← All .science files
pmid:41158409
Development of a prognostic prediction signature for idiopathic pulmonary fibrosis by integrating multiple programmed cell death-related genes and machine learning algorithms.
Jingyang Sun · Rongxuan Jiang · Meng Li · Qian Zhai · Niuniu Dong · Huanhuan Dong · Guangjian Zhang · Yanpeng Zhang
Journal of thoracic disease · 2025 doi:10.21037/jtd-2024-2173
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 · 8 items
claims array · 3 items
[0] object · 6 fields
evidence array · 1 item
[0] object · 3 fields
[1] object · 6 fields
[2] object · 6 fields
evidence array · 2 items
[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 · 8 items
[0] object · 15 fields
structure object · 4 fields
[1] object · 15 fields
structure object · 15 fields
sample_accessions array · 48 items
[2] object · 15 fields
structure object · 15 fields
sample_accessions array · 23 items
[3] object · 15 fields
structure object · 15 fields
sample_accessions array · 582 items
[4] object · 15 fields
structure object · 15 fields
sample_accessions array · 48 items
[5] object · 15 fields
structure object · 15 fields
sample_accessions array · 196 items
[6] object · 15 fields
structure object · 15 fields
sample_accessions array · 321 items
[7] object · 15 fields
structure object · 15 fields
sample_accessions array · 174 items
gaps array · 14 items
analysis object · 4 fields
methods array · 10 items
[0] object · 10 fields
dsl_operations array · 1 item
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 · 0 items
missing_operations array · 1 item
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 · 1 item
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[4] object · 10 fields
dsl_operations array · 3 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[5] object · 10 fields
dsl_operations array · 3 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[6] object · 10 fields
dsl_operations array · 0 items
missing_operations array · 1 item
parameters object · 0 fields
data_shapes array · 1 item
[7] object · 10 fields
dsl_operations array · 3 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[8] object · 10 fields
dsl_operations array · 3 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[9] object · 10 fields
dsl_operations array · 6 items
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
targets array · 0 items
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 · 1 item
missing_operations array · 0 items
capability object · 4 fields
data object · 6 fields
accessions array · 7 items
resolved_accessions array · 7 items
statements array · 1 item
can_run object · 3 fields
dsl_operations array · 1 item
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 · 19 items
assays array · 3 items
[0] object · 11 fields
required_operations array · 0 items
source_hashes object · 1 field
evidence array · 4 items
[1] object · 11 fields
required_operations array · 0 items
source_hashes object · 1 field
evidence array · 4 items
[2] object · 11 fields
required_operations array · 1 item
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:41158409", "kind": "science-record", "revision": 3, "content_hash": "sha256:95f354d07476ad09a4885dd638219d2c0f04dd5cd05163eb20156bcf193891d3", "created_at": "2026-11-01T20:31:12Z", "updated_at": "2026-11-01T22:14:54Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development of a prognostic prediction signature for idiopathic pulmonary fibrosis by integrating multiple programmed cell death-related genes and machine learning algorithms.", "doi": "10.21037/jtd-2024-2173", "pmid": "41158409", "authors": ["Jingyang Sun", "Rongxuan Jiang", "Meng Li", "Qian Zhai", "Niuniu Dong", "Huanhuan Dong", "Guangjian Zhang", "Yanpeng Zhang"], "journal": "Journal of thoracic disease", "year": 2025, "abstract": "Background: Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with a poor prognosis, severely limits patient survival. Various forms of programmed cell death (PCD) play significant roles in the progression of IPF. However, research on the comprehensive effects of different PCD patterns on the prognosis of IPF is still insufficient. This study aims to integrate multiple PCD-related genes to construct a prognostic model for IPF, providing new insights for individualized clinical prognosis assessment and facilitating more precise therapeutic strategies.\nMethods: This study used multiple machine learning analysis methods, multiple datasets, and integrated 16 types of PCD related genes for prognostic signature construction in IPF. The infiltration of different immune cell types in IPF samples was also assessed. The model's predictive performance was evaluated through validation with external datasets. Finally, on the basis of the selected key genes, potential drugs for IPF were identified using the Broad Institute's Connectivity Map databases.