在液体活检检测中发现罕见事件的无监督检测
Javier Murgoitio-Esandi1, Dean Tessone2,3, Amin Naghdloo1,2
1Department of Aerospace and Mechanical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, 90089, CA, USA.
bioRxiv : the preprint server for biology
|February 20, 2025
概括
本研究引入了一种自动化方法,用于在液体活检中检测罕见分析物,而不需要先前的签名知识. 该方法使用深度生成算法来识别血液样本中的罕见生物事件.
科学领域:
- 生物技术是生物技术.
- 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:
- 液体活检对于癌症的检测,诊断和监测至关重要.
- 在生物样本中识别稀有分析物是当前方法的一个重大挑战.
- 现有的技术需要先前了解目标分析物的签名.
研究的目的:
- 开发一种自动化的方法来隔离没有先前的签名知识的罕见分析物.
- 应用深度生成算法来无监督识别罕见的生物事件.
- 为了证明该方法在分析外周血液样本中的有效性.
主要方法:
- 试剂被分解成一个组成部分的序列.
- 这些组件的概率分布是使用深度生成算法来学习的.
- 无监督学习将罕见事件隔离出来,而无需事先知道它们的签名.
主要成果:
- 自动化方法成功地在外周血液的免疫光显微镜图像中发现了罕见的,生物相关的事件.
- 该方法在含有癌症相关细胞的样本中显示出有效性.
- 它还成功地隔离了乳腺癌晚期患者血液中的罕见事件.
结论:
- 这种新的无监督方法可以在没有签名预识别的情况下检测稀有分析物.
- 这种方法对于在疾病检测和监测中推进液体活检应用具有前景.
- 它为分析复杂的生物数据提供了一个强大的工具,特别是在瘤学中.
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