← All .science files
pmid:38028591
Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.
Yu Yin · Congcong Chen · Dong Zhang · Qianguang Han · Zijie Wang · Zhengkai Huang · Hao Chen · Li Sun · Shuang Fei · Jun Tao · Zhijian Han · Ruoyun Tan · Min Gu · Xiaobing Ju
Frontiers in genetics · 2023 doi:10.3389/fgene.2023.1276963
Paper
complete
Experiment
complete
Data
partial
Analysis
partial
Execution
absent
Execution evidence has not yet been added to this .science file. Explore its methods, data and open questions to plan the next step.
Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
paper object · 10 fields
authors array · 14 items
claims array · 3 items
[0] object · 6 fields
evidence array · 1 item
[0] object · 3 fields
[1] object · 6 fields
evidence array · 1 item
[0] object · 3 fields
gaps array · 0 items
experiment object · 8 fields
steps array · 3 items
[0] object · 13 fields
parameters object · 0 fields
inputs array · 1 item
outputs array · 1 item
instruments array · 0 items
reagents array · 0 items
[1] object · 13 fields
parameters object · 0 fields
inputs array · 1 item
outputs array · 1 item
instruments array · 0 items
reagents array · 0 items
[2] object · 13 fields
parameters object · 0 fields
inputs array · 1 item
outputs array · 1 item
instruments array · 0 items
reagents array · 0 items
materials array · 0 items
instruments array · 0 items
reagents array · 0 items
conditions object · 0 fields
gaps array · 0 items
data object · 3 fields
assets array · 6 items
[0] object · 15 fields
structure object · 4 fields
[1] object · 15 fields
structure object · 15 fields
sample_accessions array · 282 items
[2] object · 15 fields
structure object · 15 fields
sample_accessions array · 65 items
[3] object · 15 fields
structure object · 15 fields
sample_accessions array · 55 items
[4] object · 15 fields
structure object · 15 fields
sample_accessions array · 274 items
[5] object · 15 fields
structure object · 15 fields
sample_accessions array · 1208 items
gaps array · 10 items
analysis object · 4 fields
methods array · 8 items
[0] object · 10 fields
dsl_operations array · 3 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
evidence object · 3 fields
[1] object · 10 fields
dsl_operations array · 1 item
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[2] object · 10 fields
dsl_operations array · 1 item
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[3] object · 10 fields
dsl_operations array · 0 items
missing_operations array · 1 item
parameters object · 1 field
data_shapes array · 1 item
[4] object · 10 fields
dsl_operations array · 3 items
missing_operations array · 0 items
parameters object · 1 field
data_shapes array · 1 item
[5] object · 10 fields
dsl_operations array · 3 items
missing_operations array · 0 items
parameters object · 1 field
data_shapes array · 1 item
[6] object · 10 fields
dsl_operations array · 1 item
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[7] object · 10 fields
dsl_operations array · 1 item
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
targets array · 1 item
gaps array · 1 item
capability object · 3 fields
requirements array · 1 item
[0] object · 6 fields
assay_families array · 9 items
gaps array · 0 items
readiness object · 8 fields
axes object · 3 fields
method object · 5 fields
required_operations array · 3 items
missing_operations array · 0 items
capability object · 4 fields
data object · 6 fields
accessions array · 5 items
resolved_accessions array · 5 items
statements array · 1 item
can_run object · 3 fields
dsl_operations array · 3 items
assets array · 2 items
steps array · 1 item
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
gaps array · 0 items
review object · 5 fields
machine_critique object · 5 fields
reader object · 5 fields
evidence_check object · 2 fields
usage object · 3 fields
stages array · 1 item
[0] object · 4 fields
gaps array · 3 items
provenance object · 7 fields
entities array · 3 items
activities array · 3 items
[0] object · 4 fields
used array · 3 items
[0] object · 3 fields
[1] object · 3 fields
[2] object · 3 fields
[1] object · 4 fields
agents array · 2 items
lineage object · 4 fields
gaps array · 15 items
assays array · 2 items
[0] object · 11 fields
required_operations array · 0 items
source_hashes object · 1 field
evidence array · 1 item
[1] object · 11 fields
required_operations array · 3 items
source_hashes object · 1 field
evidence array · 1 item
Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38028591", "kind": "science-record", "revision": 5, "content_hash": "sha256:03237d027e529d7fa43086d861946242be3067251f82fa310d79d608862ab851", "created_at": "2026-11-01T19:02:57Z", "updated_at": "2026-11-01T22:53:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction of predictive model of interstitial fibrosis and tubular atrophy after kidney transplantation with machine learning algorithms.", "doi": "10.3389/fgene.2023.1276963", "pmid": "38028591", "authors": ["Yu Yin", "Congcong Chen", "Dong Zhang", "Qianguang Han", "Zijie Wang", "Zhengkai Huang", "Hao Chen", "Li Sun", "Shuang Fei", "Jun Tao", "Zhijian Han", "Ruoyun Tan", "Min Gu", "Xiaobing Ju"], "journal": "Frontiers in genetics", "year": 2023, "abstract": "Background: Interstitial fibrosis and tubular atrophy (IFTA) are the histopathological manifestations of chronic kidney disease (CKD) and one of the causes of long-term renal loss in transplanted kidneys. Necroptosis as a type of programmed death plays an important role in the development of IFTA, and in the late functional decline and even loss of grafts. In this study, 13 machine learning algorithms were used to construct IFTA diagnostic models based on necroptosis-related genes. Methods: We screened all 162 \"kidney transplant\"-related cohorts in the GEO database and obtained five data sets (training sets: GSE98320 and GSE76882, validation sets: GSE22459 and GSE53605, and survival set: GSE21374). The training set was constructed after removing batch effects of GSE98320 and GSE76882 by using the SVA package. The differentially expressed gene (DEG) analysis was used to identify necroptosis-related DEGs. A total of 13 machine learning algorithms-LASSO, Ridge, Enet, Stepglm, SVM, glmboost, LDA, plsRglm, random forest, GBM, XGBoost, Naive Bayes, and ANNs-were used to construct 114 IFTA diagnostic models, and the optimal models were screened by the AUC values. Post-transplantation patients were then grouped using consensus clustering, and the different subgroups were further explored using PCA, Kaplan-Meier (KM) survival analysis, functional enrichment analysis, CIBERSOFT, and single-sample Gene Set Enrichment Analysis. Results: A total of 55 necroptosis-related DEGs were identified by taking the intersection of the DEGs and necroptosis-related gene sets. Stepglm[both]+RF is the optimal model with an average AUC of 0.822. A total of four molecular subgroups of renal transplantation patients were obtained by clustering, and significant upregulation of fibrosis-related pathways and upregulation of immune response-related pathways were found in the C4 group, which had poor prognosis. Conclusion: Based on the combination of the 13 machine learning algorithms, we developed 114 IFTA classification models. Furthermore, we tested the top model using two independent data sets from GEO.", "claims": [{"id": "claim-method", "statement": "A total of 13 machine learning algorithms—LASSO, Ridge, Enet, Stepglm, SVM, glmboost, LDA, plsRglm, random forest, GBM, XGBoost, Naive Bayes, and ANNs—were used to construct 114 IFTA diagnostic models, and the optimal models were screened by the AUC values.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38028591", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "A total of 13 machine learning algorithms—LASSO, Ridge, Enet, Stepglm, SVM, glmboost, LDA, plsRglm, random forest, GBM, XGBoost, Naive Bayes, and ANNs—were used to construct 114 IFTA diagnostic models, and the optimal models were screened by the AUC values."}], "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:38028591", "locator": "sections[16] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The optimal necroptosis-related IFTA diagnostic model was Stepglm[both]+RF, which achieved an average AUC of 0.822 across the modeling and validation groups.", "kind": "quantitative", "target": {"value": 0.822, "unit": "auc", "tolerance": null, "source": "Stepglm[both]+RF is the optimal model with an average AUC of 0.822."}, "evidence": [{"asset": "source:pmid:38028591", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Stepglm[both]+RF is the optimal model with an average AUC of 0.822."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The optimal necroptosis-related IFTA diagnostic model was Stepglm[both]+RF, which achieved an average AUC of 0.822 across the modeling and validation groups.", "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:38028591"], "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:GSE21374"], "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:GSE21374"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38028591", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2023.1276963", "sha256": "sha256:aff158efb4a744b94df3bb643ce9019b25a907888cc098a41ab0b9323fc8408f", "bytes": 35680, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2023.1276963", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10646529", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE21374", "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=GSE21374", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE21374", "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": ["GSM533921", "GSM533922", "GSM533923", "GSM533924", "GSM533925", "GSM533926", "GSM533927", "GSM533928", "GSM533929", "GSM533930", "GSM533931", "GSM533932", "GSM533933", "GSM533934", "GSM533935", "GSM533936", "GSM533937", "GSM533938", "GSM533939", "GSM533940", "GSM533941", "GSM533942", "GSM533943", "GSM533944", "GSM533945", "GSM533946", "GSM533947", "GSM533948", "GSM533949", "GSM533950", "GSM533951", "GSM533952", "GSM533953", "GSM533954", "GSM533955", "GSM533956", "GSM533957", "GSM533958", "GSM533959", "GSM533960", "GSM533961", "GSM533962", "GSM533963", "GSM533964", "GSM533965", "GSM533966", "GSM533967", "GSM533968", "GSM533969", "GSM533970", "GSM533971", "GSM533972", "GSM533973", "GSM533974", "GSM533975", "GSM533976", "GSM533977", "GSM533978", "GSM533979", "GSM533980", "GSM533981", "GSM533982", "GSM533983", "GSM533984", "GSM533985", "GSM533986", "GSM533987", "GSM533988", "GSM533989", "GSM533990", "GSM533991", "GSM533992", "GSM533993", "GSM533994", "GSM533995", "GSM533996", "GSM533997", "GSM533998", "GSM533999", "GSM534000", "GSM534001", "GSM534002", "GSM534003", "GSM534004", "GSM534005", "GSM534006", "GSM534007", "GSM534008", "GSM534009", "GSM534010", "GSM534011", "GSM534012", "GSM534013", "GSM534014", "GSM534015", "GSM534016", "GSM534017", "GSM534018", "GSM534019", "GSM534020", "GSM534021", "GSM534022", "GSM534023", "GSM534024", "GSM534025", "GSM534026", "GSM534027", "GSM534028", "GSM534029", "GSM534030", "GSM534031", "GSM534032", "GSM534033", "GSM534034", "GSM534035", "GSM534036", "GSM534037", "GSM534038", "GSM534039", "GSM534040", "GSM534041", "GSM534042", "GSM534043", "GSM534044", "GSM534045", "GSM534046", "GSM534047", "GSM534048", "GSM534049", "GSM534050", "GSM534051", "GSM534052", "GSM534053", "GSM534054", "GSM534055", "GSM534056", "GSM534057", "GSM534058", "GSM534059", "GSM534060", "GSM534061", "GSM534062", "GSM534063", "GSM534064", "GSM534065", "GSM534066", "GSM534067", "GSM534068", "GSM534069", "GSM534070", "GSM534071", "GSM534072", "GSM534073", "GSM534074", "GSM534075", "GSM534076", "GSM534077", "GSM534078", "GSM534079", "GSM534080", "GSM534081", "GSM534082", "GSM534083", "GSM534084", "GSM534085", "GSM534086", "GSM534087", "GSM534088", "GSM534089", "GSM534090", "GSM534091", "GSM534092", "GSM534093", "GSM534094", "GSM534095", "GSM534096", "GSM534097", "GSM534098", "GSM534099", "GSM534100", "GSM534101", "GSM534102", "GSM534103", "GSM534104", "GSM534105", "GSM534106", "GSM534107", "GSM534108", "GSM534109", "GSM534110", "GSM534111", "GSM534112", "GSM534113", "GSM534114", "GSM534115", "GSM534116", "GSM534117", "GSM534118", "GSM534119", "GSM534120", "GSM534121", "GSM534122", "GSM534123", "GSM534124", "GSM534125", "GSM534126", "GSM534127", "GSM534128", "GSM534129", "GSM534130", "GSM534131", "GSM534132", "GSM534133", "GSM534134", "GSM534135", "GSM534136", "GSM534137", "GSM534138", "GSM534139", "GSM534140", "GSM534141", "GSM534142", "GSM534143", "GSM534144", "GSM534145", "GSM534146", "GSM534147", "GSM534148", "GSM534149", "GSM534150", "GSM534151", "GSM534152", "GSM534153", "GSM534154", "GSM534155", "GSM534156", "GSM534157", "GSM534158", "GSM534159", "GSM534160", "GSM534161", "GSM534162", "GSM534163", "GSM534164", "GSM534165", "GSM534166", "GSM534167", "GSM534168", "GSM534169", "GSM534170", "GSM534171", "GSM534172", "GSM534173", "GSM534174", "GSM534175", "GSM534176", "GSM534177", "GSM534178", "GSM534179", "GSM534180", "GSM534181", "GSM534182", "GSM534183", "GSM534184", "GSM534185", "GSM534186", "GSM534187", "GSM534188", "GSM534189", "GSM534190", "GSM534191", "GSM534192", "GSM534193", "GSM534194", "GSM534195", "GSM534196", "GSM534197", "GSM534198", "GSM534199", "GSM534200", "GSM534201", "GSM534202"], "sample_count": 282, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Expression data from human renal allograft biopsies", "resolution_checked_at": "2026-11-01T18:06:43Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE22459", "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=GSE22459", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE22459", "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": ["GSM557856", "GSM557857", "GSM557858", "GSM557859", "GSM557860", "GSM557861", "GSM557862", "GSM557863", "GSM557864", "GSM557865", "GSM557866", "GSM557867", "GSM557868", "GSM557869", "GSM557870", "GSM557871", "GSM557872", "GSM557873", "GSM557874", "GSM557875", "GSM557876", "GSM557877", "GSM557878", "GSM557879", "GSM557880", "GSM557881", "GSM557882", "GSM557883", "GSM557884", "GSM557885", "GSM557886", "GSM557887", "GSM557888", "GSM557889", "GSM557890", "GSM557891", "GSM557892", "GSM557893", "GSM557894", "GSM557895", "GSM557896", "GSM557897", "GSM557898", "GSM557899", "GSM557900", "GSM557901", "GSM557902", "GSM557903", "GSM557904", "GSM557905", "GSM557906", "GSM557907", "GSM557908", "GSM557909", "GSM557910", "GSM557911", "GSM557912", "GSM557913", "GSM557914", "GSM557915", "GSM557916", "GSM557917", "GSM557918", "GSM557919", "GSM557920"], "sample_count": 65, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Fibrosis with Inflammation at One Year Predicts Transplant Functional Decline", "resolution_checked_at": "2026-11-01T17:30:42Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE53605", "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=GSE53605", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE53605", "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": ["GSM1296666", "GSM1296667", "GSM1296668", "GSM1296669", "GSM1296670", "GSM1296671", "GSM1296672", "GSM1296673", "GSM1296674", "GSM1296675", "GSM1296676", "GSM1296677", "GSM1296678", "GSM1296679", "GSM1296680", "GSM1296681", "GSM1296682", "GSM1296683", "GSM1296684", "GSM1296685", "GSM1296686", "GSM1296687", "GSM1296688", "GSM1296689", "GSM1296690", "GSM1296691", "GSM1296692", "GSM1296693", "GSM1296694", "GSM1296695", "GSM1296696", "GSM1296697", "GSM1296698", "GSM1296699", "GSM1296700", "GSM1296701", "GSM1296702", "GSM1296703", "GSM1296704", "GSM1296705", "GSM1296706", "GSM1296707", "GSM1296708", "GSM1296709", "GSM1296710", "GSM1296711", "GSM1296712", "GSM1296713", "GSM1296714", "GSM1296715", "GSM1296716", "GSM1296717", "GSM1296718", "GSM1296719", "GSM1296720"], "sample_count": 55, "group_sizes": null, "group_sizes_basis": null, "platform": "571", "taxon": "Homo sapiens", "title": "Molecular Profiles Associated with Calcineurin Inhibitor Toxicity Post-Kidney Transplant: