AGATHA：基于自动图挖掘和Transformer的假设生成方法
AGATHA: Automatic Graph-mining And Transformer based Hypothesis generation Approach
摘要：
医学研究是有风险和昂贵的。例如，药物发现需要研究人员有效地筛选数千个潜在目标，以获得更全面的评估。然而，研究小组在看到中间结果之前很久就花费大量时间和金钱来执行确定该候选集所需的实验。假设生成系统通过挖掘大量公开可用的科学信息来预测合理的研究方向来应对这一挑战。我们介绍了AGATHA，这是一个深度学习的假设生成系统，可以在发现过程的早期引入数据驱动的见解。通过学习的排名标准，该系统快速优先考虑实体集之间的合理术语对，使我们能够推荐新的研究方向。我们用一个时间上的支持来大规模验证我们的系统，其中我们使用事先发布的数据预测2015年之后首次引入的连接。我们还探索了生物医学子领域，并展示了AGATHA在20种最受欢迎的关系类型中的预测能力。该系统在已建立的基准上实现了同类最佳的性能，并在子域中展示了高推荐分数。可复制性：所有代码、实验数据和预训练模型均可在线获取：sybrandt.com/2020/agatha
英文摘要：
Medical research is risky and expensive. Drug discovery, as an example, requires that researchers efficiently winnow thousands of potential targets to a small candidate set for more thorough evaluation. However, research groups spend significant time and money to perform the experiments necessary to determine this candidate set long before seeing intermediate results. Hypothesis generation systems address this challenge by mining the wealth of publicly available scientific information to predict plausible research directions. We present AGATHA, a deep-learning hypothesis generation system that can introduce data-driven insights earlier in the discovery process. Through a learned ranking criteria, this system quickly prioritizes plausible term-pairs among entity sets, allowing us to recommend new research directions. We massively validate our system with a temporal holdout wherein we predict connections first introduced after 2015 using data published beforehand. We additionally explore biomedical sub-domains, and demonstrate AGATHA's predictive capacity across the twenty most popular relationship types. This system achieves best-in-class performance on an established benchmark, and demonstrates high recommendation scores across subdomains. Reproducibility: All code, experimental data, and pre-trained models are available online: sybrandt.com/2020/agatha
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。
