Integrated analysis and identification of hub genes as novel biomarkers for Alzheimer's disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36110430", "kind": "science-record", "revision": 4, "content_hash": "sha256:dc3b714cea3c29e645229b8167b5ee3f436e8456ca7c6b1b64adfc0623e7718b", "created_at": "2026-11-01T18:15:22Z", "updated_at": "2026-11-01T22:07:34Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated analysis and identification of hub genes as novel biomarkers for Alzheimer's disease.", "doi": "10.3389/fnagi.2022.901972", "pmid": "36110430", "authors": ["Kun Zhao", "Hui Zhang", "Yinyan Wu", "Jianzhi Liu", "Xuezhong Li", "Jianyang Lin"], "journal": "Frontiers in aging neuroscience", "year": 2022, "abstract": "Alzheimer's disease (AD) is an intractable and progressive neurodegenerative disorder that can lead to severe cognitive decline, impaired speech, short-term memory loss, and finally an inability to function in daily life. For patients, their families, and even all of society, AD can impart great emotional pressure and economic costs. Therefore, this study aimed to investigate potential diagnostic biomarkers of AD. Using the Gene Expression Omnibus (GEO) database, the expression profiles of genes were extracted from the GSE5281, GSE28146, and GSE48350 microarray datasets. Then, immune-related genes were identified by the intersections of differentially expressed genes (DEGs). Functional enrichment analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Disease Ontology (DO), and Gene Set Enrichment Analysis (GSEA), were performed. Subsequently, random forest models and least absolute shrinkage and selection operator regression were used to further screen hub genes, which were then validated using receiver operating characteristic (ROC) curve analysis. Finally, 153 total immune-related DEGs were identified in relation to AD. DO analysis of these immune-related DEGs showed that they were enriched in \"lung disease,\" \"reproductive system disease,\" and \"atherosclerosis.\" Single GSEA of hub genes showed that they were particularly enriched in \"oxidative phosphorylation.\" ROC analysis of AGAP3 yielded an area under the ROC curve of 0.878 for GSE5281, 0.727 for GSE28146, and 0.635 for GSE48350. Moreover, immune infiltration analysis demonstrated that AGAP3 was related to follicular helper T cells, naïve CD4 T cells, naïve B cells, memory B cells, macrophages M0, macrophages M1, macrophages M2, resting natural killer (NK) cells, activated NK cells, monocytes, neutrophils, eosinophils, and activated mast cells. These results indicate that identifying immune-related DEGs might enhance the current understanding of the development and prognosis of AD. Furthermore, AGAP3 not only plays a vital role in AD progression and diagnosis but could also serve as a valuable target for further research on AD.", "claims": [{"id": "claim-method", "statement": "To identify diagnostic biomarkers related to AD, random forest models and LASSO regression analysis were used to filter the GSE5281 dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36110430", "locator": "sections[8] \"Identification and validation of diagnostic biomarkers\" paragraph 2", "quote": "To identify diagnostic biomarkers related to AD, random forest models and LASSO regression analysis were used to filter the GSE5281 dataset."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36110430", "locator": "sections[19] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "AGAP3 was selected as the final diagnostic marker for Alzheimer’s disease, with ROC AUC values of 0.878, 0.727, and 0.635 in GSE5281, GSE28146, and GSE48350, respectively.", "kind": "quantitative", "target": {"value": 0.878, "unit": "auc", "tolerance": null, "source": "Respectively, AGAP3 exhibited an AUC of 0.878 in GSE5281 (Figure 4D), 0.727 in GSE28146 (Figure 4E), and 0.635 in GSE48350 (Figure 4F)."