Input to Chronic Allograft Dysfunction", "resolution_checked_at": "2026-11-01T19:09:46Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE76882", "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=GSE76882", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE76882", "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": ["GSM2039481", "GSM2039482", "GSM2039483", "GSM2039484", "GSM2039485", "GSM2039486", "GSM2039487", "GSM2039488", "GSM2039489", "GSM2039490", "GSM2039491", "GSM2039492", "GSM2039493", "GSM2039494", "GSM2039495", "GSM2039496", "GSM2039497", "GSM2039498", "GSM2039499", "GSM2039500", "GSM2039501", "GSM2039502", "GSM2039503", "GSM2039504", "GSM2039505", "GSM2039506", "GSM2039507", "GSM2039508", "GSM2039509", "GSM2039510", "GSM2039511", "GSM2039512", "GSM2039513", "GSM2039514", "GSM2039515", "GSM2039516", "GSM2039517", "GSM2039518", "GSM2039519", "GSM2039520", "GSM2039521", "GSM2039522", "GSM2039523", "GSM2039524", "GSM2039525", "GSM2039526", "GSM2039527", "GSM2039528", "GSM2039529", "GSM2039530", "GSM2039531", "GSM2039532", "GSM2039533", "GSM2039534", "GSM2039535", "GSM2039536", "GSM2039537", "GSM2039538", "GSM2039539", "GSM2039540", "GSM2039541", "GSM2039542", "GSM2039543", "GSM2039544", "GSM2039545", "GSM2039546", "GSM2039547", "GSM2039548", "GSM2039549", "GSM2039550", "GSM2039551", "GSM2039552", "GSM2039553", "GSM2039554", "GSM2039555", "GSM2039556", "GSM2039557", "GSM2039558", "GSM2039559", "GSM2039560", "GSM2039561", "GSM2039562", "GSM2039563", "GSM2039564", "GSM2039565", "GSM2039566", "GSM2039567", "GSM2039568", "GSM2039569", "GSM2039570", "GSM2039571", "GSM2039572", "GSM2039573", "GSM2039574", "GSM2039575", "GSM2039576", "GSM2039577", "GSM2039578", "GSM2039579", "GSM2039580", "GSM2039581", "GSM2039582", "GSM2039583", "GSM2039584", "GSM2039585", "GSM2039586", "GSM2039587", "GSM2039588", "GSM2039589", "GSM2039590", "GSM2039591", "GSM2039592", "GSM2039593", "GSM2039594", "GSM2039595", "GSM2039596", "GSM2039597", "GSM2039598", "GSM2039599", "GSM2039600", "GSM2039601", "GSM2039602", "GSM2039603", "GSM2039604", "GSM2039605", "GSM2039606", "GSM2039607", "GSM2039608", "GSM2039609", "GSM2039610", "GSM2039611", "GSM2039612", "GSM2039613", "GSM2039614", "GSM2039615", "GSM2039616", "GSM2039617", "GSM2039618", "GSM2039619", "GSM2039620", "GSM2039621", "GSM2039622", "GSM2039623", "GSM2039624", "GSM2039625", "GSM2039626", "GSM2039627", "GSM2039628", "GSM2039629", "GSM2039630", "GSM2039631", "GSM2039632", "GSM2039633", "GSM2039634", "GSM2039635", "GSM2039636", "GSM2039637", "GSM2039638", "GSM2039639", "GSM2039640", "GSM2039641", "GSM2039642", "GSM2039643", "GSM2039644", "GSM2039645", "GSM2039646", "GSM2039647", "GSM2039648", "GSM2039649", "GSM2039650", "GSM2039651", "GSM2039652", "GSM2039653", "GSM2039654", "GSM2039655", "GSM2039656", "GSM2039657", "GSM2039658", "GSM2039659", "GSM2039660", "GSM2039661", "GSM2039662", "GSM2039663", "GSM2039664", "GSM2039665", "GSM2039666", "GSM2039667", "GSM2039668", "GSM2039669", "GSM2039670", "GSM2039671", "GSM2039672", "GSM2039673", "GSM2039674", "GSM2039675", "GSM2039676", "GSM2039677", "GSM2039678", "GSM2039679", "GSM2039680", "GSM2039681", "GSM2039682", "GSM2039683", "GSM2039684", "GSM2039685", "GSM2039686", "GSM2039687", "GSM2039688", "GSM2039689", "GSM2039690", "GSM2039691", "GSM2039692", "GSM2039693", "GSM2039694", "GSM2039695", "GSM2039696", "GSM2039697", "GSM2039698", "GSM2039699", "GSM2039700", "GSM2039701", "GSM2039702", "GSM2039703", "GSM2039704", "GSM2039705", "GSM2039706", "GSM2039707", "GSM2039708", "GSM2039709", "GSM2039710", "GSM2039711", "GSM2039712", "GSM2039713", "GSM2039714", "GSM2039715", "GSM2039716", "GSM2039717", "GSM2039718", "GSM2039719", "GSM2039720", "GSM2039721", "GSM2039722", "GSM2039723", "GSM2039724", "GSM2039725", "GSM2039726", "GSM2039727", "GSM2039728", "GSM2039729", "GSM2039730", "GSM2039731", "GSM2039732", "GSM2039733", "GSM2039734", "GSM2039735", "GSM2039736", "GSM2039737", "GSM2039738", "GSM2039739", "GSM2039740", "GSM2039741", "GSM2039742", "GSM2039743", "GSM2039744", "GSM2039745", "GSM2039746", "GSM2039747", "GSM2039748", "GSM2039749", "GSM2039750", "GSM2039751", "GSM2039752", "GSM2039753", "GSM2039754"], "sample_count": 274, "group_sizes": null, "group_sizes_basis": null, "platform": "13158", "taxon": "Homo sapiens", "title": "Gene Expression in Biopsies of Acute Rejection and Interstitial Fibrosis/Tubular Atrophy Reveals Highly Shared Mechanisms that Correlate with Worse Long-term Outcomes", "resolution_checked_at": "2026-11-01T17:30:42Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE98320", "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=GSE98320", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE98320", "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": ["GSM2590943", "GSM2590944", "GSM2590945", "GSM2590946", "GSM2590947", "GSM2590948", "GSM2590949", "GSM2590950", "GSM2590951", "GSM2590952", "GSM2590953", "GSM2590954", "GSM2590955", "GSM2590956", "GSM2590957", "GSM2590958", "GSM2590959", "GSM2590960", "GSM2590961", "GSM2590962", "GSM2590963", "GSM2590964", "GSM2590965", "GSM2590966", "GSM2590967", "GSM2590968", "GSM2590969", "GSM2590970", "GSM2590971", "GSM2590972", "GSM2590973", "GSM2590974", "GSM2590975", "GSM2590976", "GSM2590977", "GSM2590978", "GSM2590979", "GSM2590980", "GSM2590981", "GSM2590982", "GSM2590983", "GSM2590984", "GSM2590985", "GSM2590986", "GSM2590987", "GSM2590988", "GSM2590989", "GSM2590990", "GSM2590991", "GSM2590992", "GSM2590993", "GSM2590994", "GSM2590995", "GSM2590996", "GSM2590997", "GSM2590998", "GSM2590999", "GSM2591000", "GSM2591001", "GSM2591002", "GSM2591003", "GSM2591004", "GSM2591005", "GSM2591006", "GSM2591007", "GSM2591008", "GSM2591009", "GSM2591010", "GSM2591011", "GSM2591012", "GSM2591013", "GSM2591014", "GSM2591015", "GSM2591016", "GSM2591017", "GSM2591018", "GSM2591019", "GSM2591020", "GSM2591021", "GSM2591022", "GSM2591023", "GSM2591024", "GSM2591025", "GSM2591026", "GSM2591027", "GSM2591028", "GSM2591029", "GSM2591030", "GSM2591031", "GSM2591032", "GSM2591033", "GSM2591034", "GSM2591035", "GSM2591036", "GSM2591037", "GSM2591038", "GSM2591039", "GSM2591040", "GSM2591041", "GSM2591042", "GSM2591043", "GSM2591044", "GSM2591045", "GSM2591046", "GSM2591047", "GSM2591048", "GSM2591049", "GSM2591050", "GSM2591051", "GSM2591052", "GSM2591053", "GSM2591054", "GSM2591055", "GSM2591056", "GSM2591057", "GSM2591058", "GSM2591059", "GSM2591060", "GSM2591061", "GSM2591062", "GSM2591063", "GSM2591064", "GSM2591065", "GSM2591066", "GSM2591067", "GSM2591068", "GSM2591069", "GSM2591070", "GSM2591071", "GSM2591072", "GSM2591073", "GSM2591074", "GSM2591075", "GSM2591076", "GSM2591077", "GSM2591078", "GSM2591079", "GSM2591080", "GSM2591081", "GSM2591082", "GSM2591083", "GSM2591084", "GSM2591085", "GSM2591086", "GSM2591087", "GSM2591088", "GSM2591089", "GSM2591090", "GSM2591091", "GSM2591092", "GSM2591093", "GSM2591094", "GSM2591095", "GSM2591096", "GSM2591097", "GSM2591098", "GSM2591099", "GSM2591100", "GSM2591101", "GSM2591102", "GSM2591103", "GSM2591104", "GSM2591105", "GSM2591106", "GSM2591107", "GSM2591108", "GSM2591109", "GSM2591110", "GSM2591111", "GSM2591112", "GSM2591113", "GSM2591114", "GSM2591115", "GSM2591116", "GSM2591117", "GSM2591118", "GSM2591119", "GSM2591120", "GSM2591121", "GSM2591122", "GSM2591123", "GSM2591124", "GSM2591125", "GSM2591126", "GSM2591127", "GSM2591128", "GSM2591129", "GSM2591130", "GSM2591131", "GSM2591132", "GSM2591133", "GSM2591134", "GSM2591135", "GSM2591136", "GSM2591137", "GSM2591138", "GSM2591139", "GSM2591140", "GSM2591141", "GSM2591142", "GSM2591143", "GSM2591144", "GSM2591145", "GSM2591146", "GSM2591147", "GSM2591148", "GSM2591149", "GSM2591150", "GSM2591151", "GSM2591152", "GSM2591153", "GSM2591154", "GSM2591155", "GSM2591156", "GSM2591157", "GSM2591158", "GSM2591159", "GSM2591160", "GSM2591161", "GSM2591162", "GSM2591163", "GSM2591164", "GSM2591165", "GSM2591166", "GSM2591167", "GSM2591168", "GSM2591169", "GSM2591170", "GSM2591171", "GSM2591172", "GSM2591173", "GSM2591174", "GSM2591175", "GSM2591176", "GSM2591177", "GSM2591178", "GSM2591179", "GSM2591180", "GSM2591181", "GSM2591182", "GSM2591183", "GSM2591184", "GSM2591185", "GSM2591186", "GSM2591187", "GSM2591188", "GSM2591189", "GSM2591190", "GSM2591191", "GSM2591192", "GSM2591193", "GSM2591194", "GSM2591195", "GSM2591196", "GSM2591197", "GSM2591198", "GSM2591199", "GSM2591200", "GSM2591201", "GSM2591202", "GSM2591203", "GSM2591204", "GSM2591205", "GSM2591206", "GSM2591207", "GSM2591208", "GSM2591209", "GSM2591210", "GSM2591211", "GSM2591212", "GSM2591213", "GSM2591214", "GSM2591215", "GSM2591216", "GSM2591217", "GSM2591218", "GSM2591219", "GSM2591220", "GSM2591221", "GSM2591222", "GSM2591223", "GSM2591224", "GSM2591225", "GSM2591226", "GSM2591227", "GSM2591228", "GSM2591229", "GSM2591230", "GSM2591231", "GSM2591232", "GSM2591233", "GSM2591234", "GSM2591235", "GSM2591236", "GSM2591237", "GSM2591238", "GSM2591239", "GSM2591240", "GSM2591241", "GSM2591242", "GSM2591243", "GSM2591244", "GSM2591245", "GSM2591246", "GSM2591247", "GSM2591248", "GSM2591249", "GSM2591250", "GSM2591251", "GSM2591252", "GSM2591253", "GSM2591254", "GSM2591255", "GSM2591256", "GSM2591257", "GSM2591258", "GSM2591259", "GSM2591260", "GSM2591261", "GSM2591262", "GSM2591263", "GSM2591264", "GSM2591265", "GSM2591266", "GSM2591267", "GSM2591268", "GSM2591269", "GSM2591270", "GSM2591271", "GSM2591272", "GSM2591273", "GSM2591274", "GSM2591275", "GSM2591276", "GSM2591277", "GSM2591278", "GSM2591279", "GSM2591280", "GSM2591281", "GSM2591282", "GSM2591283", "GSM2591284", "GSM2591285", "GSM2591286", "GSM2591287", "GSM2591288", "GSM2591289", "GSM2591290", "GSM2591291", "GSM2591292", "GSM2591293", "GSM2591294", "GSM2591295", "GSM2591296", "GSM2591297", "GSM2591298", "GSM2591299", "GSM2591300", "GSM2591301", "GSM2591302", "GSM2591303", "GSM2591304", "GSM2591305", "GSM2591306", "GSM2591307", "GSM2591308", "GSM2591309", "GSM2591310", "GSM2591311", "GSM2591312", "GSM2591313", "GSM2591314", "GSM2591315", "GSM2591316", "GSM2591317", "GSM2591318", "GSM2591319", "GSM2591320", "GSM2591321", "GSM2591322", "GSM2591323", "GSM2591324", "GSM2591325", "GSM2591326", "GSM2591327", "GSM2591328", "GSM2591329", "GSM2591330", "GSM2591331", "GSM2591332", "GSM2591333", "GSM2591334", "GSM2591335", "GSM2591336", "GSM2591337", "GSM2591338", "GSM2591339", "GSM2591340", "GSM2591341", "GSM2591342", "GSM2591343", "GSM2591344", "GSM2591345", "GSM2591346", "GSM2591347", "GSM2591348", "GSM2591349", "GSM2591350", "GSM2591351", "GSM2591352", "GSM2591353", "GSM2591354", "GSM2591355", "GSM2591356", "GSM2591357", "GSM2591358", "GSM2591359", "GSM2591360", "GSM2591361", "GSM2591362", "GSM2591363", "GSM2591364", "GSM2591