\nResults: Differential analysis of five Gene Expression Omnibus (GEO) datasets identified 1,491 PCD-related hub genes. Integration with overall survival (OS) data revealed 241 differentially expressed genes (DEGs) prognostic for IPF. Key PCD types included apoptosis, immunogenic cell death, necrosis, necroptosis, and ferroptosis. Functional enrichment highlighted DEG involvement in cell death/apoptosis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways like apoptosis, tumor necrosis factor (TNF), and NF-κB signaling. Using machine learning and least absolute shrinkage and selection operator (LASSO) regression, seven prognostic biomarkers (TLR2, PDCD1LG2, AKT3, SLC1A4, ANTXR2, HTRA1, and TIMP1) were identified. The derived cell death score (CDS) effectively stratified high- and low-risk patients in training (GSE70866) and validation (GSE70867) cohorts [the area under the curve (AUC) >0.75]. A CDS-based nomogram demonstrated high prognostic accuracy (max AUC =0.907). Immune analysis revealed B cell enrichment in IPF and significant monocyte/activated natural killer (NK) cell infiltration in high-risk patients, correlating with worse OS. Screening identified 25 potential therapeutic small molecules [e.g., acetylcholinesterase inhibitors and histone deacetylase (HDAC)]. Quantitative real-time polymerase chain reaction (qRT-PCR) validation in a bleomycin-induced mouse model confirmed differential expression of the seven model genes, aligning with transcriptomic predictions.\nConclusions: The prognostic signature based on PCD-related genes provides new biomarkers for the prognostic assessment of IPF, and has high predictive accuracy. Additionally, the identified potential drugs offer new directions for the treatment of IPF, laying the foundation for future individualized therapies.", "claims": [{"id": "claim-method", "statement": "In the training cohort of GSE70866, we utilized the ‘limma’ and ‘unicox’ R packages to filter differentially expressed genes (DEGs) and prognostically relevant genes. We then intersected these genes and applied the least absolute shrinkage and selection operator (LASSO)-Cox algorithm to construct the predictive model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41158409", "locator": "sections[5] \"Prognostic model based on multiple machine learning combinations\" paragraph 3", "quote": "In the training cohort of GSE70866, we utilized the ‘limma’ and ‘unicox’ R packages to filter differentially expressed genes (DEGs) and prognostically relevant genes. We then intersected these genes and applied the least absolute shrinkage and selection operator (LASSO)-Cox algorithm to construct the predictive model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2173/dss", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41158409", "locator": "sections[29] \"Data Sharing Statement\" paragraph 2", "quote": "Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2173/dss"}], "state": "complete"}, {"id": "claim-target-result", "statement": "Multiple machine-learning analyses of multiple datasets and 16 PCD patterns, followed by LASSO-Cox regression, produced a seven-gene cell death score that stratified IPF patients into high- and low-risk groups with AUC values above 0.75.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41158409", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "This study used multiple machine learning analysis methods, multiple datasets, and integrated 16 types of PCD related genes for prognostic signature construction in IPF."}, {"asset": "source:pmid:41158409", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The derived cell death score (CDS) effectively stratified high- and low-risk patients in training (GSE70866) and validation (GSE70867) cohorts [the area under the curve (AUC) >0.75]."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Multiple machine-learning analyses of multiple datasets and 16 PCD patterns, followed by LASSO-Cox regression, produced a seven-gene cell death score that stratified IPF patients into high- and low-risk groups with AUC values above 0.75.", "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:41158409"], "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:GSE110147"], "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:GSE110147"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41158409", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/jtd-2024-2173", "sha256": "sha256:6b51d769373dc2519f6937838f3a79ba88179f5dbd07dc234c3825088897e39d", "bytes": 