Establishment and Analysis of a Combined Diagnostic Model of Alzheimer's Disease With Random Forest and Artificial Neural Network.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35847663", "kind": "science-record", "revision": 4, "content_hash": "sha256:6742ee4cbed5964a208be65d601ddf4f6459405f440278949f2e43bf2cb5b6bf", "created_at": "2026-11-01T18:14:56Z", "updated_at": "2026-11-01T22:07:04Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Establishment and Analysis of a Combined Diagnostic Model of Alzheimer's Disease With Random Forest and Artificial Neural Network.", "doi": "10.3389/fnagi.2022.921906", "pmid": "35847663", "authors": ["Dazhong Sun", "Haojun Peng", "Zhibing Wu"], "journal": "Frontiers in aging neuroscience", "year": 2022, "abstract": "Alzheimer's disease (AD) is a neurodegenerative condition that causes cognitive decline over time. Because existing diagnostic approaches for AD are limited, improving upon previously established diagnostic models based on genetic biomarkers is necessary. Firstly, four AD gene expression datasets were collected from the Gene Expression Omnibus (GEO) database. Two datasets were used to establish diagnostic models, and the other two datasets were used to verify the model effect. We merged GSE5281 with GSE44771 as the training dataset and found 120 DEGs. Then, we used random forest (RF) to screen 6 key genes (KLF15, MAFF, ITPKB, SST, DDIT4, and NRXN3) as being critical for separating AD and normal samples. The weights of these key genes were measured, and a diagnostic model was created using an artificial neural network (ANN). The area under the curve (AUC) of the model is 0.953, while the accuracy is 0.914. In the final step, two validation datasets were utilized to assess AUC performance. In GSE109887, our model had an AUC of 0.854, and in GSE132903, it had an AUC of 0.810. To summarize, we successfully identified key gene biomarkers and developed a new AD diagnostic model.", "claims": [{"id": "claim-method", "statement": "Then, we used random forest (RF) to screen 6 key genes (KLF15, MAFF, ITPKB, SST, DDIT4, and NRXN3) as being critical for separating AD and normal samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35847663", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Then, we used random forest (RF) to screen 6 key genes (KLF15, MAFF, ITPKB, SST, DDIT4, and NRXN3) as being critical for separating AD and normal samples."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository and accession numbers can be found below: https://www.ncbi.nlm.nih.gov/geo/ GEO accession numbers: GSE5281, GSE44771, GSE109887 and GSE132903.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35847663", "locator": "sections[18] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository and accession numbers can be found below: https://www.ncbi.nlm.nih.gov/geo/ GEO accession numbers: GSE5281, GSE44771, GSE109887 and GSE132903."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified six key gene biomarkers and developed an ANN-based AD diagnostic model with training AUC of 0.953 and accuracy of 0.914, obtaining validation AUCs of 0.854 and 0.810.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35847663", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The area under the curve (AUC) of the model is 0.953, while the accuracy is 0.914."}, {"asset": "source:pmid:35847663", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In GSE109887, our model had an AUC of 0.854, and in GSE132903, it had an AUC of 0.810."}, {"asset": "source:pmid:35847663", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "To summarize, we successfully identified key gene biomarkers and developed a new AD diagnostic model."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The study identified six key gene biomarkers and developed an ANN-based AD diagnostic model with training AUC of 0.953 and accuracy of 0.914, obtaining validation AUCs of 0.854 and 0.810.", "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:35847663"], "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. 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE5281", "GSE44771", "GSE109887", "GSE132903"], "resolved_accessions": ["GSE5281", "GSE44771", "GSE109887", "GSE132903"], "statements": ["The datasets presented in this study can be found in online repositories. The names of the repository and accession numbers can be found below: https://www.ncbi.nlm.nih.gov/geo/ GEO accession numbers: GSE5281, GSE44771, GSE109887 and GSE132903."], "basis": "The paper names GSE5281, GSE44771, GSE109887, GSE132903 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:35847663", "data:geo:GSE109887"], "steps": []}, "must_build": {"dsl_operations": [{"operation": "machine_learning_random_forest", "unlocks_papers": null, "priority": null}], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE109887"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:55:48Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 7100, "completion_tokens": 1439, "cached_tokens": 256}}, "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:35847663", "type": "paper", "label": "pmid:35847663"}, {"id": "source:pmid:35847663", "type": "manuscript", "label": "PMID 35847663 full text"}, {"id": "data:geo:GSE109887", "type": "dataset", "label": "GEO GSE109887"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35847663", "type": "paper", "label": "pmid:35847663"}, {"id": "source:pmid:35847663", "type": "manuscript", "label": "PMID 35847663 full text"}, {"id": "data:geo:GSE109887", "type": "dataset", "label": "GEO GSE109887"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35847663", "type": "extraction", "used": ["source:pmid:35847663"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE109887", "type": "accession_resolution", "used": ["data:geo:GSE109887"], "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:35847663", "parent_hash": "sha256:9c5c8cb381e407154ae3bcb2153fc42c699977329b358ede82f791ba512c425f", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE109887 licence not read", "asset:data:geo:GSE109887 not downloaded", "asset:data:geo:GSE132903 licence not read", "asset:data:geo:GSE132903 not downloaded", "asset:data:geo:GSE44771 licence not read", "asset:data:geo:GSE44771 not downloaded", "asset:data:geo:GSE5281 licence not read", "asset:data:geo:GSE5281 not downloaded", "execution.not_run", "method.machine_learning_random_forest", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:run_analysis blocked on method:machine_learning_random_forest"], "assays": [{"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Then, we used random forest (RF) to screen 6 key genes (KLF15, MAFF, ITPKB, SST, DDIT4, and NRXN3) as being critical for separating AD and normal samples."], "order_basis": "analysis_only", "id": "assay:1"}]}