Generation and application of drug indication inference models using typed network motif comparison analysis
Generation and application of drug indication inference models using typed network motif comparison analysis
复制标题
DOI：
10.1186/1472-6947-13-s1-s2
复制
发表时间：
2013-04-05
影响因子：
3.5
通讯作者：
Lee, Doheon
中科院分区：
文献类型：
--
作者：
Choi, Jaejoon;Kim, Kwangmin;Lee, Doheon
背景：随着公开可用的生物医学数据量的增加，从生物医学数据中发现隐藏的知识（即，Swanson提出的未发现的公共知识（Undiscovered Public Knowledge，UPK）成为生物文献挖掘领域的一个重要研究课题。药物适应症推断或药物重新定位是著名的UPK任务之一，其推断已批准药物的替代适应症。许多先前的研究试图找到现有药物的新的候选适应症，但这些工作有以下局限性：1）模型不是完全自动化的，需要手动调节所需的任务，2）不能覆盖各种生物医学实体，3）具有推理限制，这些工作只能使用有限的模式推断预定义的情况。为了克服这些问题，我们提出了一个新的药物适应症推理model.Methods：在本文中，我们采用了类型化的网络模体比较算法（TNMCA）推断新的药物适应症使用给定的网络拓扑结构。类型化网络基元（TNM）是存储数据类型而不是数据值的网络基元。由于TNM依赖于不同类型的实体和关系，因此TNMCA是一种功能强大的多层次生物医学交互数据推理算法。我们利用一个新的归一化评分函数以及网络排除，以改善推理结果。结果：与ABC模型（AUC = 0.7050）和先前的TNMCA模型（AUC = 0.5679，0.7469）相比，增强的TNMCA模型能够以更高的性能（AUC = 0.801，0.829）推断有意义的指征。文献分析也表明，TNMCA推断有意义的results.Conclusions：我们提出并加强了一种新的药物适应症推理模型，将给定的网络拓扑模式。通过利用拓扑模式的推理模型，我们能够提高药物适应症推理的推理能力。
Background: As the amount of publicly available biomedical data increases, discovering hidden knowledge from biomedical data (i.e., Undiscovered Public Knowledge (UPK) proposed by Swanson) became an important research topic in the biological literature mining field. Drug indication inference, or drug repositioning, is one of famous UPK tasks, which infers alternative indications for approved drugs. Many previous studies tried to find novel candidate indications of existing drugs, but these works have following limitations: 1) models are not fully automated which required manual modulations to desired tasks, 2) are not able to cover various biomedical entities, and 3) have inference limitations that those works could infer only pre-defined cases using limited patterns. To overcome these problems, we suggest a new drug indication inference model.Methods: In this paper, we adopted the Typed Network Motif Comparison Algorithm (TNMCA) to infer novel drug indications using topology of given network. Typed Network Motifs (TNM) are network motifs, which store types of data, instead of values of data. TNMCA is a powerful inference algorithm for multi-level biomedical interaction data as TNMs depend on the different types of entities and relations. We utilized a new normalized scoring function as well as network exclusion to improve the inference results. To validate our method, we applied TNMCA to a public database, Comparative Toxicogenomics Database (CTD).Results: The results show that enhanced TNMCA was able to infer meaningful indications with high performance (AUC = 0.801, 0.829) compared to the ABC model (AUC = 0.7050) and previous TNMCA model (AUC = 0.5679, 0.7469). The literature analysis also shows that TNMCA inferred meaningful results.Conclusions: We proposed and enhanced a novel drug indication inference model by incorporating topological patterns of given network. By utilizing inference models from the topological patterns, we were able to improve inference power in drug indication inferences.