发育科学中生物标志物的应用：基于数据驱动对20年间90种生物标志物趋势的建模
Biomarker adoption in developmental science: A data-driven modelling of trends from 90 biomarkers across 20 years.
作者信息
Qian Weiqiang, Zhang Chao, Piersiak Hannah A, Humphreys Kathryn L, Mitchell Colter
机构信息
Vanderbilt University, Nashville, Tennessee, USA.
University of Michigan, Ann Arbor, Michigan, USA.
出版信息
Infant Child Dev. 2024 Jan-Feb;33(1). doi: 10.1002/icd.2366. Epub 2022 Aug 31.
Developmental scientists have adopted numerous biomarkers in their research to better understand the biological underpinnings of development, environmental exposures, and variation in long-term health. Yet, adoption patterns merit investigation given the substantial resources used to collect, analyse, and train to use biomarkers in research with infants and children. We document trends in use of 90 biomarkers between 2000 and 2020 from approximately 430,000 publications indexed by the Web of Science. We provide a tool for researchers to examine each of these biomarkers individually using a data-driven approach to estimate the biomarker growth trajectory based on yearly publication number, publication growth rate, number of author affiliations, National Institutes of Health dedicated funding resources, journal impact factor, and years since the first publication. Results indicate that most biomarkers fit a "learning curve" trajectory (i.e., experience rapid growth followed by a plateau), though a small subset decline in use over time.
发育科学家们在其研究中采用了众多生物标志物，以更好地理解发育的生物学基础、环境暴露以及长期健康状况的差异。然而，鉴于在婴幼儿研究中收集、分析和培训使用生物标志物需要大量资源，其采用模式值得研究。我们记录了2000年至2020年间约430,000篇被科学引文索引收录的出版物中90种生物标志物的使用趋势。我们为研究人员提供了一种工具，使他们能够使用数据驱动的方法，根据年出版物数量、出版物增长率、作者单位数量、美国国立卫生研究院专项资助资源、期刊影响因子以及自首次发表以来的年份，单独检查这些生物标志物中的每一种，以估计生物标志物的增长轨迹。结果表明，大多数生物标志物符合“学习曲线”轨迹（即经历快速增长后趋于平稳），不过一小部分生物标志物的使用量会随着时间推移而下降。
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