59 genes negatively associated with prognosis, while 21 genes exhibited positive correlations with prognosis. Eight genes (ABCC2, ARL2BP, DKK1, FUT1, LRFN4, PYGL, SMNDC1, and SNAI2) were selected to construct the risk score signature. In both the discovery and independent validation datasets, LUAD patients with the higher risk score had a poorer prognosis. The nomogram based on risk score showed good performance in prognosis prediction with a C-index of 0.77. The expression levels of ABCC2, ARL2BP, DKK1, LRFN4, PYGL, SMNDC1, and SNAI2 were positively related to the resistance of EGFR-TKI. However, the expression level of FUT1 was favorably correlated with EGFR-TKI responsiveness. The RF model worked wonderfully for distinguishing sensitive and resistant EGFR-TKI samples in the internal and external testing datasets, with predictive area under the curves (AUC) of 0.973 and 0.817, respectively. Conclusion: Our investigation revealed eight genes associated with EGFR-TKI resistance and provided models for EGFR-TKI resistance and prognosis prediction in LUAD patients.", "claims": [{"id": "claim-method", "statement": "Then, based on the eight genes, we constructed a random forest model by the “caret” R package (Kuhn, 2008) to distinguish sensitive and resistant EGFR-TKI patients on the training dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36506325", "locator": "sections[9] \"2.7 Construction and validation of the random forest model to distinguish sensitive and resistant EGFR-TKI patients\" paragraph 3", "quote": "Then, based on the eight genes, we constructed a random forest model by the “caret” R package (Kuhn, 2008) to distinguish sensitive and resistant EGFR-TKI patients on the training dataset."}], "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:36506325", "locator": "sections[24] \"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": "A random forest model based on eight genes distinguished EGFR-TKI-sensitive from EGFR-TKI-resistant samples with an AUC of 0.973 in internal testing and 0.817 in external testing.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36506325", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The RF model worked wonderfully for distinguishing sensitive and resistant EGFR-TKI samples in the internal and external testing datasets, with predictive area under the curves (AUC) of 0.973 and 0.817, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "A random forest model based on eight genes distinguished EGFR-TKI-sensitive from EGFR-TKI-resistant samples with an AUC of 0.973 in internal testing and 0.817 in external testing.", "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:36506325"], "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:GSE122005"], "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:GSE122005"], "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:36506325", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2022.968376", "sha256": "sha256:4c1ec39ad4e0e50e902cb646bb7880cddbc2aee0d693166150f993851c5c3e04", "bytes": 33533, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2022.968376", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9732098", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE122005", "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=GSE122005", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE122005", "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": ["GSM3452888", "GSM3452889", "GSM3452890", "GSM3452891", "GSM3452892", "GSM3452893"], "sample_count": 6, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Activation of IGFBP7 Drives Resistance to Epidermal Growth Factor Receptor Tyrosine Kinase Inhibition in Lung Cancer", "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:GSE123066", "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=GSE123066", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE123066", "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": ["GSM3494550", "GSM3494551", "GSM3494552", "GSM3494553", "GSM3494554", "GSM3494555", "GSM3494556", "GSM3494557", "GSM3494558"], "sample_count": 