He Yongqun
Unit for Laboratory Animal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
Expert Rev Vaccines. 2014 Jul;13(7):825-41. doi: 10.1586/14760584.2014.923762. Epub 2014 Jun 7.
While vaccine efficacy and safety research has dramatically progressed with the methods of in silico prediction and data mining, many challenges still exist. A formal ontology is a human- and computer-interpretable set of terms and relations that represent entities in a specific domain and how these terms relate to each other. Several community-based ontologies (including Vaccine Ontology, Ontology of Adverse Events and Ontology of Vaccine Adverse Events) have been developed to support vaccine and adverse event representation, classification, data integration, literature mining of host-vaccine interaction networks, and analysis of vaccine adverse events. The author further proposes minimal vaccine information standards and their ontology representations, ontology-based linked open vaccine data and meta-analysis, an integrative One Network ('OneNet') Theory of Life, and ontology-based approaches to study and apply the OneNet theory. In the Big Data era, these proposed strategies provide a novel framework for advanced data integration and analysis of fundamental biological networks including vaccine immune mechanisms.
虽然通过计算机模拟预测和数据挖掘方法，疫苗效力和安全性研究取得了显著进展，但仍存在许多挑战。形式本体是一组人类和计算机均可解释的术语及关系，用于表示特定领域中的实体以及这些术语之间的相互关系。已经开发了几种基于社区的本体（包括疫苗本体、不良事件本体和疫苗不良事件本体），以支持疫苗和不良事件的表示、分类、数据整合、宿主-疫苗相互作用网络的文献挖掘以及疫苗不良事件分析。作者进一步提出了最低限度的疫苗信息标准及其本体表示、基于本体的链接开放疫苗数据和元分析、综合的“一个网络”（“OneNet”）生命理论以及基于本体的方法来研究和应用OneNet理论。在大数据时代，这些提出的策略为包括疫苗免疫机制在内的基础生物网络的高级数据整合和分析提供了一个新颖的框架。
