基于机器学习的模型的开发和验证,用于识别与败血症相关的急性损伤相关的基因
Chen Lin1, Meng Zheng2, Wensi Wu1
1Department of Emergency, The Third Affiliated People's Hospital, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
Frontiers in genetics
|August 6, 2025
概括
败血症引起的急性损伤 (AKI) 具有重大风险,但这项研究开发了一种用于早期诊断的机器学习模型. 该模型确定了关键的基因和潜在的治疗方法,提高了与败血症相关的AKI的诊断准确性.
科学领域:
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
- 基因组学就是基因组学.
背景情况:
- 败血症经常导致急性损伤 (AKI),使患者的结果复杂化并增加死亡率.
- 早期诊断和治疗败血症相关的AKI仍然具有挑战性,尽管有治疗方法.
- 了解败血症和AKI之间的分子联系对于开发新型诊断和治疗策略至关重要.
研究的目的:
- 开发和验证一种机器学习模型,用于在败血症患者中诊断AKI.
- 为了确定关键的分子途径和潜在的生物标志物共享的败血症和AKI.
- 发现与败血症相关的AKI潜在的治疗剂.
主要方法:
- 利用来自NCBI GEO数据库的两个败血症和三个AKI基因表达数据集.
- 应用批量效应缓解,差异基因表达和功能丰富分析.
- 开发并验证了机器学习模型,使用12个算法来识别AKI的诊断基因.
主要成果:
- 鉴定出28个在败血症和AKI之间重叠的差异表达基因,表明共享的分子机制.
- 功能性丰富分析强调了免疫反应和细胞粘附在AKI中的作用.
- 一个机器学习模型确定了八个关键基因 (NR3C2,PLEKHO1,CEACAM1,CDC25B,HEPACAM2,VNN1,SLC2A3,RPL36) 的诊断准确度很高 (AUC=0.978).
- 环素A和其他9种药物被确定为毒症相关的AKI的潜在治疗方法.
结论:
- 生物信息学和机器学习的整合为开发与败血症相关的AKI诊断模型提供了一个有希望的方法.
- 已识别的基因可以作为潜在的生物标志物和治疗点,以加强与败血症相关的AKI管理.
- 这项研究为未来的研究提供了基础,以改善这种关键疾病的诊断和治疗.
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