9, "group_sizes": null, "group_sizes_basis": null, "platform": "10904", "taxon": "Homo sapiens", "title": "Transcriptomic analysis of gefitinib-resistant EGFR-mutant NSCLC cells", "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:GSE31625", "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=GSE31625", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE31625", "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": ["GSM785569", "GSM785570", "GSM785571", "GSM785572", "GSM785573", "GSM785574", "GSM785575", "GSM785576", "GSM785577", "GSM785578", "GSM785579", "GSM785580", "GSM785581", "GSM785582", "GSM785583", "GSM785584", "GSM785585", "GSM785586", "GSM785587", "GSM785588", "GSM785589", "GSM785590", "GSM785591", "GSM785592", "GSM785593", "GSM785594", "GSM785595", "GSM785596", "GSM785597", "GSM785598", "GSM785599", "GSM785600", "GSM785601", "GSM785602", "GSM785603", "GSM785604", "GSM785605", "GSM785606", "GSM785607", "GSM785608", "GSM785609", "GSM785610", "GSM785611", "GSM785612", "GSM785613", "GSM785614", "GSM785615", "GSM785616"], "sample_count": 48, "group_sizes": null, "group_sizes_basis": null, "platform": "96", "taxon": "Homo sapiens", "title": "Gene Expression Patterns that Predict Sensitivity to Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors in Lung Cancer Cell Lines and Human Lung Tumors", "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:GSE34228", "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=GSE34228", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE34228", "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": ["GSM844917", "GSM844918", "GSM844919", "GSM844920", "GSM844921", "GSM844922", "GSM844923", "GSM844924", "GSM844925", "GSM844926", "GSM844927", "GSM844928", "GSM844929", "GSM844930", "GSM844931", "GSM844932", "GSM844933", "GSM844934", "GSM844935", "GSM844936", "GSM844937", "GSM844938", "GSM844939", "GSM844940", "GSM844941", "GSM844942", "GSM844943", "GSM844944", "GSM844945", "GSM844946", "GSM844947", "GSM844948", "GSM844949", "GSM844950", "GSM844951", "GSM844952", "GSM844953", "GSM844954", "GSM844955", "GSM844956", "GSM844957", "GSM844958", "GSM844959", "GSM844960", "GSM844961", "GSM844962", "GSM844963", "GSM844964", "GSM844965", "GSM844966", "GSM844967", "GSM844968", "GSM844969", "GSM844970", "GSM844971", "GSM844972", "GSM844973", "GSM844974", "GSM844975", "GSM844976", "GSM844977", "GSM844978", "GSM844979", "GSM844980", "GSM844981", "GSM844982", "GSM844983", "GSM844984", "GSM844985", "GSM844986", "GSM844987", "GSM844988", "GSM844989", "GSM844990", "GSM844991", "GSM844992", "GSM844993", "GSM844994", "GSM844995", "GSM844996", "GSM844997", "GSM844998", "GSM844999", "GSM845000", "GSM845001", "GSM845002", "GSM845003", "GSM845004", "GSM845005", "GSM845006", "GSM845007", "GSM845008", "GSM845009", "GSM845010", "GSM845011", "GSM845012", "GSM845013", "GSM845014", "GSM845015", "GSM845016", "GSM845017", "GSM845018", "GSM845019", "GSM845020", "GSM845021", "GSM845022", "GSM845023", "GSM845024", "GSM845025", "GSM845026", "GSM845027", "GSM845028", "GSM845029", "GSM845030", "GSM845031", "GSM845032", "GSM845033", "GSM845034", "GSM845035", "GSM845036", "GSM845037", "GSM845038", "GSM845039", "GSM845040", "GSM845041", "GSM845042", "GSM845043", "GSM845044", "GSM845045", "GSM845046", "GSM845047", "GSM845048", "GSM845049", "GSM845050", "GSM845051", "GSM845052", "GSM845053", "GSM845054", "GSM845055", "GSM845056", "GSM845057", "GSM845058", "GSM845059", "GSM845060", "GSM845061", "GSM845062", "GSM845063", "GSM845064", "GSM845065", "GSM845066", "GSM845067", "GSM845068", "GSM845069", "GSM845070", "GSM845071", "GSM845072", "GSM845073", "GSM845074", "GSM845075", "GSM845076", "GSM845077", "GSM845078", "GSM845079", "GSM845080", "GSM845081", "GSM845082", "GSM845083", "GSM845084", "GSM845085", "GSM845086", "GSM845087", "GSM845088", "GSM845089", "GSM845090", "GSM845091", "GSM845092", "GSM845093", "GSM845094", "GSM845095", "GSM845096", "GSM845097", "GSM845098", "GSM845099", "GSM845100", "GSM845101", "GSM845102", "GSM845103", "GSM845104", "GSM845105", "GSM845106", "GSM845107", "GSM845108", "GSM845109", "GSM845110", "GSM845111", "GSM845112", "GSM845113", "GSM845114", "GSM845115", "GSM845116", "GSM845117", "GSM845118", "GSM845119", "GSM845120", "GSM845121", "GSM845122", "GSM845123", "GSM845124"], "sample_count": 