Abstract

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.

中文翻译：

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通过链接的开放数据进行大规模监管和信令网络组装

摘要

目前正在进行巨大的努力，以通过链接的开放数据库来解决生物知识的组织问题。可以自动查询这些数据库以重建监管和信令网络。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恢复生物途径中的逻辑流的困难，组装网络意味着手动操作。我们提出了一个基于语义网络技术的框架，以在肿瘤细胞建模和药物筛选的背景下自动重建大规模监管和信号网络。提议的工具是 pyBRAvo（python 生物网络组装），在这里我们将其应用于肝癌患者发出的 910 个基因表达测量数据集。