对比多源时间知识图用于生物医学假设生成
概括
本研究引入了时间对比学习 (TCL),通过模拟科学术语在多个时间知识库的共同演变来产生生物医学假设,从而改善了研究发现.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 人工智能在医学中的应用
背景情况:
- 假设生成 (HG) 通过从科学文献中提取新的见解来加速生物医学研究.
- 现有的HG方法往往忽视了科学术语的时间动态,限制了它们捕捉不断发展的知识的能力.
- 多源时间知识库 (KB) 包含关键的最新信息,这些信息在当前的HG方法中未得到充分利用.
研究的目的:
- 开发一个创新的时间对比学习 (TCL) 框架用于假设生成.
- 为了有效地建模实体在多个时间KB的共同进化,以揭示潜在的关联.
- 通过整合多个来源的时间信息来增强科学术语的时间进化嵌入.
主要方法:
- 使用PubMed论文和比较毒基因组学数据库 (CTD) 构建了一个时间关系图.
- 使用来自医学科目标题 (MeSH) 的时间概念图,以及时间关系图.
- 训练了两个基于图形卷积网络 (GCN) 的循环网络,以学习实体时间进化嵌入.
- 实施了交叉视图时间预测任务,通过两种时间知识图 (TKG) 的对比嵌入来学习知识丰富的时间嵌入.
主要成果:
- 与单个基于TKG的方法相比,提议的TCL框架显示出更高的性能.
- 在三个真实世界生物医学术语关系数据集上取得了最先进的结果.
- 有效地捕捉和建模了科学术语及其关联的时间演变.
结论:
- 该TCL框架成功地整合了多个来源的时间KB信息,用于产生假设.
- 在跨时间KB的实体共同进化的联合建模增强了生物医学关系的发现.
- 这种方法在利用动态科学知识来产生假设方面取得了重大进展.
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