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

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).

CONCLUSION

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.