Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea.
Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea.
Comput Methods Programs Biomed. 2022 Jun;221:106866. doi: 10.1016/j.cmpb.2022.106866. Epub 2022 May 11.
With the advent of bioinformatics, biological databases have been constructed to computerize data. Biological systems can be described as interactions and relationships between elements constituting the systems, and they are organized in various biomedical open databases. These open databases have been used in approaches to predict functional interactions such as protein-protein interactions (PPI), drug-drug interactions (DDI) and disease-disease relationships (DDR). However, just combining interaction data has limited effectiveness in predicting the complex relationships occurring in a whole context. Each contributing source contains information on each element in a specific field of knowledge but there is a lack of inter-disciplinary insight in combining them.
In this study, we propose the RWD Integrated platform for Discovering Associations in Biomedical research (RIDAB) to predict interactions between biomedical entities. RIDAB is established as a graph network to construct a platform that predicts the interactions of target entities. Biomedical open database is combined with EMRs each representing a biomedical network and a real-world data. To integrate databases from different domains to build the platform, mapping of the vocabularies was required. In addition, the appropriate structure of the network and the graph embedding method to be used were needed to be selected to fit the tasks.
The feasibility of the platform was evaluated using node similarity and link prediction for drug repositioning task, a commonly used task for biomedical network. In addition, we compared the US Food and Drug Administration (FDA)-approved repositioned drugs with the predicted result. By integrating EMR database with biomedical networks, the platform showed increased f1 score in predicting repositioned drugs, from 45.62% to 57.26%, compared to platforms based on biomedical networks alone.
This study demonstrates that the elements of biomedical research findings can be reflected by integrating EMR data with open-source biomedical networks. In addition, showed the feasibility of using the established platform to represent the integration of biomedical networks and reflected the relationship between real world networks.
随着生物信息学的出现，已经构建了生物数据库来对数据进行计算机化处理。生物系统可以被描述为构成系统的元素之间的相互作用和关系，它们在各种生物医学开放数据库中进行组织。这些开放数据库已被用于预测功能相互作用，如蛋白质-蛋白质相互作用（PPI）、药物-药物相互作用（DDI）和疾病-疾病关系（DDR）。然而，仅仅结合相互作用数据在预测整个背景下发生的复杂关系方面效果有限。每个贡献源都包含特定知识领域中每个元素的信息，但在将它们结合起来时缺乏跨学科的洞察力。
在这项研究中，我们提出了用于发现生物医学研究关联的 RWD 集成平台（RIDAB），以预测生物医学实体之间的相互作用。RIDAB 被建立为一个图网络，以构建一个预测目标实体相互作用的平台。将生物医学开放数据库与代表生物医学网络和真实世界数据的 EMR 相结合。为了整合来自不同领域的数据库来构建平台，需要进行词汇映射。此外，需要选择适当的网络结构和图嵌入方法来适应任务。
使用药物重新定位任务（一种常用的生物医学网络任务）的节点相似性和链接预测评估了平台的可行性。此外，我们将 FDA 批准的重新定位药物与预测结果进行了比较。通过将 EMR 数据库与生物医学网络集成，与仅基于生物医学网络的平台相比，平台在预测重新定位药物方面的 f1 分数从 45.62%提高到 57.26%。
本研究表明，通过将 EMR 数据与开源生物医学网络集成，可以反映生物医学研究结果的要素。此外，展示了使用已建立的平台来表示生物医学网络的集成并反映真实世界网络之间关系的可行性。
