一个基于生物信息学和机器学习的诊断模型,以区分双相情感障碍与精神分裂症和严重抑郁症
Jing Shen1, Chenxu Xiao1, Xiwen Qiao1
1The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University, 251221, Suzhou, China.
Schizophrenia (Heidelberg, Germany)
|February 14, 2024
概括
这项研究开发了一种使用生物信息学和机器学习的诊断模型,以区分双相情感障碍 (BD) 与精神分裂症 (SC) 和严重抑郁症 (MDD). 确定了像RBM10和其他候选基因,显示了BD的有前途的诊断价值.
科学领域:
- 精神病学遗传学 精神病学遗传学
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 双极性障碍 (BD) 在精神疾病中自杀率最高,遗传原因和治疗方法不明.
- 在BD,精神分裂症 (SC) 和重度抑郁症 (MDD) 之间的诊断混导致患者的护理不够理想.
- 准确的区分对于有效治疗和改善BD患者的预后至关重要.
研究的目的:
- 使用生物信息学和机器学习建立一个诊断模型,将BD与SC和MDD区分开来.
- 为了识别BD诊断的新型候选基因和免疫细胞特征.
- 提供对BD的遗传和免疫学基础的见解.
主要方法:
- 利用来自GEO数据库的三个大脑组织和两个外周血液数据集.
- 微阵列数据的应用线性模型 (Limma) 用于识别差异表达基因 (DEG).
- 使用LASSO回归,PPI网络,人工神经网络 (ANN) 和ROC曲线用于模型开发和验证.
- 分析了免疫细胞的透,以评估免疫失调.
主要成果:
- 确定了RBM10作为区分BD和SC的候选基因.
- 确定了五个候选基因 (LYPD1,HMBS,HEBP2,SETD3,ECM2) 来区分BD和MDD.
- ANN模型实现了高准确度 (BD与SC的76.9%,BD与MDD的81.5%).
- 在疾病组之间发现了天真B细胞,休息NK细胞,激活杆细胞和记忆B细胞的显著差异.
结论:
- 开发的诊断模型显示了区分BD与SC和MDD的有希望的潜力.
- 候选基因RBM10,LYPD1,HMBS,HEBP2,SETD3和ECM2是BD诊断的潜在生物标志物.
- 免疫细胞透模式为BD病理生理学和差异诊断提供了进一步的见解.
相关概念视频
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The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...
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01:29Biological Causes of Schizophrenia
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Schizophrenia, a severe psychiatric disorder, arises from a complex interplay of biological factors, including genetic predisposition, structural brain abnormalities, neurotransmitter dysregulation, and developmental irregularities. These factors collectively contribute to the onset and progression of the disorder, which typically manifests in late adolescence or early adulthood.
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