Bioinformatics analysis and machine learning approach applied to the identification of novel key genes involved in non-alcoholic fatty liver disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37993474", "kind": "science-record", "revision": 3, "content_hash": "sha256:f1de3aadaee4ac5c140533a4493ddcb813cd8c7d493eb02485363acab242513f", "created_at": "2026-11-01T19:02:55Z", "updated_at": "2026-11-01T22:10:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics analysis and machine learning approach applied to the identification of novel key genes involved in non-alcoholic fatty liver disease.", "doi": "10.1038/s41598-023-46711-x", "pmid": "37993474", "authors": ["Elham Nazari", "Ghazaleh Khalili-Tanha", "Alireza Asadnia", "Ghazaleh Pourali", "Mina Maftooh", "Majid Khazaei", "Mohammadreza Nasiri", "Seyed Mahdi Hassanian", "Majid Ghayour-Mobarhan", "Gordon A Ferns", "Mohammad Ali Kiani", "Amir Avan"], "journal": "Scientific reports", "year": 2023, "abstract": "Non-alcoholic fatty liver disease (NAFLD) comprises a range of chronic liver diseases that result from the accumulation of excess triglycerides in the liver, and which, in its early phases, is categorized NAFLD, or hepato-steatosis with pure fatty liver. The mortality rate of non-alcoholic steatohepatitis (NASH) is more than NAFLD; therefore, diagnosing the disease in its early stages may decrease liver damage and increase the survival rate. In the current study, we screened the gene expression data of NAFLD patients and control samples from the public dataset GEO to detect DEGs. Then, the correlation betweenbetween the top selected DEGs and clinical data was evaluated. In the present study, two GEO datasets (GSE48452, GSE126848) were downloaded. The dysregulated expressed genes (DEGs) were identified by machine learning methods (Penalize regression models). Then, the shared DEGs between the two training datasets were validated using validation datasets. ROC-curve analysis was used to identify diagnostic markers. R software analyzed the interactions between DEGs, clinical data, and fatty liver. Ten novel genes, including ABCF1, SART3, APC5, NONO, KAT7, ZPR1, RABGAP1, SLC7A8, SPAG9, and KAT6A were found to have a differential expression between NAFLD and healthy individuals. Based on validation results and ROC analysis, NR4A2 and IGFBP1b were identified as diagnostic markers. These key genes may be predictive markers for the development of fatty liver. It is recommended that these key genes are assessed further as possible predictive markers during the development of fatty liver.", "claims": [{"id": "claim-method", "statement": "Then Penalize machine learning technique was used to detect the most important common genes between two data sets. The receiver operating characteristic (ROC) curve was performed to evaluate the efficacy of the diagnostic model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37993474", "locator": "sections[21] \"Identification of dysregulate expression genes (DEGs)\" paragraph 2", "quote": "Then Penalize machine learning technique was used to detect the most important common genes between two data sets."}, {"asset": "source:pmid:37993474", "locator": "sections[14] \"Combine ROC curve\" paragraph 2", "quote": "The receiver operating characteristic (ROC) curve was performed to evaluate the efficacy of the diagnostic model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analysed during the current study are available in the GEO repository, https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSE48452 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126848.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37993474", "locator": "sections[35] \"notes\" paragraph 1", "quote": "The datasets generated and/or analysed during the current study are available in the GEO repository, https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSE48452 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126848."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Our finding showed that NR4A2 alone (AUC of 0.92, 95% CI with a sensitivity of 1.00and specificity of 0.71), and also, its combination with ZEB2 (AUC of 0.92, 95% CI with a sensitivity of 0.90 and specificity of 0.85) had the highest rank of ROC analysis and can be considered as diagnostic markers (Fig. S5 and Table S1).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Our finding showed that NR4A2 alone (AUC of 0.92, 95% CI with a sensitivity of 1.00and specificity of 0.71), and also, its combination with ZEB2 (AUC of 0.92, 95% CI with a sensitivity of 0.90 and specificity of 0.85) had the highest rank of ROC analysis and can be considered as diagnostic markers (Fig. S5 and Table S1)."}, "evidence": [{"asset": "source:pmid:37993474", "locator": "sections[26] \"ROC curve for identification of diagnostic markers\" paragraph 2", "quote": "Our finding showed that NR4A2 alone (AUC of 0.92, 95% CI with a sensitivity of 1.00and specificity of 0.71), and also, its combination with ZEB2 (AUC of 0.92, 95% CI with a sensitivity of 0.90 and specificity of 0.85) had the highest rank of ROC analysis and can be considered as diagnostic markers (Fig. S5 and Table S1)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Our finding showed that NR4A2 alone (AUC of 0.92, 95% CI with a sensitivity of 1.00and specificity of 0.71), and also, its combination with ZEB2 (AUC of 0.92, 95% CI with a sensitivity of 0.90 and specificity of 0.85) had the highest rank of ROC analysis and can be considered as diagnostic markers (Fig. S5 and Table S1).", "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:37993474"], "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:GSE126848"], "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:GSE126848"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37993474", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-023-46711-x", "sha256": "sha256:700e2a6d0b5f181dd9ba15794a644cfbd336fa77835a47e69b5d53513ca52114", "bytes": 36446, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-023-46711-x", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10665370", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE126848", "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=GSE126848", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE126848", "version": null, "note": "The datasets generated and/or analysed during the current study are available in the GEO repository, https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSE48452 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126848.