Crowd enabled curation and querying of large and noisy text mined protein interaction data
Crowd enabled curation and querying of large and noisy text mined protein interaction data
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DOI：
10.1007/s10619-017-7209-x
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发表时间：
2018-03-01
影响因子：
1.2
通讯作者：
Sadri, Fereidoon
中科院分区：
文献类型：
--
作者：
Jamil, Hasan M.;Sadri, Fereidoon
丰富的挖掘，预测和不确定的生物数据保证了大规模，高效和可扩展的策展工作。任何成功的策展企业所需的人类专业知识往往在经济上是禁止的，特别是对于最终可能不会产生结果的投机性最终用户查询。因此，挑战仍然是设计一个低成本的引擎，能够提供一组数据项的快速但试探性的注释和管理，这些数据项可以在以后由专家进行授权验证，要求更少的投资。因此，目标是在策展继续进行的同时，尽可能早地提供大量预测数据，并对其准确性具有可接受的置信度。在本文中，我们提出了一种新的方法，使用人群计算的生物数据库内容的注释和策展。其技术贡献在于识别和管理机械土耳其人的信任，并支持ad hoc声明式查询，这两者都可以利用噪声预测交互来实现可靠的分析。虽然所提出的方法和CrowdCure系统是为文献挖掘蛋白质-蛋白质相互作用数据策展而设计的，但它们易于大量推广。
The abundance of mined, predicted and uncertain biological data warrant massive, efficient and scalable curation efforts. The human expertise required for any successful curation enterprise is often economically prohibitive, especially for speculative end user queries that ultimately may not bear fruit. So the challenge remains in devising a low cost engine capable of delivering fast but tentative annotation and curation of a set of data items that can later be authoritatively validated by experts demanding significantly smaller investment. The aim thus is to make a large volume of predicted data available for use as early as possible with an acceptable degree of confidence in their accuracy while the curation continues. In this paper, we present a novel approach to annotation and curation of biological database contents using crowd computing. The technical contribution is in the identification and management of trust of mechanical turks, and support for ad hoc declarative queries, both of which are leveraged to enable reliable analytics using noisy predicted interactions. While the proposed approach and the CrowdCure system are designed for literature mined protein-protein interaction data curation, they are amenable to substantial generalization.