利用网络技术进行综合药物发现

Qian Zhu, Sashikiran Challa, Prajakta Purohit, Yuyin Sun, M. Lajiness, D. Wild, Ying Ding
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引用次数: 0

摘要

近年来,与药物发现相关的公开信息数量大幅增加,包括化合物和生物测定信息的在线数据库;将化合物与基因、目标和疾病联系起来的学术出版物;预测模型可以提示化合物、基因、目标和疾病之间的新联系。然而,缺乏集成这些信息的工具和方法,特别是缺乏跨多个来源查找相关知识和关系的工具和方法。在印第安纳大学,我们通过应用聚合数据挖掘工具和语义web技术来解决这个问题,包括使用广泛的web服务基础设施、RDF网络和推理引擎、本体,以及从学术文献中自动提取信息。
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Using Web Technologies for Integrative Drug Discovery
Recent years have seen a huge increase in the amount of publicly-available information relevant to drug discovery, including online databases of compound and bioassay information; scholarly publications linking compounds with genes, targets and diseases; and predictive models that can suggest new links between compounds, genes, targets and diseases. However, there is a lack of tools and methods to integrate this information, and in particular to look for pertinent knowledge and relationships across multiple sources. At Indiana University we are tackling this problem by applying aggregative data mining tools and semantic web technologies including using an extensive web service infrastructure, RDF networks and inference engines, ontologies, and automated extraction of information from scholarly literature.
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