365", "GSM2591366", "GSM2591367", "GSM2591368", "GSM2591369", "GSM2591370", "GSM2591371", "GSM2591372", "GSM2591373", "GSM2591374", "GSM2591375", "GSM2591376", "GSM2591377", "GSM2591378", "GSM2591379", "GSM2591380", "GSM2591381", "GSM2591382", "GSM2591383", "GSM2591384", "GSM2591385", "GSM2591386", "GSM2591387", "GSM2591388", "GSM2591389", "GSM2591390", "GSM2591391", "GSM2591392", "GSM2591393", "GSM2591394", "GSM2591395", "GSM2591396", "GSM2591397", "GSM2591398", "GSM2591399", "GSM2591400", "GSM2591401", "GSM2591402", "GSM2591403", "GSM2591404", "GSM2591405", "GSM2591406", "GSM2591407", "GSM2591408", "GSM2591409", "GSM2591410", "GSM2591411", "GSM2591412", "GSM2591413", "GSM2591414", "GSM2591415", "GSM2591416", "GSM2591417", "GSM2591418", "GSM2591419", "GSM2591420", "GSM2591421", "GSM2591422", "GSM2591423", "GSM2591424", "GSM2591425", "GSM2591426", "GSM2591427", "GSM2591428", "GSM2591429", "GSM2591430", "GSM2591431", "GSM2591432", "GSM2591433", "GSM2591434", "GSM2591435", "GSM2591436", "GSM2591437", "GSM2591438", "GSM2591439", "GSM2591440", "GSM2591441", "GSM2591442", "GSM2591443", "GSM2591444", "GSM2591445", "GSM2591446", "GSM2591447", "GSM2591448", "GSM2591449", "GSM2591450", "GSM2591451", "GSM2591452", "GSM2591453", "GSM2591454", "GSM2591455", "GSM2591456", "GSM2591457", "GSM2591458", "GSM2591459", "GSM2591460", "GSM2591461", "GSM2591462", "GSM2591463", "GSM2591464", "GSM2591465", "GSM2591466", "GSM2591467", "GSM2591468", "GSM2591469", "GSM2591470", "GSM2591471", "GSM2591472", "GSM2591473", "GSM2591474", "GSM2591475", "GSM2591476", "GSM2591477", "GSM2591478", "GSM2591479", "GSM2591480", "GSM2591481", "GSM2591482", "GSM2591483", "GSM2591484", "GSM2591485", "GSM2591486", "GSM2591487", "GSM2591488", "GSM2591489", "GSM2591490", "GSM2591491", "GSM2591492", "GSM2591493", "GSM2591494", "GSM2591495", "GSM2591496", "GSM2591497", "GSM2591498", "GSM2591499", "GSM2591500", "GSM2591501", "GSM2591502", "GSM2591503", "GSM2591504", "GSM2591505", "GSM2591506", "GSM2591507", "GSM2591508", "GSM2591509", "GSM2591510", "GSM2591511", "GSM2591512", "GSM2591513", "GSM2591514", "GSM2591515", "GSM2591516", "GSM2591517", "GSM2591518", "GSM2591519", "GSM2591520", "GSM2591521", "GSM2591522", "GSM2591523", "GSM2591524", "GSM2591525", "GSM2591526", "GSM2591527", "GSM2591528", "GSM2591529", "GSM2591530", "GSM2591531", "GSM2591532", "GSM2591533", "GSM2591534", "GSM2591535", "GSM2591536", "GSM2591537", "GSM2591538", "GSM2591539", "GSM2591540", "GSM2591541", "GSM2591542", "GSM2591543", "GSM2591544", "GSM2591545", "GSM2591546", "GSM2591547", "GSM2591548", "GSM2591549", "GSM2591550", "GSM2591551", "GSM2591552", "GSM2591553", "GSM2591554", "GSM2591555", "GSM2591556", "GSM2591557", "GSM2591558", "GSM2591559", "GSM2591560", "GSM2591561", "GSM2591562", "GSM2591563", "GSM2591564", "GSM2591565", "GSM2591566", "GSM2591567", "GSM2591568", "GSM2591569", "GSM2591570", "GSM2591571", "GSM2591572", "GSM2591573", "GSM2591574", "GSM2591575", "GSM2591576", "GSM2591577", "GSM2591578", "GSM2591579", "GSM2591580", "GSM2591581", "GSM2591582", "GSM2591583", "GSM2591584", "GSM2591585", "GSM2591586", "GSM2591587", "GSM2591588", "GSM2591589", "GSM2591590", "GSM2591591", "GSM2591592", "GSM2591593", "GSM2591594", "GSM2591595", "GSM2591596", "GSM2591597", "GSM2591598", "GSM2591599", "GSM2591600", "GSM2591601", "GSM2591602", "GSM2591603", "GSM2591604", "GSM2591605", "GSM2591606", "GSM2591607", "GSM2591608", "GSM2591609", "GSM2591610", "GSM2591611", "GSM2591612", "GSM2591613", "GSM2591614", "GSM2591615", "GSM2591616", "GSM2591617", "GSM2591618", "GSM2591619", "GSM2591620", "GSM2591621", "GSM2591622", "GSM2591623", "GSM2591624", "GSM2591625", "GSM2591626", "GSM2591627", "GSM2591628", "GSM2591629", "GSM2591630", "GSM2591631", "GSM2591632", "GSM2591633", "GSM2591634", "GSM2591635", "GSM2591636", "GSM2591637", "GSM2591638", "GSM2591639", "GSM2591640", "GSM2591641", "GSM2591642", "GSM2591643", "GSM2591644", "GSM2591645", "GSM2591646", "GSM2591647", "GSM2591650", "GSM2591652", "GSM2591654", "GSM2591657", "GSM2591658", "GSM2591659", "GSM2591660", "GSM2591661", "GSM2591662", "GSM2591663", "GSM2591664", "GSM2591665", "GSM2591666", "GSM2591667", "GSM2591668", "GSM2591669", "GSM2591670", "GSM2591671", "GSM2591672", "GSM2591673", "GSM2591674", "GSM2591675", "GSM2591676", "GSM2591677", "GSM2591678", "GSM2591679", "GSM2591680", "GSM2591681", "GSM2591682", "GSM2591683", "GSM2591684", "GSM2591685", "GSM2591686", "GSM2591687", "GSM2591688", "GSM2591689", "GSM2591690", "GSM2591691", "GSM2591692", "GSM2591693", "GSM2591694", "GSM2591695", "GSM2591696", "GSM2591697", "GSM2591698", "GSM2591699", "GSM2591700", "GSM2591701", "GSM2591702", "GSM2591703", "GSM2591704", "GSM2591705", "GSM2591706", "GSM2591707", "GSM2591708", "GSM2591709", "GSM2591710", "GSM2591711", "GSM2591712", "GSM2591713", "GSM2591714", "GSM2591715", "GSM2591716", "GSM2591717", "GSM2591718", "GSM2591719", "GSM2591720", "GSM2591721", "GSM2591722", "GSM2591723", "GSM2591724", "GSM2591725", "GSM2591726", "GSM2591727", "GSM2591728", "GSM2591729", "GSM2591730", "GSM2591731", "GSM2591732", "GSM2591733", "GSM2591734", "GSM2591735", "GSM2591736", "GSM2591737", "GSM2591738", "GSM2591739", "GSM2591740", "GSM2591741", "GSM2591742", "GSM2591743", "GSM2591744", "GSM2591745", "GSM2591746", "GSM2591747", "GSM2591748", "GSM2591749", "GSM2591750", "GSM2591751", "GSM2591752