Predicting translational progress in biomedical research
Predicting translational progress in biomedical research
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DOI：
10.1371/journal.pbio.3000416
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发表时间：
2019-10-01
期刊：
影响因子：
9.8
通讯作者：
Santangelo, George M.
中科院分区：
文献类型：
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作者：
Hutchins, B. Ian;Davis, Matthew T.;Santangelo, George M.
基础科学进步可能需要数十年才能转化为人类健康的改善。缩短这一间隔将提高科学发现成功治疗人类疾病的速度。实现这一目标的一种方法是确定哪些知识进步最有可能转化为临床研究。为此，我们建立了一个机器学习系统，检测一篇论文是否可能被未来的临床试验或指南引用。尽管引文动力学很复杂，但短短两年的发表后数据就能准确预测一篇临床文章的最终引文(准确率=84%，F1得分=0.56；相比之下，准确率为19%)。我们发现，不同的知识流动轨迹与成功或失败影响临床研究的论文有关。因此，可以根据科学界对一篇论文的早期反应所传达的信息，实时评估和预测生物医学中的转化进展。
Fundamental scientific advances can take decades to translate into improvements in human health. Shortening this interval would increase the rate at which scientific discoveries lead to successful treatment of human disease. One way to accomplish this would be to identify which advances in knowledge are most likely to translate into clinical research. Toward that end, we built a machine learning system that detects whether a paper is likely to be cited by a future clinical trial or guideline. Despite the noisiness of citation dynamics, as little as 2 years of postpublication data yield accurate predictions about a paper's eventual citation by a clinical article (accuracy = 84%, F1 score = 0.56; compared to 19% accuracy by chance). We found that distinct knowledge flow trajectories are linked to papers that either succeed or fail to influence clinical research. Translational progress in biomedicine can therefore be assessed and predicted in real time based on information conveyed by the scientific community's early reaction to a paper.