56070, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/jtd-2024-2173", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12557667", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE110147", "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=GSE110147", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE110147", "version": null, "note": "Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2173/dss", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2173/dss", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2978752", "GSM2978753", "GSM2978754", "GSM2978755", "GSM2978756", "GSM2978757", "GSM2978758", "GSM2978759", "GSM2978760", "GSM2978761", "GSM2978762", "GSM2978763", "GSM2978764", "GSM2978765", "GSM2978766", "GSM2978767", "GSM2978768", "GSM2978769", "GSM2978770", "GSM2978771", "GSM2978772", "GSM2978773", "GSM2978774", "GSM2978775", "GSM2978776", "GSM2978777", "GSM2978778", "GSM2978779", "GSM2978780", "GSM2978781", "GSM2978782", "GSM2978783", "GSM2978784", "GSM2978785", "GSM2978786", "GSM2978787", "GSM2978788", "GSM2978789", "GSM2978790", "GSM2978791", "GSM2978792", "GSM2978793", "GSM2978794", "GSM2978795", "GSM2978796", "GSM2978797", "GSM2978798", "GSM2978799"], "sample_count": 48, "group_sizes": null, "group_sizes_basis": null, "platform": "6244", "taxon": "Homo sapiens", "title": "Gene expression profiling of idiopathic pulmonary fibrosis and non-specific interstitial pneumonia", "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:GSE24206", "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=GSE24206", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE24206", "version": null, "note": "Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2173/dss", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2173/dss", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM595407", "GSM595411", "GSM595414", "GSM595416", "GSM595417", "GSM595419", "GSM595421", "GSM595422", "GSM595423", "GSM595424", "GSM595425", "GSM595426", "GSM595427", "GSM595428", "GSM595429", "GSM595432", "GSM595434", "GSM595435", "GSM595437", "GSM595439", "GSM595441", "GSM595443", "GSM595445"], "sample_count": 23, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Validated Gene Expression Signatures of Idiopathic Pulmonary Fibrosis", "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:GSE47460", "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=GSE47460", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE47460", "version": null, "note": "Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2173/dss", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2173/dss", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1149948", "GSM1149949", "GSM1149950", "GSM1149951", "GSM1149952", "GSM1149953", "GSM1149954", "GSM1149955", "GSM1149956", "GSM1149957", "GSM1149958", "GSM1149959", "GSM1149960", "GSM1149961", "GSM1149962", "GSM1149963", "GSM1149964", "GSM1149965", "GSM1149966", "GSM1149967", "GSM1149968", "GSM1149969", "GSM1149970", "GSM1149971", "GSM1149972", "GSM1149973", "GSM1149974", "GSM1149975", "GSM1149976", "GSM1149977", "GSM1149978", "GSM1149979", "GSM1149980", "GSM1149981", "GSM1149982", "GSM1149983", "GSM1149984", "GSM1149985", "GSM1149986", "GSM1149987", "GSM1149988", "GSM1149989", "GSM1149990", "GSM1149991", "GSM1149992", "GSM1149993", "GSM1149994", "GSM1149995", "GSM1149996", "GSM1149997", "GSM1149998", "GSM1149999", "GSM1150000", "GSM1150001", "GSM1150002", "GSM1150003", "GSM1150004", "GSM1150005", "GSM1150006", "GSM1150007", "GSM1150008", "GSM1150009", "GSM1150010", "GSM1150011", "GSM1150012", "GSM1150013", "GSM1150014", "GSM1150015", "GSM1150016", "GSM1150017", "GSM1150018", "GSM1150019", "GSM1150020", "GSM1150021", "GSM1150022", "GSM1150023", "GSM1150024", "GSM1150025", "GSM1150026", "GSM1150027", "GSM1150028", "GSM1150029", "GSM1150030", "GSM1150031", "GSM1150032", "GSM1150033", "GSM1150034", "GSM1150035", "GSM1150036", "GSM1150037", "GSM1150038", "GSM1150039", "GSM1150040", "GSM1150041", "GSM1150042", "GSM1150043", "GSM1150044", "GSM1150045", "GSM1150046", "GSM1150047", "GSM1150048", "GSM1150049", "GSM1150050", "GSM1150051", "GSM1150052", "GSM1150053", "GSM1150054", "GSM1150055", "GSM1150056", "GSM1150057", "GSM1150058", "GSM1150059", "GSM1150060", "GSM1150061", "GSM1150062", "GSM1150063", "GSM1150064", "GSM1150065", "GSM1150066", "GSM1150067", "GSM1150068", "GSM1150069", "GSM1150070", "GSM1150071", "GSM1150072", "GSM1150073", "GSM1150074", "GSM1150075", "GSM1150076", "GSM1150077", "GSM1150078", "GSM1150079", "GSM1150080", "GSM1150081", "GSM1150082", "GSM1150083", "GSM1150084", "GSM1150085", "GSM1150086", "GSM1150087", "GSM1150088", "GSM1150089", "GSM1150090", "GSM1150091", "GSM1150092", "GSM1150093", "GSM1150094", "GSM1150095", "GSM1150096", "GSM1150097", "GSM1150098", "GSM1150099", "GSM1150100", "GSM1150101", "GSM1150102", "GSM1150103", "GSM1150104", "GSM1150105", "GSM1150106", "GSM1150107", "GSM1150108", "GSM1150109", "GSM1150110", "GSM1150111", "GSM1150112", "GSM1150113", "GSM1150114", "GSM1150115", "GSM1150116", "GSM1150117", "GSM1150118", "GSM1150119", "GSM1150120", "GSM1150121", "GSM1150122", "GSM1150123", "GSM1150124", "GSM1150125", "GSM1150126", "GSM1150127", "GSM1150128", "GSM1150129", "GSM1150130", "GSM1150131", "GSM1150132", "GSM1150133", "GSM1150134", "GSM1150135", "GSM1150