ORIGINAL RESEARCH article
Front. Psychiatry
Sec. Molecular Psychiatry
Volume 16 - 2025 | doi: 10.3389/fpsyt.2025.1564095
Integrating Weighted Gene Co-Expression Network Analysis and Machine Learning to Elucidate Neural Characteristics in a Mouse Model of Depression
Provisionally accepted
- 1Affiliated Kangning Hospital of Wenzhou Medical University, Wenzhou, China
- 2Kangda College, Nanjing Medical University, Lian Yungang, China
- 3Jiaxing Nanhu University, Jiaxing, China
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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.Methods: 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.Results: 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).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.
Keywords: Depression, mouse model, Neurobiology, Gene Co-expression Network, random forest, artificial intelligence
Received: 21 Jan 2025; Accepted: 11 Jun 2025.
Copyright: © 2025 Gao, Wang, Liu, Zheng, Liu, Gao and Zhu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jinli Gao, Affiliated Kangning Hospital of Wenzhou Medical University, Wenzhou, China
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