208, "group_sizes": null, "group_sizes_basis": null, "platform": "4133", "taxon": "Homo sapiens", "title": "Time-course gene expression profiles of gefitinib-sensitive or -resistant lung adenocarcinoma cells stimulated with EGF, in the presence or absence of gefitinib, EGF receptor tyrosine kinase-specific inhibitor", "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:GSE37745", "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=GSE37745", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE37745", "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": ["GSM1019138", "GSM1019139", "GSM1019140", "GSM1019141", "GSM1019142", "GSM1019143", "GSM1019144", "GSM1019145", "GSM1019146", "GSM1019147", "GSM1019148", "GSM1019149", "GSM1019150", "GSM1019151", "GSM1019152", "GSM1019153", "GSM1019154", "GSM1019155", "GSM1019156", "GSM1019157", "GSM1019158", "GSM1019159", "GSM1019160", "GSM1019161", "GSM1019162", "GSM1019163", "GSM1019164", "GSM1019165", "GSM1019166", "GSM1019167", "GSM1019168", "GSM1019169", "GSM1019170", "GSM1019171", "GSM1019172", "GSM1019173", "GSM1019174", "GSM1019175", "GSM1019176", "GSM1019177", "GSM1019178", "GSM1019179", "GSM1019180", "GSM1019181", "GSM1019182", "GSM1019183", "GSM1019184", "GSM1019185", "GSM1019186", "GSM1019187", "GSM1019188", "GSM1019189", "GSM1019190", "GSM1019191", "GSM1019192", "GSM1019193", "GSM1019194", "GSM1019195", "GSM1019196", "GSM1019197", "GSM1019198", "GSM1019199", "GSM1019200", "GSM1019201", "GSM1019202", "GSM1019203", "GSM1019204", "GSM1019205", "GSM1019206", "GSM1019207", "GSM1019208", "GSM1019209", "GSM1019210", "GSM1019211", "GSM1019212", "GSM1019213", "GSM1019214", "GSM1019215", "GSM1019216", "GSM1019217", "GSM1019218", "GSM1019219", "GSM1019220", "GSM1019221", "GSM1019222", "GSM1019223", "GSM1019224", "GSM1019225", "GSM1019226", "GSM1019227", "GSM1019228", "GSM1019229", "GSM1019230", "GSM1019231", "GSM1019232", "GSM1019233", "GSM1019234", "GSM1019235", "GSM1019236", "GSM1019237", "GSM1019238", "GSM1019239", "GSM1019240", "GSM1019241", "GSM1019242", "GSM1019243", "GSM1019244", "GSM1019245", "GSM1019246", "GSM1019247", "GSM1019248", "GSM1019249", "GSM1019250", "GSM1019251", "GSM1019252", "GSM1019253", "GSM1019254", "GSM1019255", "GSM1019256", "GSM1019257", "GSM1019258", "GSM1019259", "GSM1019260", "GSM1019261", "GSM1019262", "GSM1019263", "GSM1019264", "GSM1019265", "GSM1019266", "GSM1019267", "GSM1019268", "GSM1019269", "GSM1019270", "GSM1019271", "GSM1019272", "GSM1019273", "GSM1019274", "GSM1019275", "GSM1019276", "GSM1019277", "GSM1019278", "GSM1019279", "GSM1019280", "GSM1019281", "GSM1019282", "GSM1019283", "GSM1019284", "GSM1019285", "GSM1019286", "GSM1019287", "GSM1019288", "GSM1019289", "GSM1019290", "GSM1019291", "GSM1019292", "GSM1019293", "GSM1019294", "GSM1019295", "GSM1019296", "GSM1019297", "GSM1019298", "GSM1019299", "GSM1019300", "GSM1019301", "GSM1019302", "GSM1019303", "GSM1019304", "GSM1019305", "GSM1019306", "GSM1019307", "GSM1019308", "GSM1019309", "GSM1019310", "GSM1019311", "GSM1019312", "GSM1019313", "GSM1019314", "GSM1019315", "GSM1019316", "GSM1019317", "GSM1019318", "GSM1019319", "GSM1019320", "GSM1019321", "GSM1019322", "GSM1019323", "GSM1019324", "GSM1019325", "GSM1019326", "GSM1019327", "GSM1019328", "GSM1019329", "GSM1019330", "GSM1019331", "GSM1019332", "GSM1019333"], "sample_count": 196, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Biomarker discovery in non-small cell lung cancer: integrating gene expression profiling, meta-analysis and tissue microarray validation", "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:GSE80344", "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=GSE80344", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE80344", "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": ["GSM2124846", "GSM2124847", "GSM2124848", "GSM2124849", "GSM2124850", "GSM2124851", "GSM2124852", "GSM2124853", "GSM2124854", "GSM2124855", "GSM2124856", "GSM2124857", "GSM2124858", "GSM2124859", "GSM2124860", "GSM2124861"], "sample_count": 