面向药物研发的大规模数据语义整合与挖掘模式探索

Research on Big Data Semantic Integration and Mining Pattern for Drug Discovery

整合并在语义层面上充分互连药物研发数据，将有利于从全局、系统化的视角开展药物研发工作，同时也有助于预测药物的不良副作用、加快药物研发流程、缩减药物开发成本等。文章试图探索语义技术如何支持药物研发数据的整合和挖掘，通过基于知识组织体系的语义标注，以及多类型实体互连策略构建充分互连的药物关联数据，支持药物研究人员对这些大量复杂实体及其关系的查找、探索和知识发现，从而帮助药物研发人员和，I盘床工作者更好地利用大规模药物数据，解决药物研发面临的实际问题。

Integrating and linking drug discovery data in semantic level is helpful to make drug discovery research implement on global, systematic, and predicable perspectives, also it will accelerate the research flow and reduce the cost of development. In this paper, we explore how semantic technology supports the integrating and mining of drug data, through semantic annotation based on knowledge organization system and multi-type entity linking strategy to construct interlinked data for drug discovery. Our research supports the search, exploration and mining of large scale entities and complex entity relations. The object of our effort is to make the most of drug big data for supporting researchers and clinicians to resolve the real problems existing in drug discovery.