", "GSM2591753", "GSM2591754", "GSM2591755", "GSM2591756", "GSM2591757", "GSM2591758", "GSM2591759", "GSM2591760", "GSM2591761", "GSM2591762", "GSM2591763", "GSM2591764", "GSM2591765", "GSM2591766", "GSM2591767", "GSM2591768", "GSM2591769", "GSM2591770", "GSM2591771", "GSM2591772", "GSM2591773", "GSM2591774", "GSM2591775", "GSM2591776", "GSM2591777", "GSM2591778", "GSM2591779", "GSM2591780", "GSM2591781", "GSM2591782", "GSM2591783", "GSM2591784", "GSM2591785", "GSM2591786", "GSM2591787", "GSM2591788", "GSM2591789", "GSM2591790", "GSM2591791", "GSM2591792", "GSM2591793", "GSM2591794", "GSM2591795", "GSM2591796", "GSM2591797", "GSM2591798", "GSM2591799", "GSM2591800", "GSM2591801", "GSM2591802", "GSM2591803", "GSM2591804", "GSM2591805", "GSM2591806", "GSM2591807", "GSM2591808", "GSM2591809", "GSM2591810", "GSM2591811", "GSM2591812", "GSM2591813", "GSM2591814", "GSM2591815", "GSM2591816", "GSM2591817", "GSM2591818", "GSM2591819", "GSM2591820", "GSM2591821", "GSM2591822", "GSM2591823", "GSM2591824", "GSM2591825", "GSM2591826", "GSM2591827", "GSM2591828", "GSM2591829", "GSM2591830", "GSM2591831", "GSM2591832", "GSM2591833", "GSM2591834", "GSM2591835", "GSM2591836", "GSM2591837", "GSM2591838", "GSM2591839", "GSM2591840", "GSM2591841", "GSM2591842", "GSM2591843", "GSM2591844", "GSM2591845", "GSM2591846", "GSM2591847", "GSM2591848", "GSM2591849", "GSM2591850", "GSM2591851", "GSM2591852", "GSM2591853", "GSM2591854", "GSM2591855", "GSM2591856", "GSM2591857", "GSM2591858", "GSM2591859", "GSM2591860", "GSM2591861", "GSM2591862", "GSM2591863", "GSM2591864", "GSM2591865", "GSM2591866", "GSM2591867", "GSM2591868", "GSM2591869", "GSM2591870", "GSM2591871", "GSM2591872", "GSM2591873", "GSM2591874", "GSM2591875", "GSM2591876", "GSM2591877", "GSM2591878", "GSM2591879", "GSM2591880", "GSM2591881", "GSM2591882", "GSM2591883", "GSM2591884", "GSM2591885", "GSM2591886", "GSM2591887", "GSM2591888", "GSM2591889", "GSM2591890", "GSM2591891", "GSM2591892", "GSM2591893", "GSM2591894", "GSM2591895", "GSM2591896", "GSM2591897", "GSM2591898", "GSM2591899", "GSM2591900", "GSM2591901", "GSM2591902", "GSM2591903", "GSM2591904", "GSM2591905", "GSM2591906", "GSM2591907", "GSM2591908", "GSM2591909", "GSM2591910", "GSM2591911", "GSM2591912", "GSM2591913", "GSM2591914", "GSM2591915", "GSM2591916", "GSM2591917", "GSM2591918", "GSM2591919", "GSM2591920", "GSM2591921", "GSM2591922", "GSM2591923", "GSM2591924", "GSM2591925", "GSM2591926", "GSM2591927", "GSM2591928", "GSM2591929", "GSM2591930", "GSM2591931", "GSM2591932", "GSM2591933", "GSM2591934", "GSM2591935", "GSM2591936", "GSM2591937", "GSM2591938", "GSM2591939", "GSM2591940", "GSM2591941", "GSM2591942", "GSM2591943", "GSM2591944", "GSM2591945", "GSM2591946", "GSM2591947", "GSM2591948", "GSM2591949", "GSM2591950", "GSM2591951", "GSM2591952", "GSM2591953", "GSM2591954", "GSM2591955", "GSM2591956", "GSM2591957", "GSM2591958", "GSM2591959", "GSM2591960", "GSM2591961", "GSM2591962", "GSM2591963", "GSM2591964", "GSM2591965", "GSM2591966", "GSM2591967", "GSM2591968", "GSM2591969", "GSM2591970", "GSM2591971", "GSM2591972", "GSM2591973", "GSM2591974", "GSM2591975", "GSM2591976", "GSM2591977", "GSM2591978", "GSM2591979", "GSM2591980", "GSM2591981", "GSM2591982", "GSM2591983", "GSM2591984", "GSM2591985", "GSM2591986", "GSM2591987", "GSM2591988", "GSM2591989", "GSM2591990", "GSM2591991", "GSM2591992", "GSM2591993", "GSM2591994", "GSM2591995", "GSM2591996", "GSM2591997", "GSM2591998", "GSM2591999", "GSM2592000", "GSM2592001", "GSM2592002", "GSM2592003", "GSM2592004", "GSM2592005", "GSM2592006", "GSM2592007", "GSM2592008", "GSM2592009", "GSM2592010", "GSM2592011", "GSM2592012", "GSM2592013", "GSM2592014", "GSM2592015", "GSM2592016", "GSM2592017", "GSM2592018", "GSM2592019", "GSM2592020", "GSM2592021", "GSM2592022", "GSM2592023", "GSM2592024", "GSM2592025", "GSM2592026", "GSM2592027", "GSM2592028", "GSM2592029", "GSM2592030", "GSM2592031", "GSM2592032", "GSM2592033", "GSM2592034", "GSM2592035", "GSM2592036", "GSM2592037", "GSM2592038", "GSM2592039", "GSM2592040", "GSM2592041", "GSM2592042", "GSM2592043", "GSM2592044", "GSM2592045", "GSM2592046", "GSM2592047", "GSM2592048", "GSM2592049", "GSM2592050", "GSM2592051", "GSM2592052", "GSM2592053", "GSM2592054", "GSM2592055", "GSM2592056", "GSM2592057", "GSM2592058", "GSM2592059", "GSM2592060", "GSM2592061", "GSM2592062", "GSM2592063", "GSM2592064", "GSM2592065", "GSM2592066", "GSM2592067", "GSM2592068", "GSM2592069", "GSM2592070", "GSM2592071", "GSM2592072", "GSM2592073", "GSM2592074", "GSM2592075", "GSM2592076", "GSM2592077", "GSM2592078", "GSM2592079", "GSM2592080", "GSM2592081", "GSM2592082", "GSM2592083", "GSM2592084", "GSM2592085", "GSM2592086", "GSM2592087", "GSM2592088", "GSM2592089", "GSM2592090", "GSM2592091", "GSM2592092", "GSM2592093", "GSM2592094", "GSM2592095", "GSM2592096", "GSM2592097", "GSM2592098", "GSM2592099", "GSM2592100", "GSM2592101", "GSM2592102", "GSM2592103", "GSM2592104", "GSM2592105", "GSM2592106", "GSM2592107", "GSM2592108", "GSM2592109", "GSM2592110", "GSM2592111", "GSM2592112", "GSM2592113", "GSM2592114", "GSM2592115", "GSM2592116", "GSM2592117", "GSM2592118", "GSM2592119", "GSM2592120", "GSM2592121", "GSM2592122", "GSM2592123", "GSM2592124", "GSM2592125", "GSM2592126", "GSM2592127", "GSM2592128", "GSM2592129", "GSM2592130", "GSM2592131", "GSM2592132", "GSM2592133", "GSM2592134", "GSM2592135", "GSM2592136", "GSM2592137", "GSM2592138", "GSM2592139", "GSM2592140", "GSM2592141", "GSM2592142", "GSM2592143", "GSM2592144", "GSM2592145", "GSM2592146", "GSM2592147", "GSM2592148", "GSM2592149", "GSM2592150", "GSM2592151", "GSM2592152", "GSM2592153", "GSM2592154", "GSM2592155", "GSM2592156"], "sample_count": 