TNMCA: generation and application of network motif based inference models for drug repositioning

Jaejoon Choi, Kwangmin Kim, Min-Keun Song, Doheon Lee
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引用次数: 3

Abstract

Since the increase of the public biomedical data, Undiscovered Public Knowledge (UPK, proposed by Swanson) became an important research topic in the biological field. Drug repositioning is one of famous UPK tasks which infer alternative indications for approved drugs. Many researchers tried to find novel candidates of existing drugs, but these previous works are not fully automated which required manual modulations to desired tasks, and was not able to cover various biomedical entities. In addition, they had inference limitations that those works could infer only pre-defined cases using limited patterns. In this paper, we propose the Typed Network Motif Comparison Algorithm (TNMCA) to discover novel drug indications using topological patterns of data. Typed network motifs (TNM) are connected sub-graphs of data, which store types of data, instead of values of data. While previous researches depends on ABC model (or extension of it), TNMCA utilizes more generalized patterns as its inference models. Also, TNMCA can infer not only an existence of interaction, but also the type of the interaction. TNMCA is suited for multi-level biomedical interaction data as TNMs depend on the different types of entities and relations. We apply TNMCA to a public database, Comparative Toxicogenomics Database (CTD), to validate our method. The results show that TNMCA could infer meaningful indications with high performance (AUC=0.7469) compared to the ABC model (AUC=0.7050).
TNMCA:基于网络基序的药物重新定位推理模型的生成与应用
随着生物医学公共数据的增加,由Swanson提出的未发现公共知识(Undiscovered public Knowledge, UPK)成为生物领域的一个重要研究课题。药物重新定位是著名的UPK任务之一,它推断已批准药物的替代适应症。许多研究人员试图找到现有药物的新候选药物,但这些先前的工作不是完全自动化的,需要手动调节所需的任务,并且无法覆盖各种生物医学实体。此外,它们有推理限制,即这些作品只能使用有限的模式推断预先定义的情况。在本文中,我们提出了类型化网络基序比较算法(TNMCA)来发现新的药物适应症,利用数据的拓扑模式。类型化网络母图(TNM)是数据的连接子图,它存储数据的类型,而不是数据的值。与以往的研究依赖于ABC模型(或ABC模型的扩展)相比,TNMCA采用更广义的模式作为其推理模型。此外,TNMCA不仅可以推断出相互作用的存在,还可以推断出相互作用的类型。TNMCA适用于多层次生物医学相互作用数据,因为tnm依赖于不同类型的实体和关系。我们将TNMCA应用于一个公共数据库,比较毒物基因组学数据库(CTD),以验证我们的方法。结果表明,与ABC模型(AUC=0.7050)相比,TNMCA模型能够推断出有意义的适应症,且AUC=0.7469。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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