通过将患者的特定时间基因表达特征与生物医学知识库相结合,实现个性化的疾病诊断
Ghanshyam Verma1,2, Dietrich Rebholz-Schuhmann3, Michael G Madden4,5
1Insight Centre for Data Analytics, School of Computer Science, University of Galway, Galway, Ireland. ghanshyam.verma@insight-centre.org.
BMC bioinformatics
|February 7, 2024
概括
新的算法LOADDx和SCADDx将患者的基因表达数据与生物医学知识库相结合,以改善疾病诊断. 这些方法通过从大量数据中识别可能的疾病来增强个性化诊断,帮助临床决策.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 医学诊断 医学诊断 医学诊断
背景情况:
- 生物医学知识库 (KB) 提供了利用生物医学知识的新方式.
- 基因疾病协会和相关实体是 KBs 的关键组成部分.
- 整合KBs与诊断的患者时间临床数据仍然未得到充分探索.
研究的目的:
- 通过将基因表达数据与生物医学KBs结合起来,开发个性化疾病诊断算法.
- 帮助医疗诊断决策支持系统.
主要方法:
- 提出了两个新的算法:LOADDx和SCADDx.
- 结合了患者基因表达数据与 KBs 的基因疾病关联.
- 在真实世界的病毒性呼吸道感染基因表达数据集上测试了算法.
- 与使用LOOCV和内部验证的五种最先进的机器学习算法进行性能比较.
主要成果:
- SCADDx和LOADDx都超过了现有的机器学习算法.
- 在特定数据集中,SCADDx在检测感染方面达到高达100%的准确性.
- 在感染72小时内,SCADDx和LOADDx的平均准确率分别为91.38%和92.66%,超过XGBoost的86.43%.
结论:
- 使用差异表达基因和KBs的新方法可以识别患者可能患有的疾病.
- 算法提供疾病,受影响的基因和相关实体的排名列表.
- 这些发现支持医疗保健专业人员在临床决策中.
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