136", "GSM1150137", "GSM1150138", "GSM1150139", "GSM1150140", "GSM1150141", "GSM1150142", "GSM1150143", "GSM1150144", "GSM1150145", "GSM1150146", "GSM1150147", "GSM1150148", "GSM1150149", "GSM1150150", "GSM1150151", "GSM1150152", "GSM1150153", "GSM1150154", "GSM1150155", "GSM1150156", "GSM1150157", "GSM1150158", "GSM1150159", "GSM1150160", "GSM1150161", "GSM1150162", "GSM1150163", "GSM1150164", "GSM1150165", "GSM1150166", "GSM1150167", "GSM1150168", "GSM1150169", "GSM1150170", "GSM1150171", "GSM1150172", "GSM1150173", "GSM1150174", "GSM1150175", "GSM1150176", "GSM1150177", "GSM1150178", "GSM1150179", "GSM1150180", "GSM1150181", "GSM1150182", "GSM1150183", "GSM1150184", "GSM1150185", "GSM1150186", "GSM1150187", "GSM1150188", "GSM1150189", "GSM1150190", "GSM1150191", "GSM1150192", "GSM1150193", "GSM1150194", "GSM1150195", "GSM1150196", "GSM1150197", "GSM1150198", "GSM1150199", "GSM1150200", "GSM1150201", "GSM1150202", "GSM1150203", "GSM1150204", "GSM1150205", "GSM1150206", "GSM1150207", "GSM1150208", "GSM1150209", "GSM1150210", "GSM1150211", "GSM1150212", "GSM1150213", "GSM1150214", "GSM1150215", "GSM1150216", "GSM1150217", "GSM1150218", "GSM1150219", "GSM1150220", "GSM1150221", "GSM1150222", "GSM1150223", "GSM1150224", "GSM1150225", "GSM1150226", "GSM1150227", "GSM1150228", "GSM1150229", "GSM1150230", "GSM1150231", "GSM1150232", "GSM1150233", "GSM1150234", "GSM1150235", "GSM1150236", "GSM1150237", "GSM1150238", "GSM1150239", "GSM1150240", "GSM1150241", "GSM1150242", "GSM1150243", "GSM1150244", "GSM1150245", "GSM1150246", "GSM1150247", "GSM1150248", "GSM1150249", "GSM1150250", "GSM1150251", "GSM1150252", "GSM1150253", "GSM1150254", "GSM1150255", "GSM1150256", "GSM1150257", "GSM1150258", "GSM1150259", "GSM1150260", "GSM1150261", "GSM1150262", "GSM1150263", "GSM1150264", "GSM1150265", "GSM1150266", "GSM1150267", "GSM1150268", "GSM1150269", "GSM1150270", "GSM1150271", "GSM1150272", "GSM1150273", "GSM1150274", "GSM1150275", "GSM1150276", "GSM1150277", "GSM1150278", "GSM1150279", "GSM1150280", "GSM1150281", "GSM1150282", "GSM1150283", "GSM1150284", "GSM1150285", "GSM1150286", "GSM1150287", "GSM1150288", "GSM1150289", "GSM1150290", "GSM1150291", "GSM1150292", "GSM1150293", "GSM1150294", "GSM1150295", "GSM1150296", "GSM1150297", "GSM1150298", "GSM1150299", "GSM1150300", "GSM1150301", "GSM1150302", "GSM1150303", "GSM1150304", "GSM1150305", "GSM1150306", "GSM1150307", "GSM1150308", "GSM1150309", "GSM1150310", "GSM1150311", "GSM1150312", "GSM1150313", "GSM1150314", "GSM1150315", "GSM1150316", "GSM1150317", "GSM1150318", "GSM1150319", "GSM1150321", "GSM1150331", "GSM1150332", "GSM1150334", "GSM1150336", "GSM1150338", "GSM1150339", "GSM1150341", "GSM1150343", "GSM1150344", "GSM1150346", "GSM1150347", "GSM1150349", "GSM1150351", "GSM1150352", "GSM1150353", "GSM1150354", "GSM1150355", "GSM1150356", "GSM1150357", "GSM1150358", "GSM1150359", "GSM1150360", "GSM1150361", "GSM1150362", "GSM1150363", "GSM1150364", "GSM1150365", "GSM1150366", "GSM1150367", "GSM1150368", "GSM1150369", "GSM1150370", "GSM1150371", "GSM1150372", "GSM1150373", "GSM1150374", "GSM1150375", "GSM1150376", "GSM1150377", "GSM1150378", "GSM1150379", "GSM1150380", "GSM1150381", "GSM1150382", "GSM1150383", "GSM1150384", "GSM1150385", "GSM1150386", "GSM1150387", "GSM1150388", "GSM1150389", "GSM1150390", "GSM1150391", "GSM1150392", "GSM1150393", "GSM1150394", "GSM1150395", "GSM1150396", "GSM1150397", "GSM1150398", "GSM1150399", "GSM1150400", "GSM1150401", "GSM1150402", "GSM1150403", "GSM1150404", "GSM1150405", "GSM1150406", "GSM1150407", "GSM1150408", "GSM1150409", "GSM1150410", "GSM1150411", "GSM1150412", "GSM1150413", "GSM1150414", "GSM1150415", "GSM1150416", "GSM1150417", "GSM1150418", "GSM1150419", "GSM1150420", "GSM1150421", "GSM1150422", "GSM1150423", "GSM1150424", "GSM1150425", "GSM1150426", "GSM1150427", "GSM1150428", "GSM1150429", "GSM1150430", "GSM1150431", "GSM1150432", "GSM1150433", "GSM1150434", "GSM1150435", "GSM1150436", "GSM1150437", "GSM1150438", "GSM1150439", "GSM1150440", "GSM1150441", "GSM1150442", "GSM1150443", "GSM1150444", "GSM1150445", "GSM1150446", "GSM1150447", "GSM1150448", "GSM1150449", "GSM1150450", "GSM1150451", "GSM1150452", "GSM1150453", "GSM1150454", "GSM1150455", "GSM1150456", "GSM1150457", "GSM1150458", "GSM1150459", "GSM1150460", "GSM1150461", "GSM1150462", "GSM1150463", "GSM1150464", "GSM1150465", "GSM1150466", "GSM1150467", "GSM1150468", "GSM1150469", "GSM1150470", "GSM1150471", "GSM1150472", "GSM1150473", "GSM1150474", "GSM1150475", "GSM1150476", "GSM1150477", "GSM1150478", "GSM1150479", "GSM1150480", "GSM1150481", "GSM1150482", "GSM1150483", "GSM1150484", "GSM1150485", "GSM1150486", "GSM1150487", "GSM1150488", "GSM1150489", "GSM1150490", "GSM1150491", "GSM1150492", "GSM1150493", "GSM1150494", "GSM1150495", "GSM1150496", "GSM1150497", "GSM1150498", "GSM1150499", "GSM1150500", "GSM1150501", "GSM1150502", "GSM1150503", "GSM1150504", "GSM1150505", "GSM1150506", "GSM1150507", "GSM1150508", "GSM1150509", "GSM1150510", "GSM1150511", "GSM1150512", "GSM1150513", "GSM1150514", "GSM1150515", "GSM1150516", "GSM1150517", "GSM1150518", "GSM1150519", "GSM1150520", "GSM1150521", "GSM1150522", "GSM1150523", "GSM1150524", "GSM1150525", "GSM1150526", "GSM1150527", "GSM1150528", "GSM1150529", "GSM1150530", "GSM1150531", "GSM1150532", "GSM1150533", "GSM1150534", "GSM1150535", "GSM1150536", "GSM1150537", "GSM1150538", "GSM1150539", "GSM1150540", "GSM1150541", "GSM1150542", "GSM1150543", "GSM1150544", "GSM1150545", "GSM1150546", "GSM1150547"], "sample_count": 