发表机构
SonyAI; The Systems Biology Institute; Okinawa Institute of Science and Technology (OIST)（索尼人工智能; 系统生物学研究所; 冲绳科学技术大学院大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Hakken是领域无关的知识预测与解释系统，结合Transformer模型与LLM语义知识，在生物医学领域建立时间感知多标签关系预测新基准，其部分衰老相关预测经湿实验室验证，发现TP53与BAMBI、RAF1与TNF的新相互作用。
AI 中文摘要
我们提出了Hakken，这是一种领域无关的预测与解释系统，用于执行知识预测，即通过建立新关系来拓展科学知识，这些关系并不局限于先前知识的演绎闭合域。Hakken采用基于Transformer的预测模型，该模型构建于从大量研究出版物中提取的知识图谱的时间序列之上，并结合了大语言模型（LLM）的语义知识，用于预测科学概念之间尚未被记录的关系的存在并定义其类型。随后，它调用与模型无关的解释框架，为每个预测提供配套信息，使科学家能够评估所提出的新关系。尽管Hakken具有通用性，我们通过将其应用于生物医学领域来展示其实用能力。在该领域中，Hakken的预测模型建立了时间感知多标签关系预测的新基准，且我们表明，该模型的输出在历史数据的较长时间跨度内保持一致且具有信息性。此外，我们对与衰老相关的150万条高于置信阈值的假设进行了评分，与生物学家对这些预测的批次进行了定性验证，并推进其中3条假设进入湿实验室的实验验证。两条在药物发现和重新利用方面具有潜在重大影响的预测得到了确认，为生物医学领域引入了TP53与BAMBI之间、以及RAF1与TNF之间此前未被记录的相互作用。
英文摘要
We present Hakken, a domain-agnostic prediction and explanation system performing knowledge prediction, i.e., growing scientific knowledge by establishing novel relationships, ones that are not limited to the deductive hull of previous knowledge. Hakken uses a transformer-based prediction model built on temporal sequences of knowledge graphs extracted from vast bodies of research publications, fused with an LLM's semantic knowledge, to predict the presence and define the type of as-yet undocumented relationships between scientific concepts. It then calls a model-agnostic explanation framework to provide accompanying information for each prediction that allows scientists to evaluate the suggested new relationship. While general purpose, we demonstrate Hakken's practical capabilities by applying it to the biomedical domain. There, Hakken's prediction model establishes a new benchmark for time-aware multi-label relation prediction, and we show that the model's output stays coherent and informative over extended time spans in historic data. In addition, we scored 1.5 million above-confidence-threshold hypotheses related to aging, qualitatively validated batches of these predictions with biologists and progressed three of them for empirical validation in wet-lab. Two predictions with potentially significant impact in the context of drug discovery and repurposing were confirmed, introducing previously undocumented interactions between TP53 and BAMBI, and between RAF1 and TNF, to biomedical science.
Comments66 pages
