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The Arrowsmith Project: 2005 Status Report

机译:《箭匠计划》:2005年状态报告

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In the 1980s, Don Swanson proposed the concept of "undiscovered public knowledge," and published several examples in which two disparate literatures (i.e., sets of articles having no papers in common, no authors in common, and few cross-citations) nevertheless held complementary pieces of knowledge that, when brought together, made compelling and testable predictions about potential therapies for human disorders. In the 1990s, Don and I published more predictions together and created a computer-assisted search strategy ("Arrowsmith"). At first, the so-called one-node search was emphasized, in which one begins with a single literature (e.g., that dealing with a disease) and searches for a second unknown literature having complementary knowledge (e.g. that dealing with potential therapies). However, we soon realized that the two-node search is better aligned to the information practices of most biomedical investigators: in this case, the user chooses two literatures and then seeks to identify meaningful links between them. Could typical biomedical investigators learn to carry out Arrowsmith analyses? Would they find routine occasions for using such a sophisticated tool? Would they uncover significant links that affect their experiments? Four years ago, we initiated a project to answer these questions, working with several neuroscience field testers. Initially we expected that investigators would spend several days learning how to carry out searches, and would spend several days analyzing each search. Instead, we completely re-designed the user interface, the back-end databases, and the methods of processing linking terms, so that investigators could use Arrowsmith without any tutorial at all, and requiring only minutes to carry out a search. The Arrowsmith Project now hosts a suite of free, public tools. It has launched new research spanning medical informatics, genomics and social informatics, and has, indeed, assisted investigators in formulating new experiments, with direct impact on basic science and neurological diseases.
机译:在1980年代,唐·斯旺森(Don Swanson)提出了“未发现的公共知识”的概念,并发表了一些例子,其中保留了两种不同的文献(即,几套文章没有共同的论文,没有共同的作者,很少有交叉引用)知识的补充,被整合在一起,就人类疾病的潜在疗法做出了令人信服且可验证的预测。在1990年代,唐和我一起发表了更多的预测,并创建了一种计算机辅助的搜索策略(“ Arrowsmith”)。首先,强调了所谓的单节点搜索,其中从一本文献开始(例如,关于疾病的文献),然后搜索另一本具有补充知识的未知文献(例如,关于潜在疗法的文献)。但是,我们很快意识到,两节点搜索可以更好地与大多数生物医学研究者的信息实践保持一致:在这种情况下,用户选择了两种文献,然后试图找出它们之间的有意义的联系。典型的生物医学研究者能否学会进行Arrowsmith分析?他们会找到使用这种复杂工具的例行场合吗?他们会发现影响实验的重要链接吗?四年前,我们与几个神经科学领域的测试人员合作,发起了一个回答这些问题的项目。最初,我们希望调查人员花几天时间学习如何进行搜索,并花几天时间分析每次搜索。相反,我们完全重新设计了用户界面,后端数据库以及处理链接术语的方法,以便调查人员可以完全不用任何教程即可使用Arrowsmith,并且只需几分钟即可进行搜索。 Arrowsmith项目现在托管了一套免费的公共工具。它开展了涵盖医学信息学,基因组学和社会信息学的新研究,并且确实协助研究人员制定了新的实验,对基础科学和神经系统疾病产生了直接影响。

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