Retrieval, alignment, and clustering of computational models based on semantic annotations.
Retrieval, alignment, and clustering of computational models based on semantic annotations.
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DOI：
10.1038/msb.2011.41
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发表时间：
2011-07-19
影响因子：
9.9
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中科院分区：
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随着计算系统生物学模型的增加，需要新的方法来探索其内容并使用实验数据建立连接。在这篇观点文章中，作者提出了一个灵活的语义框架，可以帮助实现这些目标。
在过去几年中，系统生物学家生产的计算模型数量爆炸是邀请结构和利用这一新信息的邀请。研究人员希望追踪与特定科学问题相关的模型，以探索它们的生物含量，对齐和结合它们，并与实验数据相匹配。为了自动化这些过程，必须考虑描述其生物学含义的语义注释。作为广泛计算方法的先决条件，我们为根据语义注释计算的系统生物学模型提出了一般和灵活的相似性度量。通过使用这些措施和大型可扩展本体论，我们实现了一个可以检索，群集和对齐系统生物学模型和实验数据集的平台。目前，其主要应用程序是在生物模型数据库中搜索相关模型，从初始模型，数据集或生物学概念列表开始。除了相似性搜索之外，语义特征向量对模型的表示可能为大量模型和相应数据集合的可视化，探索和统计分析铺平了道路。
As the number of computational systems biology models increases, new methods are needed to explore their content and build connections with experimental data. In this Perspective article, the authors propose a flexible semantic framework that can help achieve these aims. The exploding number of computational models produced by Systems Biologists over the last years is an invitation to structure and exploit this new wealth of information. Researchers would like to trace models relevant to specific scientific questions, to explore their biological content, to align and combine them, and to match them with experimental data. To automate these processes, it is essential to consider semantic annotations, which describe their biological meaning. As a prerequisite for a wide range of computational methods, we propose general and flexible similarity measures for Systems Biology models computed from semantic annotations. By using these measures and a large extensible ontology, we implement a platform that can retrieve, cluster, and align Systems Biology models and experimental data sets. At present, its major application is the search for relevant models in the BioModels Database, starting from initial models, data sets, or lists of biological concepts. Beyond similarity searches, the representation of models by semantic feature vectors may pave the way for visualisation, exploration, and statistical analysis of large collections of models and corresponding data.