Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.
Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA and Molecular and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.
Bioinformatics. 2014 Oct 15;30(20):2923-30. doi: 10.1093/bioinformatics/btu403. Epub 2014 Jun 27.
The emergence of network medicine not only offers more opportunities for better and more complete understanding of the molecular complexities of diseases, but also serves as a promising tool for identifying new drug targets and establishing new relationships among diseases that enable drug repositioning. Computational approaches for drug repositioning by integrating information from multiple sources and multiple levels have the potential to provide great insights to the complex relationships among drugs, targets, disease genes and diseases at a system level.
In this article, we have proposed a computational framework based on a heterogeneous network model and applied the approach on drug repositioning by using existing omics data about diseases, drugs and drug targets. The novelty of the framework lies in the fact that the strength between a disease-drug pair is calculated through an iterative algorithm on the heterogeneous graph that also incorporates drug-target information. Comprehensive experimental results show that the proposed approach significantly outperforms several recent approaches. Case studies further illustrate its practical usefulness.
Supplementary data are available at Bioinformatics online.
网络医学的出现不仅为更好、更全面地理解疾病的分子复杂性提供了更多的机会，而且还为识别新的药物靶点和建立疾病之间的新关系提供了一种很有前途的工具，从而实现药物重定位。通过整合来自多个来源和多个层次的信息的计算方法来进行药物重定位，有可能从系统层面上提供药物、靶点、疾病基因和疾病之间复杂关系的深刻见解。
在本文中，我们提出了一个基于异构网络模型的计算框架，并应用该方法利用现有的关于疾病、药物和药物靶点的组学数据进行药物重定位。该框架的新颖之处在于，疾病-药物对之间的强度是通过在异构图上的迭代算法计算的，该算法还整合了药物-靶点信息。综合实验结果表明，所提出的方法明显优于几种最近的方法。案例研究进一步说明了它的实际用途。
补充数据可在《生物信息学》在线获取。
