Research and Development, Biovista, Charlottesville, VA 22903, USA.
Brief Bioinform. 2011 Jul;12(4):357-68. doi: 10.1093/bib/bbr005. Epub 2011 Jun 28.
The immense growth of MEDLINE coupled with the realization that a vast amount of biomedical knowledge is recorded in free-text format, has led to the appearance of a large number of literature mining techniques aiming to extract biomedical terms and their inter-relations from the scientific literature. Ontologies have been extensively utilized in the biomedical domain either as controlled vocabularies or to provide the framework for mapping relations between concepts in biology and medicine. Literature-based approaches and ontologies have been used in the past for the purpose of hypothesis generation in connection with drug discovery. Here, we review the application of literature mining and ontology modeling and traversal to the area of drug repurposing (DR). In recent years, DR has emerged as a noteworthy alternative to the traditional drug development process, in response to the decreased productivity of the biopharmaceutical industry. Thus, systematic approaches to DR have been developed, involving a variety of in silico, genomic and high-throughput screening technologies. Attempts to integrate literature mining with other types of data arising from the use of these technologies as well as visualization tools assisting in the discovery of novel associations between existing drugs and new indications will also be presented.
随着 MEDLINE 的迅猛发展,以及人们意识到大量的生物医学知识是记录在非结构化文本中的,大量的文献挖掘技术应运而生,旨在从科学文献中提取生物医学术语及其相互关系。本体论在生物医学领域得到了广泛的应用,既可以作为受控词汇表,也可以为生物学和医学中概念之间的映射关系提供框架。过去,文献挖掘和本体论建模和遍历技术已经被用于与药物发现相关的假设生成。在这里,我们回顾了文献挖掘和本体论建模和遍历在药物再利用(DR)领域的应用。近年来,DR 作为传统药物开发过程的一个替代方案出现,以应对生物制药行业生产力的下降。因此,已经开发了系统的 DR 方法,涉及各种计算、基因组和高通量筛选技术。还将介绍将文献挖掘与使用这些技术产生的其他类型的数据以及可视化工具集成的尝试,这些工具有助于发现现有药物和新适应症之间的新关联。