RAPID: Automated discovery of COVID-19 related hypotheses using publicly available scientific literature

RAPID：使用公开的科学文献自动发现 COVID-19 相关假设

• 批准号: 2127776
• 负责人: Ilya Safro
• 金额: $ 10.45万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2127776&HistoricalAwards=false
• 关键词: RAPID; Automated; discovery; COVID; 19

The vast amounts of biomedical information that accumulate in modern databases (such as MEDLINE of the National Library of Medicine) impose a great difficulty for efficient wide surveying by researchers who try to evaluate new information considering existing biomedical literature even when advanced information search engines are used. Automated hypotheses generation systems are designed to help scientists to overcome these difficulties and accelerate their research. The pandemic situation with COVID-19 is precisely one of the cases when such systems can play an extremely important role in coping with the coronavirus. Using two different AI approaches, we have developed two systems to discover plausible hypotheses in the biomedical domain. In this project, we will will deploy the COVID-19 customized hypothesis generation and knowledge discovery system, massively run it on any relevant to this research queries, and publish the results (including trained AI models, and discovered information) in the open domain for broad scientific community with the goal to accelerate the COVID-19 research. This work focuses heavily on addressing fundamental knowledge discovery questions by modeling and formulating scientific hypotheses using the publicly available information in the biomedical domain. However, in general, these methods are not restricted to any specific information domain, i.e., they can be broadly used to discover knowledge in texts. Although our experimental work will be related to COVID-19, the methods can be applied with some reservations to any literature-based analysis. For example, in the Materials Science Initiative, one of the goals is to establish a systematic understanding of the material properties and discover new materials which can be done by analyzing using the massive corpus of papers. In the legal world, identifying related patents can be done using a similar hypothesis modeling methodology.In the heart of the proposed approach lies a big multi-modal and multi-relational semantic knowledge network of all biomedical objects extracted from a variety of heterogeneous databases of the National Library of Medicine. These objects include but are not limited to scientific papers, abstracts, keywords, phrases, elements of thesaurus, genes, proteins, mutations, pathways, diseases, and diagnoses. We will leverage two systems, namely MOLIERE and AGATHA, that are based on structural and deep learning, respectively. We will customize them using the rapidly updated dataset of new papers that has not been yet processed by the National Library of Medicine but already exists in the open domain such as in various preprint archives and reports. The MOLIERE system is based on the network analysis techniques applied on the graph constructed using the low-dimensional embedding of the papers with the result interpretation methods that are based on the probabilistic topic modeling. The AGATHA system processes texts at much finer granularity, and creates a semantic knowledge network using more accurate embedding techniques followed by the deep learning training for knowledge discovery. Two systems complement each other. While the AGATHA is of higher quality, the MOLIERE is more interpretable. A combination of both will be leveraged in this research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在现代数据库中积累的大量生物医学信息（例如国家医学图书馆的MEDLINE）对于研究人员进行了有效的广泛测量，他们试图评估新信息，即使使用高级信息搜索引擎，他们也试图评估新信息。自动假设生成系统旨在帮助科学家克服这些困难并加速其研究。 Covid-19的大流行状况正是这种系统在应对冠状病毒中起着极为重要的作用的情况之一。使用两种不同的AI方法，我们开发了两个系统，以发现生物医学领域中的合理假设。在这个项目中，我们将在与本研究查询相关的任何与任何相关的任何相关的自定义假设生成和知识发现系统中部署Covid-19，并将结果（包括训练有素的AI模型和发现的信息）发布到开放的领域中，以实现广泛的科学界的范围，目的是加速Covid-1919研究。这项工作主要致力于通过使用生物医学领域中的公共信息对科学假设进行建模和制定科学假设来解决基本知识发现问题。但是，通常，这些方法不仅限于任何特定的信息域，即，它们可以广泛用于发现文本中的知识。尽管我们的实验工作将与COVID-19有关，但可以将这些方法应用于任何基于文献的分析。例如，在《材料科学计划》中，目标之一是建立对材料特性的系统理解，并发现可以通过使用大量论文来分析来完成的新材料。在法律世界中，可以使用类似的假设建模方法来识别相关专利。在拟议方法的核心中，所有生物医学对象是从国家医学图书馆的各种异构数据库中提取的所有生物医学对象的大型多模式和多关系语义知识网络。这些对象包括但不限于科学论文，摘要，关键字，短语，词库，基因，蛋白质，突变，途径，疾病和诊断的元素。我们将利用分别基于结构和深度学习的两个系统，即Moliere和Agatha。我们将使用尚未由国家医学图书馆处理的新论文的快速更新的新论文数据集自定义它们，但已经存在于开放型域中，例如在各种预印本档案和报告中。 Moliere系统基于使用论文的低维嵌入构建的图表上的网络分析技术，其结果是基于概率主题建模的结果解释方法。 Agatha系统以更精细的粒度处理文本，并使用更准确的嵌入技术创建语义知识网络，然后是深度学习培训以进行知识发现。两个系统相互补充。尽管Agatha的质量更高，但Moliere更容易解释。这项研究将利用两者的结合。该奖项反映了NSF的法定任务，并被认为是使用基金会的知识分子优点和更广泛影响的评论标准的评估值得支持的。

项目成果

Accelerating COVID-19 Research with Graph Mining and Transformer-Based Learning

通过图挖掘和基于 Transformer 的学习加速 COVID-19 研究

• DOI: 10.1609/aaai.v36i11.21543
• 发表时间: 2022
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Tyagin, Ilya;Kulshrestha, Ankit;Sybrandt, Justin;Matta, Krish;Shtutman, Michael;Safro, Ilya
• 通讯作者: Safro, Ilya