UMR 1332 Biologie du Fruit et Pathologie, INRAE, Univ. Bordeaux, 72 Avenue Edouard Bourlaux, CS20032, 33882, Villenave d'Ornon cedex, France.
Université de Nantes, CNRS, INSERM, l'Institut du Thorax F-44000, Nantes, France.
Database (Oxford). 2021 Jan 18;2021. doi: 10.1093/database/baaa113.
Huge efforts are currently underway to address the organization of biological knowledge through linked open databases. These databases can be automatically queried to reconstruct regulatory and signaling networks. However, assembling networks implies manual operations due to source-specific identification of biological entities and relationships, multiple life-science databases with redundant information and the difficulty of recovering logical flows in biological pathways. We propose a framework based on Semantic Web technologies to automate the reconstruction of large-scale regulatory and signaling networks in the context of tumor cells modeling and drug screening. The proposed tool is pyBRAvo (python Biological netwoRk Assembly), and here we have applied it to a dataset of 910 gene expression measurements issued from liver cancer patients. The tool is publicly available at https://github.com/pyBRAvo/pyBRAvo.
目前，人们正在努力通过链接的开放数据库来组织生物知识。可以自动查询这些数据库，以重建调控和信号网络。然而，由于生物实体和关系的特定来源识别、具有冗余信息的多个生命科学数据库以及在生物途径中恢复逻辑流程的困难，组装网络需要手动操作。我们提出了一个基于语义网技术的框架，以自动重建肿瘤细胞建模和药物筛选背景下的大规模调控和信号网络。所提出的工具是 pyBRAvo（python Biological netwoRk Assembly），我们已经将其应用于从肝癌患者获得的 910 个基因表达测量数据集。该工具可在 https://github.com/pyBRAvo/pyBRAvo 上获得。