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and/or analysed during the current study are available in the GEO repository, https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSE48452 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126848.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM3615293", "GSM3615294", "GSM3615295", "GSM3615296", "GSM3615297", "GSM3615298", "GSM3615299", "GSM3615300", "GSM3615301", "GSM3615302", "GSM3615303", "GSM3615304", "GSM3615305", "GSM3615306", "GSM3615307", "GSM3615308", "GSM3615309", "GSM3615310", "GSM3615311", "GSM3615312", "GSM3615313", "GSM3615314", "GSM3615315", "GSM3615316", "GSM3615317", "GSM3615318", "GSM3615319", "GSM3615320", "GSM3615321", "GSM3615322", "GSM3615323", "GSM3615324", "GSM3615325", "GSM3615326", "GSM3615327", "GSM3615328", "GSM3615329", "GSM3615330", "GSM3615331", "GSM3615332", "GSM3615333", "GSM3615334", "GSM3615335", "GSM3615336", "GSM3615337", "GSM3615338", "GSM3615339", "GSM3615340", "GSM3615341", "GSM3615342", "GSM3615343", "GSM3615344", "GSM3615345", "GSM3615346", "GSM3615347", "GSM3615348", "GSM3615349"], "sample_count": 57, "group_sizes": null, "group_sizes_basis": null, "platform": "18573", "taxon": "Homo sapiens", "title": "HEPATIC TRANSCRIPTOME SIGNATURES IN PATIENTS WITH VARYING DEGREES OF NON-ALCOHOLIC FATTY LIVER DISEASE COMPARED TO HEALTHY NORMAL-WEIGHT INDIVIDUALS", "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:GSE48452", "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=GSE48452", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE48452", "version": null, "note": "The datasets generated and/or analysed during the current study are available in the GEO repository, https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSE48452 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126848.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and/or analysed during the current study are available in the GEO repository, https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSE48452 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126848.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1178970", "GSM1178971", "GSM1178972", "GSM1178973", "GSM1178974", "GSM1178975", "GSM1178976", "GSM1178977", "GSM1178978", "GSM1178979", "GSM1178980", "GSM1178981", "GSM1178982", "GSM1178983", "GSM1178984", "GSM1178985", "GSM1178986", "GSM1178987", "GSM1178988", "GSM1178989", "GSM1178990", "GSM1178991", "GSM1178992", "GSM1178993", "GSM1178994", "GSM1178995", "GSM1178996", "GSM1178997", "GSM1178998", "GSM1178999", "GSM1179000", "GSM1179001", "GSM1179002", "GSM1179003", "GSM1179004", "GSM1179005", "GSM1179006", "GSM1179007", "GSM1179008", "GSM1179009", "GSM1179010", "GSM1179011", "GSM1179012", "GSM1179013", "GSM1179014", "GSM1179015", "GSM1179016", "GSM1179017", "GSM1179018", "GSM1179019", "GSM1179020", "GSM1179021", "GSM1179022", "GSM1179023", "GSM1179024", "GSM1179025", "GSM1179026", "GSM1179027", "GSM1179028", "GSM1179029", "GSM1179030", "GSM1179031", "GSM1179032", "GSM1179033", "GSM1179034", "GSM1179035", "GSM1179036", "GSM1179037", "GSM1179038", "GSM1179039", "GSM1179040", "GSM1179041", "GSM1179042"], "sample_count": 73, "group_sizes": null, "group_sizes_basis": null, "platform": "11532", "taxon": "Homo sapiens", "title": "Human liver biopsy of different phases from control to NASH", "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:GSE63067", "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=GSE63067", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE63067", "version": null, "note": "The datasets generated and/or analysed during the current study are available in the GEO repository, https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSE48452 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126848.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and/or analysed during the current study are available in the GEO repository, https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSE48452 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126848.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1539877", "GSM1539878", "GSM1539879", "GSM1539880", "GSM1539881", "GSM1539882", "GSM1539883", "GSM1539884", "GSM1539885", "GSM1539886", "GSM1539887", "GSM1539888", "GSM1539889", "GSM1539890", "GSM1539891", "GSM1539892", "GSM1539893", "GSM1539894"], "sample_count": 18, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Expression data from human non-alcoholic fatty liver disease stages", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE89632", "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=GSE89632", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE89632", "version": null, "note": "The datasets generated and/or analysed during the current study are available in the GEO repository, https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSE48452 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126848.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and/or analysed during the current study are available in the GEO repository, https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSE48452 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126848.