582, "group_sizes": null, "group_sizes_basis": null, "platform": "6480;14550", "taxon": "Homo sapiens", "title": "Gene expression profiling of chronic lung disease for the Lung Genomics Research Consortium", "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:GSE53845", "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=GSE53845", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE53845", "version": null, "note": "Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2173/dss", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2173/dss", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1302033", "GSM1302034", "GSM1302035", "GSM1302036", "GSM1302037", "GSM1302038", "GSM1302039", "GSM1302040", "GSM1302041", "GSM1302042", "GSM1302043", "GSM1302044", "GSM1302045", "GSM1302046", "GSM1302047", "GSM1302048", "GSM1302049", "GSM1302050", "GSM1302051", "GSM1302052", "GSM1302053", "GSM1302054", "GSM1302055", "GSM1302056", "GSM1302057", "GSM1302058", "GSM1302059", "GSM1302060", "GSM1302061", "GSM1302062", "GSM1302063", "GSM1302064", "GSM1302065", "GSM1302066", "GSM1302067", "GSM1302068", "GSM1302069", "GSM1302070", "GSM1302071", "GSM1302072", "GSM1302073", "GSM1302074", "GSM1302075", "GSM1302076", "GSM1302077", "GSM1302078", "GSM1302079", "GSM1302080"], "sample_count": 48, "group_sizes": null, "group_sizes_basis": null, "platform": "6480", "taxon": "Homo sapiens", "title": "40 IPF patients and 8 healthy controls", "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:GSE70866", "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=GSE70866", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE70866", "version": null, "note": "Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2173/dss", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2173/dss", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1820719", "GSM1820720", "GSM1820721", "GSM1820722", "GSM1820723", "GSM1820724", "GSM1820725", "GSM1820726", "GSM1820727", "GSM1820728", "GSM1820729", "GSM1820730", "GSM1820731", "GSM1820732", "GSM1820733", "GSM1820734", "GSM1820735", "GSM1820736", "GSM1820737", "GSM1820738", "GSM1820739", "GSM1820740", "GSM1820741", "GSM1820742", "GSM1820743", "GSM1820744", "GSM1820745", "GSM1820746", "GSM1820747", "GSM1820748", "GSM1820749", "GSM1820750", "GSM1820751", "GSM1820752", "GSM1820753", "GSM1820754", "GSM1820755", "GSM1820756", "GSM1820757", "GSM1820758", "GSM1820759", "GSM1820760", "GSM1820761", "GSM1820762", "GSM1820763", "GSM1820764", "GSM1820765", "GSM1820766", "GSM1820767", "GSM1820768", "GSM1820769", "GSM1820770", "GSM1820771", "GSM1820772", "GSM1820773", "GSM1820774", "GSM1820775", "GSM1820776", "GSM1820777", "GSM1820778", "GSM1820779", "GSM1820780", "GSM1820781", "GSM1820782", "GSM1820783", "GSM1820784", "GSM1820785", "GSM1820786", "GSM1820787", "GSM1820788", "GSM1820789", "GSM1820790", "GSM1820791", "GSM1820792", "GSM1820793", "GSM1820794", "GSM1820795", "GSM1820796", "GSM1820797", "GSM1820798", "GSM1820799", "GSM1820800", "GSM1820801", "GSM1820802", "GSM1820803", "GSM1820804", "GSM1820805", "GSM1820806", "GSM1820807", "GSM1820808", "GSM1820809", "GSM1820810", "GSM1820811", "GSM1820812", "GSM1820813", "GSM1820814", "GSM1820815", "GSM1820816", "GSM1820817", "GSM1820818", "GSM1820819", "GSM1820820", "GSM1820821", "GSM1820822", "GSM1820823", "GSM1820824", "GSM1820825", "GSM1820826", "GSM1820827", "GSM1820828", "GSM1820829", "GSM1820830", "GSM1820831", "GSM1820832", "GSM1820833", "GSM1820834", "GSM1820835", "GSM1820836", "GSM1820837", "GSM1820838", "GSM1820839", "GSM1820840", "GSM1820841", "GSM1820842", "GSM1820843", "GSM1820844", "GSM1820845", "GSM1820846", "GSM1820847", "GSM1820848", "GSM1820849", "GSM1820850", "GSM1820851", "GSM1820852", "GSM1820853", "GSM1820854", "GSM1820855", "GSM1820856", "GSM1820857", "GSM1820858", "GSM1820859", "GSM1820860", "GSM1820861", "GSM1820862", "GSM1820863", "GSM1820864", "GSM1820865", "GSM1820866", "GSM1820867", "GSM1820868", "GSM1820869", "GSM1820870", "GSM1820871", "GSM1820872", "GSM1820873", "GSM1820874", "GSM1820875", "GSM1820876", "GSM1820877", "GSM1820878", "GSM1820879", "GSM1820880", "GSM1820881", "GSM1820882", "GSM1820883", "GSM1820884", "GSM1820885", "GSM1820886", "GSM1820887", "GSM1820888", "GSM1820889", "GSM1820890", "GSM1820891", "GSM1820892", "GSM1820893", "GSM1820894", "GSM1820895", "GSM1820896", "GSM1820897", "GSM1820898", "GSM1820899", "GSM1820900", "GSM1820901", "GSM1820902", "GSM1820903", "GSM1820904", "GSM1820905", "GSM1820906", "GSM1820907", "GSM1820908", "GSM1820909", "GSM1820910", "GSM1820911", "GSM1820912", "GSM1820913", "GSM1820914"], "sample_count": 