Integrating weighted gene co-expression network analysis and machine learning to elucidate neural characteristics in a mouse model of depression.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40656047", "kind": "science-record", "revision": 3, "content_hash": "sha256:183f6749eee32c0e7ce1a9f9b00fbad343c55a6e40bc9b7d852b229ef3495064", "created_at": "2026-11-01T20:30:45Z", "updated_at": "2026-11-01T22:14:22Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrating weighted gene co-expression network analysis and machine learning to elucidate neural characteristics in a mouse model of depression.", "doi": "10.3389/fpsyt.2025.1564095", "pmid": "40656047", "authors": ["Jinli Gao", "Qinglang Wang", "Jie Liu", "Siqian Zheng", "Jiahong Liu", "Zhiyong Gao", "Cheng Zhu"], "journal": "Frontiers in psychiatry", "year": 2025, "abstract": "Introduction: An AI-assisted deep learning strategy was applied to analyze the neurobiological characteristics of depression in mouse models. Integration of weighted gene co-expression network analysis (WGCNA) with the random forest algorithm enabled the identification of critical genes strongly associated with depression onset, offering theoretical support and potential biomarkers for early diagnosis and precision treatment.\nMethods: Gene expression data from depression-related mouse models were obtained from public GEO datasets (e.g., GSE102556) and normalized using Z-score transformation. WGCNA was employed to construct gene co-expression networks and explore associations between modules and depression-like behavioral phenotypes. Depression-related gene modules were identified and subjected to feature selection using the random forest model. The biological relevance of selected genes was further assessed, and model accuracy was validated through performance evaluation.\nResults: Our findings revealed significant differential expression of genes such as Oprm1, BDNF, Tph2, and Zfp769 in the depression mouse model (p < 0.05). Notably, Oprm1 exhibited the highest feature importance, contributing to a model accuracy of 94.5%. Gene expression patterns showed strong consistency across the prefrontal cortex (PFC) and nucleus accumbens (NAC).\nConclusion: The combined application of machine learning and transcriptomic analysis effectively identified core neurobiological genes in a depression model. Genes including Oprm1 and BDNF demonstrated functional relevance in modulating neural activity and behavior, offering promising candidates for early diagnosis and individualized treatment of depression.", "claims": [{"id": "claim-method", "statement": "For statistical analysis, independent samples t-tests (Student’s t-test) were used to evaluate the significance of group differences, such as between CUS and control groups, based on normalized gene expression values.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40656047", "locator": "sections[6] \"Statistical analysis\" paragraph 2", "quote": "For statistical analysis, independent samples t-tests (Student’s t-test) were used to evaluate the significance of group differences, such as between CUS and control groups, based on normalized gene expression values."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40656047", "locator": "sections[15] \"Data availability statement\" paragraph 2", "quote": "The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The Random Forest model achieved 94.5% overall classification accuracy, with an AUC of 0.937, when distinguishing control from chronic unpredictable stress mice.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40656047", "locator": "sections[10] \"Visualization of the results of the neurobiological characteristics recognition model for depression mice\" paragraph 4", "quote": "The results showed that the model achieved a classification accuracy of 95% for the control group (Ctrl) and 94% for the CUS model group, with an overall classification accuracy of 94.5%."}, {"asset": "source:pmid:40656047", "locator": "sections[10] \"Visualization of the results of the neurobiological characteristics recognition model for depression mice\" paragraph 5", "quote": "The area under the curve (AUC) reached 0.937, indicating strong predictive performance across varying classification thresholds."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The Random Forest model achieved 94.5% overall classification accuracy, with an AUC of 0.937, when distinguishing control from chronic unpredictable stress mice.", "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:40656047"], "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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