MOLIERE：生物医学假设自动生成系统
MOLIERE: Automatic Biomedical Hypothesis Generation System
摘要：
假设生成正在成为一种重要的节省时间的技术，使生物医学研究人员能够快速发现重要概念之间的隐含联系。通常，这些系统对公共医疗数据的特定领域部分进行操作。相比之下，MOLIERE利用了来自2450多万份文件的信息。在我们的方法的核心在于从美国国家生物技术信息中心（NCBI）的多个异构数据集提取的生物医学对象的多模态和多关系网络。这些对象包括但不限于科学论文、关键字、基因、蛋白质、疾病和诊断。我们使用潜在的Dirichlet分配模型的假设应用于在这个网络中发现的最短路径附近发现的摘要，并通过对历史数据进行假设生成来证明MOLIERE的有效性。我们的网络，实施和产生的数据都公开提供给广大的科学界。
英文摘要：
Hypothesis generation is becoming a crucial time-saving technique which allows biomedical researchers to quickly discover implicit connections between important concepts. Typically, these systems operate on domain-specific fractions of public medical data. MOLIERE, in contrast, utilizes information from over 24.5 million documents. At the heart of our approach lies a multi-modal and multi-relational network of biomedical objects extracted from several heterogeneous datasets from the National Center for Biotechnology Information (NCBI). These objects include but are not limited to scientific papers, keywords, genes, proteins, diseases, and diagnoses. We model hypotheses using Latent Dirichlet Allocation applied on abstracts found near shortest paths discovered within this network, and demonstrate the effectiveness of MOLIERE by performing hypothesis generation on historical data. Our network, implementation, and resulting data are all publicly available for the broad scientific community.
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。
