Department of Computer Science, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.
Department of Life Science Informatics, Bonn-Aachen International Center for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.
Bioinformatics. 2019 Sep 15;35(18):3538-3540. doi: 10.1093/bioinformatics/btz117.
Knowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs' nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programing and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Finally, we present a case study in which we used a novel biological pathway mapping resource to predict links that represent pathway crosstalks and hierarchies.
BioKEEN and PyKEEN are open source Python packages publicly available under the MIT License at https://github.com/SmartDataAnalytics/BioKEEN and https://github.com/SmartDataAnalytics/PyKEEN.
Supplementary data are available at Bioinformatics online.
由于能够预测链接并为图的节点和边创建密集表示，知识图嵌入（KGE）在其他领域受到了广泛关注。然而，将其应用于生物信息学的软件生态系统对于没有编程和机器学习专业知识的用户来说仍然是有限的和难以访问的。因此，我们开发了 BioKEEN（Biological KnowlEdge EmbeddiNgs）和 PyKEEN（Python KnowlEdge EmbeddiNgs），通过交互式命令行界面方便用户轻松使用。最后，我们提出了一个案例研究，我们使用了一种新的生物途径映射资源来预测代表途径串扰和层次结构的链接。
BioKEEN 和 PyKEEN 是开源 Python 包，根据麻省理工学院的许可，可在 https://github.com/SmartDataAnalytics/BioKEEN 和 https://github.com/SmartDataAnalytics/PyKEEN 上公开获取。
补充数据可在《Bioinformatics》在线获得。