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2385720", "GSM2385721", "GSM2385722", "GSM2385723", "GSM2385724", "GSM2385725", "GSM2385726", "GSM2385727", "GSM2385728", "GSM2385729", "GSM2385730", "GSM2385731", "GSM2385732", "GSM2385733", "GSM2385734", "GSM2385735", "GSM2385736", "GSM2385737", "GSM2385738", "GSM2385739", "GSM2385740", "GSM2385741", "GSM2385742", "GSM2385743", "GSM2385744", "GSM2385745", "GSM2385746", "GSM2385747", "GSM2385748", "GSM2385749", "GSM2385750", "GSM2385751", "GSM2385752", "GSM2385753", "GSM2385754", "GSM2385755", "GSM2385756", "GSM2385757", "GSM2385758", "GSM2385759", "GSM2385760", "GSM2385761", "GSM2385762", "GSM2385763", "GSM2385764", "GSM2385765", "GSM2385766", "GSM2385767", "GSM2385768", "GSM2385769", "GSM2385770", "GSM2385771", "GSM2385772", "GSM2385773", "GSM2385774", "GSM2385775", "GSM2385776", "GSM2385777", "GSM2385778", "GSM2385779", "GSM2385780", "GSM2385781", "GSM2385782"], "sample_count": 63, "group_sizes": null, "group_sizes_basis": null, "platform": "14951", "taxon": "Homo sapiens", "title": "Genome-wide analysis of hepatic gene expression in patients with non-alcoholic fatty liver disease  and in healthy donors in relation to hepatic fatty acid composition and other nutritional factors", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE126848 not downloaded", "asset:data:geo:GSE126848 licence not read", "asset:data:geo:GSE48452 not downloaded", "asset:data:geo:GSE48452 licence not read", "asset:data:geo:GSE63067 not downloaded", "asset:data:geo:GSE63067 licence not read", "asset:data:geo:GSE89632 not downloaded", "asset:data:geo:GSE89632 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "Principal Component Analysis"}}, {"id": "method-2", "family": "diagnostic_evaluation", "specific": "roc", "label": "Diagnostic test evaluation", "dsl_operations": ["confusion_counts", "roc_curve", "sensitivity_specificity", "solve_confusion_matrix", "classification_metrics", "published_precision_envelope"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["reference_standard_and_test"], "evidence": {"score": 0.7, "band": "medium", "quote": "ROC analysis"}}, {"id": "method-3", "family": null, "specific": null, "label": "Penalized machine-learning regression models (LASSO, Ridge, and Elastic Net) with five-fold cross-validation, followed by receiver operating characteristic (ROC) curve analysis", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "reordered", "quote": "Then Penalize machine learning technique was used to detect the most important common genes between two data sets. The receiver operating characteristic (ROC) curve was performed to evaluate the efficacy of the diagnostic model."}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "classification_metrics", "expected_value": 95.0, "unit": "per cent", "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."}, {"id": "target-published-estimate-auc", "claim_id": "claim-target-result", "operation": "classification_metrics", "expected_value": 0.92, "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_named_unmapped"]}, "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": ["pca_fit"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: pca."}, "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": ["GSE48452", "GSE126848", "GSE89632", "GSE63067"], "resolved_accessions": ["GSE48452", "GSE126848", "GSE89632", "GSE63067"], "statements": ["The datasets generated and/or analysed during the current study are available in the GEO repository, https://www.ncbi.nlm.nih.gov/geo/geo2r/?acc=GSE48452 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126848."], "basis": "The paper names GSE48452, GSE126848, GSE89632, GSE63067 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": ["pca_fit"], "assets": ["source:pmid:37993474", "data:geo:GSE126848"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE126848"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:59:54Z", "independence": "single_reader"}, "evidence_check": {"method": "reordered", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 11080, "completion_tokens": 1837, "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:37993474", "type": "paper", "label": "pmid:37993474"}, {"id": "source:pmid:37993474", "type": "manuscript", "label": "PMID 37993474 full text"}, {"id": "data:geo:GSE126848", "type": "dataset", "label": "GEO GSE126848"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37993474", "type": "paper", "label": "pmid:37993474"}, {"id": "source:pmid:37993474", "type": "manuscript", "label": "PMID 37993474 full text"}, {"id": "data:geo:GSE126848", "type": "dataset", "label": "GEO GSE126848"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37993474", "type": "extraction", "used": ["source:pmid:37993474"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE126848", "type": "accession_resolution", "used": ["data:geo:GSE126848"], "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:37993474", "parent_hash": "sha256:21f6651769946da2be1e66ff2116ed07c20c0cd012632cf7dc271e0b8700b3e9", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE126848 licence not read", "asset:data:geo:GSE126848 not downloaded", "asset:data:geo:GSE48452 licence not read", "asset:data:geo:GSE48452 not downloaded", "asset:data:geo:GSE63067 licence not read", "asset:data:geo:GSE63067 not downloaded", "asset:data:geo:GSE89632 licence not read", "asset:data:geo:GSE89632 not downloaded", "execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["Methods and materials Workflow The RNASeq data of fatty liver patients and clinical fe"], "order_basis": "paper_text_order:1756", "id": "assay:1"}, {"role": "analysis", "definition_id": "principal_component_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["pca_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.8, "evidence": ["Principal Component Analysis"], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}