Holtzapple Emilee, Telmer Cheryl A, Miskov-Zivanov Natasa
Department of Computational and Systems Biology, University of Pittsburgh, 3501 Fifth Ave, Pittsburgh, Pennsylvania 15213, USA.
Molecular Biosensor and Imagining Center, Carnegie Mellon University, 4400 Fifth Ave, Pittsburgh, Pennsylvania 15213, USA.
Database (Oxford). 2020 Jan 1;2020. doi: 10.1093/database/baaa056.
State-of-the-art machine reading methods extract, in hours, hundreds of thousands of events from the biomedical literature. However, many of the extracted biomolecular interactions are incorrect or not relevant for computational modeling of a system of interest. Therefore, rapid, automated methods are required to filter and select accurate and useful information. The FiLter for Understanding True Events (FLUTE) tool uses public protein interaction databases to filter interactions that have been extracted by machines from databases such as PubMed and score them for accuracy. Confidence in the interactions allows for rapid and accurate model assembly. As our results show, FLUTE can reliably determine the confidence in the biomolecular interactions extracted by fast machine readers and at the same time provide a speedup in interaction filtering by three orders of magnitude. Database URL: https://bitbucket.org/biodesignlab/flute.
最先进的机器阅读方法能在数小时内从生物医学文献中提取出数十万事件。然而，许多提取出的生物分子相互作用是不正确的，或者与感兴趣系统的计算建模无关。因此，需要快速、自动化的方法来筛选和选择准确且有用的信息。用于理解真实事件的过滤器（FLUTE）工具利用公共蛋白质相互作用数据库来筛选机器从诸如PubMed等数据库中提取的相互作用，并对其准确性进行评分。对相互作用的置信度有助于快速准确地进行模型组装。正如我们的结果所示，FLUTE能够可靠地确定快速机器阅读器提取的生物分子相互作用的置信度，同时将相互作用筛选速度提高三个数量级。数据库网址：https://bitbucket.org/biodesignlab/flute 。