196, "group_sizes": null, "group_sizes_basis": null, "platform": "17077;14550", "taxon": "Homo sapiens", "title": "BAL cell gene expression is predictive of Mortality in Idiopathic Pulmonary Fibrosis and enriched for Genes of Airway Basal Cells (II)", "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:GSE70867", "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=GSE70867", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE70867", "version": null, "note": "Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2173/dss", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2173/dss", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1820697", "GSM1820698", "GSM1820699", "GSM1820700", "GSM1820701", "GSM1820702", "GSM1820703", "GSM1820704", "GSM1820705", "GSM1820706", "GSM1820707", "GSM1820708", "GSM1820709", "GSM1820710", "GSM1820711", "GSM1820712", "GSM1820713", "GSM1820714", "GSM1820715", "GSM1820716", "GSM1820717", "GSM1820718", "GSM1820719", "GSM1820720", "GSM1820721", "GSM1820722", "GSM1820723", "GSM1820724", "GSM1820725", "GSM1820726", "GSM1820727", "GSM1820728", "GSM1820729", "GSM1820730", "GSM1820731", "GSM1820732", "GSM1820733", "GSM1820734", "GSM1820735", "GSM1820736", "GSM1820737", "GSM1820738", "GSM1820739", "GSM1820740", "GSM1820741", "GSM1820742", "GSM1820743", "GSM1820744", "GSM1820745", "GSM1820746", "GSM1820747", "GSM1820748", "GSM1820749", "GSM1820750", "GSM1820751", "GSM1820752", "GSM1820753", "GSM1820754", "GSM1820755", "GSM1820756", "GSM1820757", "GSM1820758", "GSM1820759", "GSM1820760", "GSM1820761", "GSM1820762", "GSM1820763", "GSM1820764", "GSM1820765", "GSM1820766", "GSM1820767", "GSM1820768", "GSM1820769", "GSM1820770", "GSM1820771", "GSM1820772", "GSM1820773", "GSM1820774", "GSM1820775", "GSM1820776", "GSM1820777", "GSM1820778", "GSM1820779", "GSM1820780", "GSM1820781", "GSM1820782", "GSM1820783", "GSM1820784", "GSM1820785", "GSM1820786", "GSM1820787", "GSM1820788", "GSM1820789", "GSM1820790", "GSM1820791", "GSM1820792", "GSM1820793", "GSM1820794", "GSM1820795", "GSM1820796", "GSM1820797", "GSM1820798", "GSM1820799", "GSM1820800", "GSM1820801", "GSM1820802", "GSM1820803", "GSM1820804", "GSM1820805", "GSM1820806", "GSM1820807", "GSM1820808", "GSM1820809", "GSM1820810", "GSM1820811", "GSM1820812", "GSM1820813", "GSM1820814", "GSM1820815", "GSM1820816", "GSM1820817", "GSM1820818", "GSM1820819", "GSM1820820", "GSM1820821", "GSM1820822", "GSM1820823", "GSM1820824", "GSM1820825", "GSM1820826", "GSM1820827", "GSM1820828", "GSM1820829", "GSM1820830", "GSM1820831", "GSM1820832", "GSM1820833", "GSM1820834", "GSM1820835", "GSM1820836", "GSM1820837", "GSM1820838", "GSM1820839", "GSM1820840", "GSM1820841", "GSM1820842", "GSM1820843", "GSM1820844", "GSM1820845", "GSM1820846", "GSM1820847", "GSM1820848", "GSM1820849", "GSM1820850", "GSM1820851", "GSM1820852", "GSM1820853", "GSM1820854", "GSM1820855", "GSM1820856", "GSM1820857", "GSM1820858", "GSM1820859", "GSM1820860", "GSM1820861", "GSM1820862", "GSM1820863", "GSM1820864", "GSM1820865", "GSM1820866", "GSM1820867", "GSM1820868", "GSM1820869", "GSM1820870", "GSM1820871", "GSM1820872", "GSM1820873", "GSM1820874", "GSM1820875", "GSM1820876", "GSM1820877", "GSM1820878", "GSM1820879", "GSM1820880", "GSM1820881", "GSM1820882", "GSM1820883", "GSM1820884", "GSM1820885", "GSM1820886", "GSM1820887", "GSM1820888", "GSM1820889", "GSM1820890", "GSM1820891", "GSM1820892", "GSM1820893", "GSM1820894", "GSM1820895", "GSM1820896", "GSM1820897", "GSM1820898", "GSM1820899", "GSM1820900", "GSM1820901", "GSM1820902", "GSM1820903", "GSM1820904", "GSM1820905", "GSM1820906", "GSM1820907", "GSM1820908", "GSM1820909", "GSM1820910", "GSM1820911", "GSM1820912", "GSM1820913", "GSM1820914", "GSM1892482", "GSM1892483", "GSM1892484", "GSM1892485", "GSM1892486", "GSM1892487", "GSM1892488", "GSM1892489", "GSM1892490", "GSM1892491", "GSM1892492", "GSM1892493", "GSM1892494", "GSM1892495", "GSM1892496", "GSM1892497", "GSM1892498", "GSM1892499", "GSM1892500", "GSM1892501", "GSM1892502", "GSM1892503", "GSM1892504", "GSM1892505", "GSM1892506", "GSM1892507", "GSM1892508", "GSM1892509", "GSM1892510", "GSM1892511", "GSM1892512", "GSM1892513", "GSM1892514", "GSM1892515", "GSM1892516", "GSM1892517", "GSM1892518", "GSM1892519", "GSM1892520", "GSM1892521", "GSM1892522", "GSM1892523", "GSM1892524", "GSM1892525", "GSM1892526", "GSM1892527", "GSM1892528", "GSM1892529", "GSM1892530", "GSM1892531", "GSM1892532", "GSM1892533", "GSM1892534", "GSM1892535", "GSM1892536", "GSM1892537", "GSM1892538", "GSM1892539", "GSM1892540", "GSM1892541", "GSM1892542", "GSM1892543", "GSM1892544", "GSM1892545", "GSM1892546", "GSM1892547", "GSM1892548", "GSM1892549", "GSM1892550", "GSM1892551", "GSM1892552", "GSM1892553", "GSM1892554", "GSM1892555", "GSM1892556", "GSM1892557", "GSM1892558", "GSM1892559", "GSM1892560", "GSM1892561", "GSM1892562", "GSM1892563", "GSM1892564", "GSM1892565", "GSM1892566", "GSM1892567", "GSM1892568", "GSM1892569", "GSM1892570", "GSM1892571", "GSM1892572", "GSM1892573", "GSM1892574", "GSM1892575", "GSM1892576", "GSM1892577", "GSM1892578", "GSM1892579", "GSM1892580", "GSM1892581", "GSM1892582", "GSM1892583", "GSM1892584"], "sample_count": 