}, "evidence": [{"asset": "source:pmid:36110430", "locator": "sections[14] \"Screening of diagnostic markers\" paragraph 2", "quote": "Respectively, AGAP3 exhibited an AUC of 0.878 in GSE5281 (Figure 4D), 0.727 in GSE28146 (Figure 4E), and 0.635 in GSE48350 (Figure 4F)."}, {"asset": "source:pmid:36110430", "locator": "sections[14] \"Screening of diagnostic markers\" paragraph 2", "quote": "Thus, AGAP3 was selected as the final diagnostic marker."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "AGAP3 was selected as the final diagnostic marker for Alzheimer’s disease, with ROC AUC values of 0.878, 0.727, and 0.635 in GSE5281, GSE28146, and GSE48350, respectively.", "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:36110430"], "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:GSE28146"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE28146"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36110430", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fnagi.2022.901972", "sha256": "sha256:644c7a9f671d03caacef30dffca03006cee4fe1d6f654796a78d520fd67bcbf7", "bytes": 44261, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fnagi.2022.901972", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9468260", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE28146", "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=GSE28146", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE28146", "version": null, "note": "The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM697308", "GSM697309", "GSM697310", "GSM697311", "GSM697312", "GSM697313", "GSM697314", "GSM697315", "GSM697316", "GSM697317", "GSM697318", "GSM697319", "GSM697320", "GSM697321", "GSM697322", "GSM697323", "GSM697324", "GSM697325", "GSM697326", "GSM697327", "GSM697328", "GSM697329", "GSM697330", "GSM697331", "GSM697332", "GSM697333", "GSM697334", "GSM697335", "GSM697336", "GSM697337"], "sample_count": 30, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Microarray analyses of laser-captured hippocampus reveal distinct gray and white matter signatures associated with incipient Alzheimer’s disease", "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:GSE48350", "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=GSE48350", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE48350", "version": null, "note": "The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1176196", "GSM1176197", "GSM1176198", "GSM1176199", "GSM1176200", "GSM1176201", "GSM1176202", "GSM1176203", "GSM1176204", "GSM1176205", "GSM1176206", "GSM1176207", "GSM1176208", "GSM1176209", "GSM1176210", "GSM1176211", "GSM1176212", "GSM1176213", "GSM1176214", "GSM1176215", "GSM1176216", "GSM1176217", "GSM1176218", "GSM1176219", "GSM1176220", "GSM1176221", "GSM1176222", "GSM1176223", "GSM1176224", "GSM1176225", "GSM1176226", "GSM1176227", "GSM1176228", "GSM1176229", "GSM1176230", "GSM1176231", "GSM1176232", "GSM1176233", "GSM1176234", "GSM1176235", "GSM1176236", "GSM1176237", "GSM1176238", "GSM1176239", "GSM1176240", "GSM1176241", "GSM1176242", "GSM1176243", "GSM1176244", "GSM1176245", "GSM1176246", "GSM1176247", "GSM1176248", "GSM1176249", "GSM1176250", "GSM1176251", "GSM1176252", "GSM1176253", "GSM1176254", "GSM1176255", "GSM1176256", "GSM1176257", "GSM1176258", "GSM1176259", "GSM1176260", "GSM1176261", "GSM1176262", "GSM1176263", "GSM1176264", "GSM1176265", "GSM1176266", "GSM1176267", "GSM1176268", "GSM1176269", "GSM1176270", "GSM1176271", "GSM1176272", "GSM1176273", "GSM1176274", "GSM1176275", "GSM300166", "GSM300167", "GSM300168", "GSM300169", "GSM300170", "GSM300171", "GSM300172", "GSM300173", "GSM300174", "GSM300175", "GSM300176", "GSM300177", "GSM300178", "GSM300179", "GSM300180", "GSM300181", "GSM300182", "GSM300183", "GSM300185", "GSM300186", "GSM300187", "GSM300188", "GSM300189", "GSM300190", "GSM300191", "GSM300192", "GSM300193", "GSM300194", "GSM300195", "GSM300196", "GSM300197", "GSM300198", "GSM300199", "GSM300200", "GSM300201", "GSM300202", "GSM300203", "GSM300204", "GSM300205", "GSM300206", "GSM300207", "GSM300208", "GSM300209", "GSM300210", "GSM300211", "GSM300212