16, "group_sizes": null, "group_sizes_basis": null, "platform": "16699", "taxon": "Homo sapiens", "title": "Gene expression profiling of Erlotinib sensitive and resistant human non small cell lung cancer (NSCLC) cell lines", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE122005 not downloaded", "asset:data:geo:GSE122005 licence not read", "asset:data:geo:GSE123066 not downloaded", "asset:data:geo:GSE123066 licence not read", "asset:data:geo:GSE31625 not downloaded", "asset:data:geo:GSE31625 licence not read", "asset:data:geo:GSE34228 not downloaded", "asset:data:geo:GSE34228 licence not read", "asset:data:geo:GSE37745 not downloaded", "asset:data:geo:GSE37745 licence not read", "asset:data:geo:GSE80344 not downloaded", "asset:data:geo:GSE80344 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": "Then, based on the eight genes, we constructed a random forest model by the “caret” R package (Kuhn, 2008) to distinguish sensitive and resistant EGFR-TKI patients on the training dataset."}}, {"id": "method-2", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "principal component analysis"}}, {"id": "method-3", "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", "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": ["GSE31625", "GSE34228", "GSE122005", "GSE80344", "GSE123066", "GSE37745"], "resolved_accessions": ["GSE31625", "GSE34228", "GSE122005", "GSE80344", "GSE123066", "GSE37745"], "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 GSE31625, GSE34228, GSE122005, GSE80344, GSE123066, GSE37745 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:36506325", "data:geo:GSE122005"], "steps": []}, "must_build": {"dsl_operations": [{"operation": "machine_learning_random_forest", "unlocks_papers": null, "priority": null}], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE122005"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:19:23Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 10078, "completion_tokens": 2226, "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:36506325", "type": "paper", "label": "pmid:36506325"}, {"id": "source:pmid:36506325", "type": "manuscript", "label": "PMID 36506325 full text"}, {"id": "data:geo:GSE122005", "type": "dataset", "label": "GEO GSE122005"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36506325", "type": "paper", "label": "pmid:36506325"}, {"id": "source:pmid:36506325", "type": "manuscript", "label": "PMID 36506325 full text"}, {"id": "data:geo:GSE122005", "type": "dataset", "label": "GEO GSE122005"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36506325", "type": "extraction", "used": ["source:pmid:36506325"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE122005", "type": "accession_resolution", "used": ["data:geo:GSE122005"], "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:36506325", "parent_hash": "sha256:763b1fbb06bd3c271b0facbd24dc3243ab2e3727f785b0ad9c6049ee825311a8", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE122005 licence not read", "asset:data:geo:GSE122005 not downloaded", "asset:data:geo:GSE123066 licence not read", "asset:data:geo:GSE123066 not downloaded", "asset:data:geo:GSE31625 licence not read", "asset:data:geo:GSE31625 not downloaded", "asset:data:geo:GSE34228 licence not read", "asset:data:geo:GSE34228 not downloaded", "asset:data:geo:GSE37745 licence not read", "asset:data:geo:GSE37745 not downloaded", "asset:data:geo:GSE80344 licence not read", "asset:data:geo:GSE80344 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": "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": ["Then, based on the eight genes, we constructed a random forest model by the “caret” R package (Kuhn, 2008) to distinguish sensitive and resistant EGFR-TKI patients on the training dataset."], "order_basis": "analysis_only", "id": "assay:1"}]}