使用训练在精神分裂症炎症标志物的血水平上训练的机器学习分类器预测抗精神病反应
Jie Yin Yee1, Ser-Xian Phua2, Yuen Mei See1
1North Region, Institute of Mental Health, Singapore, Singapore.
Translational psychiatry
|February 14, 2025
概括
机器学习模型使用外围炎症生物标志物预测精神分裂症治疗反应. 支持载体机器确定了关键蛋白质,有助于为这种复杂的精神健康障碍制定个性化治疗策略.
科学领域:
- 神经科学是一个神经科学.
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
背景情况:
- 由于其异质性,精神分裂症提出了一个复杂的挑战.
- 识别不同的治疗应答子组对于有效的患者管理至关重要.
- 外周炎症生物标志物为治疗结果提供了潜在的指标.
研究的目的:
- 根据外围炎症生物标志物开发机器学习模型,以预测基于外围炎症生物标志物的精神分裂患者子组.
- 确定与差异性治疗反应相关的特定蛋白质生物标志物.
- 评估支持矢量机 (SVM) 在分类精神分裂症患者及其治疗响应性的实用性.
主要方法:
- 使用了146名精神分裂症患者和49名健康对照组的队列.
- 使用Olink Target 96炎症面板量化免疫生物标志物蛋白质水平.
- 训练并评估了一个支持向量机 (SVM) 分类器,用于二进制分类的交叉验证.
主要成果:
- 达到0.74的AUC来区分健康对照和精神分裂症患者.
- 成功区分了抗精神病药物反应的患者,AUC为0.88.
- 确定了耐治疗的个体,其AUC为0.78.8.
- 递归特征消除确定了区分治疗子组的关键蛋白质生物标志物.
结论:
- 机器学习,特别是SVM,有效地识别了精神分裂症复杂生物数据中的微妙模式.
- 这项研究表明了外围炎症生物标志物的潜力,可以预测治疗反应.
- 将机器学习与可解释的人工智能集成,提高了生物标志物发现和个性化精神分裂症治疗的临床实用性.
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