Mullen Joseph, Cockell Simon J, Tipney Hannah, Woollard Peter M, Wipat Anil
Interdisciplinary Computing and Complex BioSystems Research Group, School of Computing Science, University of Newcastle-upon-Tyne , Newcastle upon Tyne , United Kingdom.
Bioinformatics Support Unit, University of Newcastle-upon-Tyne , United Kingdom.
PeerJ. 2016 Jan 19;4:e1558. doi: 10.7717/peerj.1558. eCollection 2016.
Current research and development approaches to drug discovery have become less fruitful and more costly. One alternative paradigm is that of drug repositioning. Many marketed examples of repositioned drugs have been identified through serendipitous or rational observations, highlighting the need for more systematic methodologies to tackle the problem. Systems level approaches have the potential to enable the development of novel methods to understand the action of therapeutic compounds, but requires an integrative approach to biological data. Integrated networks can facilitate systems level analyses by combining multiple sources of evidence to provide a rich description of drugs, their targets and their interactions. Classically, such networks can be mined manually where a skilled person is able to identify portions of the graph (semantic subgraphs) that are indicative of relationships between drugs and highlight possible repositioning opportunities. However, this approach is not scalable. Automated approaches are required to systematically mine integrated networks for these subgraphs and bring them to the attention of the user. We introduce a formal framework for the definition of integrated networks and their associated semantic subgraphs for drug interaction analysis and describe DReSMin, an algorithm for mining semantically-rich networks for occurrences of a given semantic subgraph. This algorithm allows instances of complex semantic subgraphs that contain data about putative drug repositioning opportunities to be identified in a computationally tractable fashion, scaling close to linearly with network data. We demonstrate the utility of our approach by mining an integrated drug interaction network built from 11 sources. This work identified and ranked 9,643,061 putative drug-target interactions, showing a strong correlation between highly scored associations and those supported by literature. We discuss the 20 top ranked associations in more detail, of which 14 are novel and 6 are supported by the literature. We also show that our approach better prioritizes known drug-target interactions, than other state-of-the art approaches for predicting such interactions.
当前药物研发的方法成效越来越低，成本却越来越高。一种替代模式是药物重新定位。许多已上市的重新定位药物案例都是通过偶然或合理观察发现的，这凸显了需要更系统的方法来解决这一问题。系统层面的方法有潜力开发新方法来理解治疗性化合物的作用，但需要对生物数据采用综合方法。整合网络可以通过结合多种证据来源来促进系统层面的分析，从而对药物、其靶点及其相互作用进行丰富描述。传统上，此类网络可以由技术人员手动挖掘，他们能够识别图中的部分（语义子图），这些部分表明药物之间的关系，并突出可能的重新定位机会。然而，这种方法不可扩展。需要自动化方法来系统地挖掘这些子图的整合网络，并将其呈现给用户。我们引入了一个正式框架，用于定义用于药物相互作用分析的整合网络及其相关语义子图，并描述了DReSMin算法，这是一种用于在语义丰富的网络中挖掘给定语义子图出现情况的算法。该算法能够以计算上易于处理的方式识别包含假定药物重新定位机会数据的复杂语义子图实例，其规模与网络数据接近线性增长。我们通过挖掘一个由11个来源构建的整合药物相互作用网络来证明我们方法的实用性。这项工作识别并对9,643,061个假定的药物 - 靶点相互作用进行了排名，结果显示高分关联与文献支持的关联之间存在很强的相关性。我们更详细地讨论了排名前20的关联，其中14个是新发现的，6个得到了文献支持。我们还表明，与其他预测此类相互作用的现有先进方法相比，我们的方法能更好地对已知药物 - 靶点相互作用进行优先级排序。