321, "group_sizes": null, "group_sizes_basis": null, "platform": "17077;14550;570;10739", "taxon": "Homo sapiens", "title": "BAL cell gene expression is predictive of Mortality in Idiopathic Pulmonary Fibrosis and enriched for Genes of Airway Basal 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:GSE93606", "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=GSE93606", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE93606", "version": null, "note": "Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2173/dss", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2173/dss", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2458563", "GSM2458564", "GSM2458565", "GSM2458566", "GSM2458567", "GSM2458568", "GSM2458569", "GSM2458570", "GSM2458571", "GSM2458572", "GSM2458573", "GSM2458574", "GSM2458575", "GSM2458576", "GSM2458577", "GSM2458578", "GSM2458579", "GSM2458580", "GSM2458581", "GSM2458582", "GSM2458583", "GSM2458584", "GSM2458585", "GSM2458586", "GSM2458587", "GSM2458588", "GSM2458589", "GSM2458590", "GSM2458591", "GSM2458592", "GSM2458593", "GSM2458594", "GSM2458595", "GSM2458596", "GSM2458597", "GSM2458598", "GSM2458599", "GSM2458600", "GSM2458601", "GSM2458602", "GSM2458603", "GSM2458604", "GSM2458605", "GSM2458606", "GSM2458607", "GSM2458608", "GSM2458609", "GSM2458610", "GSM2458611", "GSM2458612", "GSM2458613", "GSM2458614", "GSM2458615", "GSM2458616", "GSM2458617", "GSM2458618", "GSM2458619", "GSM2458620", "GSM2458621", "GSM2458622", "GSM2458623", "GSM2458624", "GSM2458625", "GSM2458626", "GSM2458627", "GSM2458628", "GSM2458629", "GSM2458630", "GSM2458631", "GSM2458632", "GSM2458633", "GSM2458634", "GSM2458635", "GSM2458636", "GSM2458637", "GSM2458638", "GSM2458639", "GSM2458640", "GSM2458641", "GSM2458642", "GSM2458643", "GSM2458644", "GSM2458645", "GSM2458646", "GSM2458647", "GSM2458648", "GSM2458649", "GSM2458650", "GSM2458651", "GSM2458652", "GSM2458653", "GSM2458654", "GSM2458655", "GSM2458656", "GSM2458657", "GSM2458658", "GSM2458659", "GSM2458660", "GSM2458661", "GSM2458662", "GSM2458663", "GSM2458664", "GSM2458665", "GSM2458666", "GSM2458667", "GSM2458668", "GSM2458669", "GSM2458670", "GSM2458671", "GSM2458672", "GSM2458673", "GSM2458674", "GSM2458675", "GSM2458676", "GSM2458677", "GSM2458678", "GSM2458679", "GSM2458680", "GSM2458681", "GSM2458682", "GSM2458683", "GSM2458684", "GSM2458685", "GSM2458686", "GSM2458687", "GSM2458688", "GSM2458689", "GSM2458690", "GSM2458691", "GSM2458692", "GSM2458693", "GSM2458694", "GSM2458695", "GSM2458696", "GSM2458697", "GSM2458698", "GSM2458699", "GSM2458700", "GSM2458701", "GSM2458702", "GSM2458703", "GSM2458704", "GSM2458705", "GSM2458706", "GSM2458707", "GSM2458708", "GSM2458709", "GSM2458710", "GSM2458711", "GSM2458712", "GSM2458713", "GSM2458714", "GSM2458715", "GSM2458716", "GSM2458717", "GSM2458718", "GSM2458719", "GSM2458720", "GSM2458721", "GSM2458722", "GSM2458723", "GSM2458724", "GSM2458725", "GSM2458726", "GSM2458727", "GSM2458728", "GSM2458729", "GSM2458730", "GSM2458731", "GSM2458732", "GSM2458733", "GSM2458734", "GSM2458735", "GSM2458736"], "sample_count": 174, "group_sizes": null, "group_sizes_basis": null, "platform": "11532", "taxon": "Homo sapiens", "title": "Host-Microbial interactions in Idiopathic Pulmonary Fibrosis", "resolution_checked_at": "2026-11-01T17:30:42Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE110147 not downloaded", "asset:data:geo:GSE110147 licence not read", "asset:data:geo:GSE24206 not downloaded", "asset:data:geo:GSE24206 licence not read", "asset:data:geo:GSE47460 not downloaded", "asset:data:geo:GSE47460 licence not read", "asset:data:geo:GSE53845 not downloaded", "asset:data:geo:GSE53845 licence not read", "asset:data:geo:GSE70866 not downloaded", "asset:data:geo:GSE70866 licence not read", "asset:data:geo:GSE70867 not downloaded", "asset:data:geo:GSE70867 licence not read", "asset:data:geo:GSE93606 not downloaded", "asset:data:geo:GSE93606 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "In the training cohort of GSE70866, we utilized the ‘limma’ and ‘unicox’ R packages to filter differentially expressed genes (DEGs) and prognostically relevant genes. We then intersected these genes and applied the least absolute shrinkage and selection operator (LASSO)-Cox algorithm to construct the predictive model."