Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
Ersilia Open Source Initiative, Cambridge, UK.
Nat Commun. 2022 Sep 9;13(1):5304. doi: 10.1038/s41467-022-33026-0.
Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a major challenge, so that multiple views of a given biological event can be considered simultaneously. Here we present the Bioteque, a resource of unprecedented size and scope that contains pre-calculated biomedical descriptors derived from a gigantic knowledge graph, displaying more than 450 thousand biological entities and 30 million relationships between them. The Bioteque integrates, harmonizes, and formats data collected from over 150 data sources, including 12 biological entities (e.g., genes, diseases, drugs) linked by 67 types of associations (e.g., 'drug treats disease', 'gene interacts with gene'). We show how Bioteque descriptors facilitate the assessment of high-throughput protein-protein interactome data, the prediction of drug response and new repurposing opportunities, and demonstrate that they can be used off-the-shelf in downstream machine learning tasks without loss of performance with respect to using original data. The Bioteque thus offers a thoroughly processed, tractable, and highly optimized assembly of the biomedical knowledge available in the public domain.
生物医学数据正以前所未有的速度积累，将其整合到一个统一的框架中是一个主要挑战，以便可以同时考虑给定生物事件的多个视图。在这里，我们介绍了 Bioteque，这是一个具有空前规模和范围的资源，其中包含从巨大的知识图谱中预先计算得出的生物医学描述符，展示了超过 45 万个生物实体和它们之间的 3000 万个关系。Bioteque 集成、协调和格式化了来自 150 多个数据源的数据，其中包括 12 种生物实体（例如基因、疾病、药物），它们通过 67 种类型的关联（例如“药物治疗疾病”、“基因相互作用”）相互连接。我们展示了 Bioteque 描述符如何促进高通量蛋白质相互作用组数据的评估、药物反应和新用途的预测，并证明它们可以在下游机器学习任务中现成使用，而不会降低使用原始数据的性能。因此，Bioteque 提供了公共领域中可用的生物医学知识的经过彻底处理、可处理和高度优化的组合。
