Adding a Little Reality to Building Ontologies for Biology
Adding a Little Reality to Building Ontologies for Biology
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DOI：
10.1371/journal.pone.0012258
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发表时间：
2010-09-03
期刊：
影响因子：
3.7
通讯作者：
Stevens, Robert
中科院分区：
文献类型：
--
作者：
Lord, Phillip;Stevens, Robert
背景：生物学的许多领域对数学和计算建模是开放的。离散的逻辑形式主义的应用定义了生物医学本体论领域。本体论在生物信息学中有很多用途。最广泛使用的是对已收集的数据的实体的描述，允许跨多个资源进行集成和分析。现在有60多个本体论在积极使用，越来越多地发展为大型的国际合作。然而，关于本体论应该如何编写，也就是说，什么是适合表示的，有很多意见。最近，一种普遍的观点是“现实主义”方法，它对被认为合适的建模风格施加限制。方法论/主要发现：在这里，我们使用一些案例研究来描述生物实验的结果。结论/意义：从我们的分析中，我们得出结论，虽然现实主义原则可以为一些主题提供直接的建模，但科学及其研究的现象有一些关键方面不适合这种方法；现实主义似乎过于简单化，这反常地导致了过于复杂的本体论模型。我们认为，在建模本体时不可能避免妥协；对这些妥协的更清晰的理解将更好地实现适当的建模，满足计算生物学中对离散数学模型的许多需求。
Background: Many areas of biology are open to mathematical and computational modelling. The application of discrete, logical formalisms defines the field of biomedical ontologies. Ontologies have been put to many uses in bioinformatics. The most widespread is for description of entities about which data have been collected, allowing integration and analysis across multiple resources. There are now over 60 ontologies in active use, increasingly developed as large, international collaborations. There are, however, many opinions on how ontologies should be authored; that is, what is appropriate for representation. Recently, a common opinion has been the "realist" approach that places restrictions upon the style of modelling considered to be appropriate.Methodology/Principal Findings: Here, we use a number of case studies for describing the results of biological experiments. We investigate the ways in which these could be represented using both realist and non-realist approaches; we consider the limitations and advantages of each of these models.Conclusions/Significance: From our analysis, we conclude that while realist principles may enable straight-forward modelling for some topics, there are crucial aspects of science and the phenomena it studies that do not fit into this approach; realism appears to be over-simplistic which, perversely, results in overly complex ontological models. We suggest that it is impossible to avoid compromise in modelling ontology; a clearer understanding of these compromises will better enable appropriate modelling, fulfilling the many needs for discrete mathematical models within computational biology.