", "GSM300213", "GSM300214", "GSM300215", "GSM300216", "GSM300217", "GSM300218", "GSM300219", "GSM300220", "GSM300221", "GSM300222", "GSM300223", "GSM300224", "GSM300225", "GSM300226", "GSM300227", "GSM300228", "GSM300229", "GSM300230", "GSM300231", "GSM300232", "GSM300233", "GSM300234", "GSM300235", "GSM300236", "GSM300237", "GSM300238", "GSM300239", "GSM300240", "GSM300241", "GSM300242", "GSM300243", "GSM300244", "GSM300245", "GSM300246", "GSM300247", "GSM300248", "GSM300249", "GSM300250", "GSM300251", "GSM300252", "GSM300253", "GSM300254", "GSM300255", "GSM300256", "GSM300258", "GSM300259", "GSM300260", "GSM300261", "GSM300262", "GSM300263", "GSM300264", "GSM300265", "GSM300266", "GSM300267", "GSM300268", "GSM300269", "GSM300270", "GSM300271", "GSM300272", "GSM300273", "GSM300274", "GSM300275", "GSM300276", "GSM300277", "GSM300278", "GSM300279", "GSM300280", "GSM300281", "GSM300282", "GSM300283", "GSM300284", "GSM300285", "GSM300286", "GSM300287", "GSM300288", "GSM300289", "GSM300290", "GSM300291", "GSM300292", "GSM300293", "GSM300294", "GSM300295", "GSM300296", "GSM300297", "GSM300298", "GSM300299", "GSM300300", "GSM300301", "GSM300302", "GSM300303", "GSM300304", "GSM300305", "GSM300306", "GSM300307", "GSM300308", "GSM300309", "GSM300310", "GSM300311", "GSM300312", "GSM300313", "GSM300314", "GSM300315", "GSM300316", "GSM300317", "GSM300318", "GSM300319", "GSM300320", "GSM300321", "GSM300322", "GSM300323", "GSM300324", "GSM300325", "GSM300326", "GSM300327", "GSM300328", "GSM300329", "GSM300330", "GSM300331", "GSM300332", "GSM300333", "GSM300335", "GSM300338", "GSM300339", "GSM300340", "GSM300341", "GSM318840", "GSM350078"], "sample_count": 253, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Alzheimer's Disease Dataset", "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:GSE5281", "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=GSE5281", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE5281", "version": null, "note": "The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM119615", "GSM119616", "GSM119617", "GSM119618", "GSM119619", "GSM119620", "GSM119621", "GSM119622", "GSM119623", "GSM119624", "GSM119625", "GSM119626", "GSM119627", "GSM119628", "GSM119629", "GSM119630", "GSM119631", "GSM119632", "GSM119633", "GSM119634", "GSM119635", "GSM119636", "GSM119637", "GSM119638", "GSM119639", "GSM119640", "GSM119641", "GSM119642", "GSM119643", "GSM119644", "GSM119645", "GSM119646", "GSM119647", "GSM119648", "GSM119649", "GSM119650", "GSM119651", "GSM119652", "GSM119653", "GSM119654", "GSM119655", "GSM119656", "GSM119657", "GSM119658", "GSM119659", "GSM119660", "GSM119661", "GSM119662", "GSM119663", "GSM119664", "GSM119665", "GSM119666", "GSM119667", "GSM119668", "GSM119669", "GSM119670", "GSM119671", "GSM119672", "GSM119673", "GSM119674", "GSM119675", "GSM119676", "GSM119677", "GSM119678", "GSM119679", "GSM119680", "GSM119681", "GSM119682", "GSM119683", "GSM119684", "GSM119685", "GSM119686", "GSM119687", "GSM119688", "GSM238763", "GSM238790", "GSM238791", "GSM238792", "GSM238793", "GSM238794", "GSM238795", "GSM238796", "GSM238797", "GSM238798", "GSM238799", "GSM238800", "GSM238801", "GSM238802", "GSM238803", "GSM238804", "GSM238805", "GSM238806", "GSM238807", "GSM238808", "GSM238809", "GSM238810", "GSM238811", "GSM238812", "GSM238813", "GSM238815", "GSM238816", "GSM238817", "GSM238818", "GSM238819", "GSM238820", "GSM238821", "GSM238822", "GSM238823", "GSM238824", "GSM238825", "GSM238826", "GSM238827", "GSM238834", "GSM238835", "GSM238837", "GSM238838", "GSM238839", "GSM238840", "GSM238841", "GSM238842", "GSM238843", "GSM238844", "GSM238845", "GSM238846", "GSM238847", "GSM238848", "GSM238851", "GSM238854", "GSM238855", "GSM238856", "GSM238857", "GSM238858", "GSM238860", "GSM238861", "GSM238862", "GSM238863", "GSM238864", "GSM238865", "GSM238867", "GSM238868", "GSM238870", "GSM238871", "GSM238872", "GSM238873", "GSM238874", "GSM238875", "GSM238877", "GSM238941", "GSM238942", "GSM238943", "GSM238944", "GSM238945", "GSM238946", "GSM238947", "GSM238948", "GSM238949", "GSM238951", "GSM238952", "GSM238953", "GSM238955", "GSM238963"], "sample_count": 161, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Alzheimer's disease and the normal aged brain (steph-affy-human-433773)", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE28146 not downloaded", "asset:data:geo:GSE28146 licence not read", "asset:data:geo:GSE48350 not downloaded", "asset:data:geo:GSE48350 licence not read", "asset:data:geo:GSE5281 not downloaded", "asset:data:geo:GSE5281 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "To identify diagnostic biomarkers related to AD, random forest models and LASSO regression analysis were used to filter the GSE5281 dataset."}}, {"id": "method-2", "family": "group_comparison", "specific": "mann_whitney", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_mann_whitney"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Mann-Whitney"}}, {"id": "method-3", "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-4", "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"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "classification_metrics", "expected_value": 0.878, "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", "method.machine_learning_random_forest"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": false, "required_operations": [], "missing_operations": ["machine_learning_random_forest"], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: random_forest."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE5281", "GSE28146", "GSE48350"], "resolved_accessions": ["GSE5281", "GSE28146", "GSE48350"], "statements": ["The original contributions presented in this study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author."], "basis": "The paper names GSE5281, GSE28146, GSE48350 in National Center for Biotechnology Information (NCBI) GEO database and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:36110430", "data:geo:GSE28146"], "steps": []}, "must_build": {"dsl_operations": [{"operation": "machine_learning_random_forest", "unlocks_papers": null, "priority": null}], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE28146"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:05:37Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 13286, "completion_tokens": 2058, "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:36110430", "type": "paper", "label": "pmid:36110430"}, {"id": "source:pmid:36110430", "type": "manuscript", "label": "PMID 36110430 full text"}, {"id": "data:geo:GSE28146", "type": "dataset", "label": "GEO GSE28146"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36110430", "type": "paper", "label": "pmid:36110430"}, {"id": "source:pmid:36110430", "type": "manuscript", "label": "PMID 36110430 full text"}, {"id": "data:geo:GSE28146", "type": "dataset", "label": "GEO GSE28146"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36110430", "type": "extraction", "used": ["source:pmid:36110430"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE28146", "type": "accession_resolution", "used": ["data:geo:GSE28146"], "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:36110430", "parent_hash": "sha256:94760abee64e4e16f0749da36482da4a5b8e6f381e652b0f66f576bcf6ba4479", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE28146 licence not read", "asset:data:geo:GSE28146 not downloaded", "asset:data:geo:GSE48350 licence not read", "asset:data:geo:GSE48350 not downloaded", "asset:data:geo:GSE5281 licence not read", "asset:data:geo:GSE5281 not downloaded", "execution.not_run", "method.machine_learning_random_forest", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:run_analysis blocked on method:machine_learning_random_forest"], "assays": [{"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["To identify diagnostic biomarkers related to AD, random forest models and LASSO regression analysis were used to filter the GSE5281 dataset."], "order_basis": "analysis_only", "id": "assay:1"}]}