Large-scale microarray studies on post-mortem brain tissues have been utilized to investigate the complex molecular pathology of bipolar disorder. However, a major challenge in characterizing the dysregulation of gene expression in patients with bipolar disorder includes the lack of convergence between different studies, limiting comprehensive understanding from individual results. In this study, we aimed to identify genes that are both validated in published literature and are important classification features of unsupervised machine learning analysis of Stanley Brain Bank microarray database, followed by augmented intelligence method to identify distinct patient molecular subgroups. Through combining traditional literature approaches and machine learning, we identified TBL1XR1, SMARCA2, and CHMP5 to be replicated in 3 of the 4 studies included our analysis. The expression of these genes segregated unique subgroups of patients with bipolar disorder. Our study suggests the involvement of PPARγ pathway regulation in patients with bipolar disorder.

对死后脑组织的大规模微阵列研究已被用于研究双相情感障碍的复杂分子病理学。然而，表征双相情感障碍患者基因表达失调的一个主要挑战包括不同研究之间缺乏收敛性，限制了对个体结果的全面理解。在这项研究中，我们旨在识别在已发表文献中得到验证的基因，并且是斯坦利脑库微阵列数据库无监督机器学习分析的重要分类特征，然后是增强智能方法来识别不同的患者分子亚群。通过结合传统文献方法和机器学习，我们确定了 TBL1XR1、SMARCA2、在 4 项研究中的 3 项中复制的 CHMP5 包括我们的分析。这些基因的表达分离了躁郁症患者的独特亚组。我们的研究表明 PPARγ 通路调节参与双相情感障碍患者。