从科学中检测信号：研究社区结构与先验知识提升基因调控实验预测
Detecting signal from science:The structure of research communities and prior knowledge improves prediction of genetic regulatory experiments
AI总结：
本研究提出贝叶斯计算方法，利用文献自动提取的基因相互作用数据与LINCS L1000实验匹配，发现科学聚焦且机构独立的研究更易复现，为科研资助政策提供指导。
摘要：
近年来，科学家、科学期刊、文章和发现的爆炸性增长，指数级地增加了科学家在导航先验知识时所面临的困难。这一挑战因已发表发现的可重复性不确定性而进一步加剧。一方面，大规模数字档案、机器阅读和提取工具的可用性，另一方面，自动化高通量实验，使我们能够大规模评估这些挑战，并识别加速科学进步的新机会。在此，我们展示了一种贝叶斯计算方法，能够利用从已发表的基因相互作用文献中自动提取的发现，积极预测稳健、可复现的科学主张。我们将这些经过科学过滤的发现，与大规模LINCS L1000高通量实验测量的未过滤基因相互作用进行匹配，以识别并抵消偏差来源。我们的计算方法建立在易于提取的出版物元数据之上，这些元数据涉及科学主张在先验知识网络中的位置，及其跨机构、作者和社区的支持广度，揭示出科学聚焦但社会和机构独立的研究活动最有可能被复现。这些发现推荐的政策与将生物医学研究资金集中于中央研究联盟和机构而非更广泛分散的常见做法相悖。我们的结果表明，稳健的科学发现依赖于共享焦点与独立性之间的微妙平衡，且这种复杂模式可以被计算性地利用来解码偏差并预测已发表发现的可复现性。这些见解为科学家导航研究文献以及寻求改进科学资助的资助者提供了指导。
英文摘要：
The explosive growth of scientists, scientific journals, articles and findings in recent years exponentially increases the difficulty scientists face in navigating prior knowledge. This challenge is exacerbated by uncertainty about the reproducibility of published findings. The availability of massive digital archives, machine reading and extraction tools on the one hand, and automated high-throughput experiments on the other, allow us to evaluate these challenges at scale and identify novel opportunities for accelerating scientific advance. Here we demonstrate a Bayesian calculus that enables the positive prediction of robust, replicable scientific claims with findings automatically extracted from published literature on gene interactions. We matched these findings, filtered by science, with unfiltered gene interactions measured by the massive LINCS L1000 high-throughput experiment to identify and counteract sources of bias. Our calculus is built on easily extracted publication meta-data regarding the position of a scientific claim within the web of prior knowledge, and its breadth of support across institutions, authors and communities, revealing that scientifically focused but socially and institutionally independent research activity is most likely to replicate. These findings recommend policies that go against the common practice of channeling biomedical research funding into centralized research consortia and institutes rather than dispersing it more broadly. Our results demonstrate that robust scientific findings hinge upon a delicate balance of shared focus and independence, and that this complex pattern can be computationally exploited to decode bias and predict the replicability of published findings. These insights provide guidance for scientists navigating the research literature and for science funders seeking to improve it.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