1208, "group_sizes": null, "group_sizes_basis": null, "platform": "15207", "taxon": "Homo sapiens", "title": "Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes", "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:GSE21374 not downloaded", "asset:data:geo:GSE21374 licence not read", "asset:data:geo:GSE22459 not downloaded", "asset:data:geo:GSE22459 licence not read", "asset:data:geo:GSE53605 not downloaded", "asset:data:geo:GSE53605 licence not read", "asset:data:geo:GSE76882 not downloaded", "asset:data:geo:GSE76882 licence not read", "asset:data:geo:GSE98320 not downloaded", "asset:data:geo:GSE98320 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "A total of 13 machine learning algorithms—LASSO, Ridge, Enet, Stepglm, SVM, glmboost, LDA, plsRglm, random forest, GBM, XGBoost, Naive Bayes, and ANNs—were used to construct 114 IFTA diagnostic models, and the optimal models were screened by the AUC values."}}, {"id": "method-2", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "Kaplan-Meier"}}, {"id": "method-3", "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": 0.7, "band": "medium", "quote": "PCA"}}, {"id": "method-4", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {"auc": "ls were screened by the AUC values"}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "random forest"}}, {"id": "method-5", "family": "machine_learning", "specific": "support_vector_machine", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {"auc": "ls were screened by the AUC values"}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "SVM"}}, {"id": "method-6", "family": "machine_learning", "specific": "gradient_boosting", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {"auc": "ls were screened by the AUC values"}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "XGBoost"}}, {"id": "method-7", "family": "clustering", "specific": "kmeans", "label": "Clustering", "dsl_operations": ["kmeans_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "k-means"}}, {"id": "method-8", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "GO enrichment"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "cross_validate", "expected_value": 0.822, "unit": "auc", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none."}], "gaps": ["method.reader_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": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE98320", "GSE76882", "GSE22459", "GSE53605", "GSE21374"], "resolved_accessions": ["GSE98320", "GSE76882", "GSE22459", "GSE53605", "GSE21374"], "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 GSE98320, GSE76882, GSE22459, GSE53605, GSE21374 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:38028591", "data:geo:GSE21374"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE21374"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:00:51Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 10463, "completion_tokens": 1812, "cached_tokens": 264}}, "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:38028591", "type": "paper", "label": "pmid:38028591"}, {"id": "source:pmid:38028591", "type": "manuscript", "label": "PMID 38028591 full text"}, {"id": "data:geo:GSE21374", "type": "dataset", "label": "GEO GSE21374"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:38028591", "type": "paper", "label": "pmid:38028591"}, {"id": "source:pmid:38028591", "type": "manuscript", "label": "PMID 38028591 full text"}, {"id": "data:geo:GSE21374", "type": "dataset", "label": "GEO GSE21374"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-38028591", "type": "extraction", "used": ["source:pmid:38028591"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE21374", "type": "accession_resolution", "used": ["data:geo:GSE21374"], "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:38028591", "parent_hash": "sha256:ed20f6ba07f079de7b355e1e1abb20d6529e3cb4540727f2de75b2f8e5257616", "fork_of": null, "generation": 4}, "gaps": ["asset:data:geo:GSE21374 licence not read", "asset:data:geo:GSE21374 not downloaded", "asset:data:geo:GSE22459 licence not read", "asset:data:geo:GSE22459 not downloaded", "asset:data:geo:GSE53605 licence not read", "asset:data:geo:GSE53605 not downloaded", "asset:data:geo:GSE76882 licence not read", "asset:data:geo:GSE76882 not downloaded", "asset:data:geo:GSE98320 licence not read", "asset:data:geo:GSE98320 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": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.69, "evidence": ["n rejection, inflammatory response gene set, apoptosis gene set, TGF-β signal pathway gene set, and"], "order_basis": "paper_text_order:5154", "id": "assay:1"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["A total of 13 machine learning algorithms—LASSO, Ridge, Enet, Stepglm, SVM, glmboost, LDA, plsRglm, random forest, GBM, XGBoost, Naive Bayes, and ANNs—were used to construct 114 IFTA diagnostic models, and the optimal models were screened by the AUC values."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}