可解释的机器学习识别了免疫炎症生物标志物和耐药性的治疗候选者
Tayyab Ijaz1, Hamna Maqsood1, Abdur Rehman2
1Department of Bioinformatics and Biotechnology, Government College University Faisalabad, Faisalabad, 38000, Pakistan.
Scientific reports
|December 25, 2025
概括
这项研究使用转录组分析和机器学习识别了药物耐药性 (DRE) 中关键的免疫相关基因特征. 这些生物标志物为新的药物标和个性化治疗提供了潜力.
科学领域:
- 神经科学是一个神经科学.
- 基因组学就是基因组学.
- 免疫学 免疫学 免疫学
背景情况:
- 药物耐药性 (DRE) 影响了大量患者,构成了重大的临床挑战.
- 神经炎症越来越多地被认为是导致发症和治疗耐药性的因素.
- 对DRE缺乏全面和可复制的转录基因生物标志物阻碍了进展.
研究的目的:
- 为了识别与DRE相关的免疫炎症基因特征.
- 为了利用生物标志物发现的综合转录基因分析和机器学习.
- 基于已识别的基因标,探索潜在的治疗性重新利用.
主要方法:
- 来自197个皮质和海马体样本 (162例,35例对照) 的综合RNA测序数据.
- 应用微分表达式分析和集体机器学习分类器 (LR,RF,SVM,ETs,XGBoost) 与SHAP用于特征选择.
- 进行了外部验证和药物基因相互作用分析,并进行了分子对接.
主要成果:
- 鉴定了897个差异表达基因 (DEGs),富含免疫-炎症通路.
- 机器学习模型实现了高的内部 (ROC-AUC:0.98-0.99) 和外部 (ROC-AUC:0.93) 验证.
- 优先考虑的基因如TNF,IL1B和P2RY12,与微质和单细胞通路相关;确定了prasugrel和pentamidine作为潜在的候选药物.
结论:
- 揭示了DRE.的可复制的免疫相关的转录生物标志物.
- 突出了治疗的药物重用可行的目标.
- 提供了精准医学治疗治疗方法的框架.
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