从文献中的知识引导细胞网络模型的组装
Guided assembly of cellular network models from knowledge in literature
摘要：
计算建模对于理解和分析复杂系统至关重要。在生物学中，模型创建是一项依赖人类的任务，需要阅读数百篇论文并进行湿实验室实验，这需要几天或几个月的时间。为了克服这一障碍，我们提出了一种新的自动化方法，该方法利用文献中发表的知识，通过在几秒钟内选择最相关和最有用的信息来建议模型扩展。特别是，我们的新方法将从文献中提取的事件组织为具有依赖于文献中事件发生频率的额外度量的协作图。此外，我们发现，常见的图形中心性指标不同的评估提取的事件。我们已经证明了所提出的方法的可靠性，使用三种不同的选择模型，即，T细胞分化，T细胞大颗粒淋巴细胞，和胰腺癌细胞。我们提出的方法能够找到高百分比的期望新事件，平均召回率为82%。
英文摘要：
Computational modeling is crucial for understanding and analyzing complex systems. In biology, model creation is a human dependent task that requires reading hundreds of papers and conducting wet lab experiments, which would take days or months. To overcome this hurdle, we propose a novel automated method, that utilizes the knowledge published in literature to suggest model extensions by selecting most relevant and useful information in few seconds. In particular, our novel approach organizes the events extracted from the literature as a collaboration graph with additional metric that relies on the event occurrence frequency in literature. Additionally, we show that common graph centrality metrics vary in the assessment of the extracted events. We have demonstrated the reliability of the proposed method using three different selected models, namely, T cell differentiation, T cell large granular lymphocyte, and pancreatic cancer cell. Our proposed method was able to find high percent of the desired new events with an average recall of 82%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
