Deng Michelle, Zollanvari Amin, Alterovitz Gil
AMIA Jt Summits Transl Sci Proc. 2012;2012:25-34. Epub 2012 Mar 19.
The immense corpus of biomedical literature existing today poses challenges in information search and integration. Many links between pieces of knowledge occur or are significant only under certain contexts-rather than under the entire corpus. This study proposes using networks of ontology concepts, linked based on their co-occurrences in annotations of abstracts of biomedical literature and descriptions of experiments, to draw conclusions based on context-specific queries and to better integrate existing knowledge. In particular, a Bayesian network framework is constructed to allow for the linking of related terms from two biomedical ontologies under the queried context concept. Edges in such a Bayesian network allow associations between biomedical concepts to be quantified and inference to be made about the existence of some concepts given prior information about others. This approach could potentially be a powerful inferential tool for context-specific queries, applicable to ontologies in other fields as well.
当今存在的海量生物医学文献在信息搜索和整合方面带来了挑战。知识片段之间的许多联系仅在特定背景下出现或具有重要意义，而非在整个文献库中。本研究建议使用本体概念网络，这些网络基于它们在生物医学文献摘要注释和实验描述中的共现情况进行链接，以便根据特定背景的查询得出结论并更好地整合现有知识。特别是，构建了一个贝叶斯网络框架，以允许在查询的背景概念下将来自两个生物医学本体的相关术语进行链接。这种贝叶斯网络中的边使生物医学概念之间的关联能够被量化，并能根据关于其他概念的先验信息对某些概念的存在进行推断。这种方法可能成为用于特定背景查询的强大推理工具，也适用于其他领域的本体。
