Machine learning to detect signatures of disease in liquid biopsies - a user's guide.,Lab on a Chip

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Machine learning to detect signatures of disease in liquid biopsies - a user's guide.
Lab on a Chip ( IF 6.1 ) Pub Date : 2017-11-23 00:00:00 , DOI: 10.1039/c7lc00955k
Jina Ko ₁ , Steven N Baldassano , Po-Ling Loh , Konrad Kording , Brian Litt , David Issadore

Affiliation

New technologies that measure sparse molecular biomarkers from easily accessible bodily fluids (e.g. blood, urine, and saliva) are revolutionizing disease diagnostics and precision medicine. Microchip devices can measure more disease biomarkers with better sensitivity and specificity each year, but clinical interpretation of these biomarkers remains a challenge. Single biomarkers in ‘liquid biopsy’ often cannot accurately predict the state of a disease due to heterogeneity in phenotype and disease expression across individuals. To address this challenge, investigators are combining multiplexed measurements of different biomarkers that together define robust signatures for specific disease states. Machine learning is a useful tool to automatically discover and detect these signatures, especially as new technologies output increasing quantities of molecular data. In this paper, we review the state of the field of machine learning applied to molecular diagnostics and provide practical guidance to use this tool effectively and to avoid common pitfalls.

中文翻译：

用于检测液体活检中疾病特征的机器学习 - 用户指南。

从易于获取的体液（例如血液、尿液和唾液）中测量稀疏分子生物标志物的新技术正在彻底改变疾病诊断和精准医学。微芯片设备每年可以以更高的灵敏度和特异性测量更多的疾病生物标志物，但这些生物标志物的临床解释仍然是一个挑战。由于个体间表型和疾病表达的异质性，“液体活检”中的单一生物标志物通常无法准确预测疾病的状态。为了应对这一挑战，研究人员正在结合不同生物标志物的多重测量，这些生物标志物共同定义了特定疾病状态的稳健特征。机器学习是自动发现和检测这些特征的有用工具，特别是当新技术输出越来越多的分子数据时。在本文中，我们回顾了应用于分子诊断的机器学习领域的现状，并提供了有效使用该工具并避免常见陷阱的实用指导。

更新日期：2017-11-23

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