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "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": 0.8, "band": "high", "quote": "Kaplan-Meier"}}, {"id": "method-4", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "log-rank"}}, {"id": "method-5", "family": "differential_expression", "specific": "deseq2", "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": 0.8, "band": "high", "quote": "DESeq2"}}, {"id": "method-6", "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": 0.8, "band": "high", "quote": "limma"}}, {"id": "method-7", "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": 0.8, "band": "high", "quote": "random forest"}}, {"id": "method-8", "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": 0.8, "band": "high", "quote": "SVM"}}, {"id": "method-9", "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": "gradient boosting"}}, {"id": "method-10", "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 analysis"}}], "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": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "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": ["GSE24206", "GSE47460", "GSE53845", "GSE93606", "GSE110147", "GSE70866", "GSE70867"], "resolved_accessions": ["GSE24206", "GSE47460", "GSE53845", "GSE93606", "GSE110147", "GSE70866", "GSE70867"], "statements": ["Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2173/dss"], "basis": "The paper names GSE24206, GSE47460, GSE53845, GSE93606, GSE110147, GSE70866, GSE70867 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": ["cox_ph"], "assets": ["source:pmid:41158409", "data:geo:GSE110147"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE110147"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T20:04:39Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 15322, "completion_tokens": 2562, "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:41158409", "type": "paper", "label": "pmid:41158409"}, {"id": "source:pmid:41158409", "type": "manuscript", "label": "PMID 41158409 full text"}, {"id": "data:geo:GSE110147", "type": "dataset", "label": "GEO GSE110147"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:41158409", "type": "paper", "label": "pmid:41158409"}, {"id": "source:pmid:41158409", "type": "manuscript", "label": "PMID 41158409 full text"}, {"id": "data:geo:GSE110147", "type": "dataset", "label": "GEO GSE110147"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-41158409", "type": "extraction", "used": ["source:pmid:41158409"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE110147", "type": "accession_resolution", "used": ["data:geo:GSE110147"], "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:41158409", "parent_hash": "sha256:98b7c8fbf87cb3c8147ab3a022922096941200a6ff23daf34810e1d1345c582b", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE110147 licence not read", "asset:data:geo:GSE110147 not downloaded", "asset:data:geo:GSE24206 licence not read", "asset:data:geo:GSE24206 not downloaded", "asset:data:geo:GSE47460 licence not read", "asset:data:geo:GSE47460 not downloaded", "asset:data:geo:GSE53845 licence not read", "asset:data:geo:GSE53845 not downloaded", "asset:data:geo:GSE70866 licence not read", "asset:data:geo:GSE70866 not downloaded", "asset:data:geo:GSE70867 licence not read", "asset:data:geo:GSE70867 not downloaded", "asset:data:geo:GSE93606 licence not read", "asset:data:geo:GSE93606 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": 0.99, "evidence": [") prognostic for IPF. Key PCD types included apoptosis, immunogenic cell death, necrosis, necroptos", "nt highlighted DEG involvement in cell death/apoptosis and the Kyoto Encyclopedia of Genes and Geno", "ia of Genes and Genomes (KEGG) pathways like apoptosis, tumor necrosis factor (TNF), and NF-κB sign", "ne may participate in multiple forms of PCD, apoptosis was found to potentially have the greatest i"], "order_basis": "paper_text_order:1466", "id": "assay:1"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["(qRT-PCR) validation in a bleomycin-induced mouse model confirmed differential expression of the sev", "(qRT-PCR) validation in a bleomycin-induced mouse model confirmed differential expression of t", "cific Pathogen-Free (SPF) grade male C57Bl/6 mice were raised in the Experimental Animal Center of", "umidity of 50%, and a 12-h night/day cycle). Mice were randomly assigned to either the control grou"], "order_basis": "paper_text_order:2603", "id": "assay:2"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["In the training cohort of GSE70866, we utilized the ‘limma’ and ‘unicox’ R packages to filter differentially expressed genes (DEGs) and prognostically relevant genes. We then intersected these genes and applied the least absolute shrinkage and selection operator (LASSO)-Cox algorithm to construct the predictive model."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}