Department of Computer Science, University of Missouri, 201 EBW, Columbia, MO 65211, USA.
BMC Bioinformatics. 2011 Jun 24;12:260. doi: 10.1186/1471-2105-12-260.
The ability to search for and precisely compare similar phenotypic appearances within and across species has vast potential in plant science and genetic research. The difficulty in doing so lies in the fact that many visual phenotypic data, especially visually observed phenotypes that often times cannot be directly measured quantitatively, are in the form of text annotations, and these descriptions are plagued by semantic ambiguity, heterogeneity, and low granularity. Though several bio-ontologies have been developed to standardize phenotypic (and genotypic) information and permit comparisons across species, these semantic issues persist and prevent precise analysis and retrieval of information. A framework suitable for the modeling and analysis of precise computable representations of such phenotypic appearances is needed.
We have developed a new framework called the Computable Visually Observed Phenotype Ontological Framework for plants. This work provides a novel quantitative view of descriptions of plant phenotypes that leverages existing bio-ontologies and utilizes a computational approach to capture and represent domain knowledge in a machine-interpretable form. This is accomplished by means of a robust and accurate semantic mapping module that automatically maps high-level semantics to low-level measurements computed from phenotype imagery. The framework was applied to two different plant species with semantic rules mined and an ontology constructed. Rule quality was evaluated and showed high quality rules for most semantics. This framework also facilitates automatic annotation of phenotype images and can be adopted by different plant communities to aid in their research.
The Computable Visually Observed Phenotype Ontological Framework for plants has been developed for more efficient and accurate management of visually observed phenotypes, which play a significant role in plant genomics research. The uniqueness of this framework is its ability to bridge the knowledge of informaticians and plant science researchers by translating descriptions of visually observed phenotypes into standardized, machine-understandable representations, thus enabling the development of advanced information retrieval and phenotype annotation analysis tools for the plant science community.
在植物科学和遗传研究中，搜索和精确比较物种内和跨物种相似表型外观的能力具有巨大的潜力。但这样做的难点在于，许多视觉表型数据，尤其是那些无法直接进行定量测量的视觉观察表型，都是以文本注释的形式存在，而这些描述存在语义模糊、异质性和粒度低等问题。尽管已经开发了几个生物本体论来规范表型（和基因型）信息并允许跨物种比较，但这些语义问题仍然存在，阻碍了信息的精确分析和检索。需要一个适合于对这些表型外观的精确可计算表示进行建模和分析的框架。
我们开发了一个名为植物可计算视觉观察表型本体论框架的新框架。这项工作为植物表型描述提供了一种新的定量视角，利用现有的生物本体论并采用计算方法以机器可解释的形式捕获和表示领域知识。这是通过一个强大而准确的语义映射模块来实现的，该模块自动将高级语义映射到从表型图像计算得出的低级测量值。该框架已应用于两个不同的植物物种，挖掘了语义规则并构建了一个本体。评估了规则的质量，并显示出大多数语义的高质量规则。该框架还促进了表型图像的自动注释，并可以被不同的植物社区采用，以帮助他们进行研究。
植物可计算视觉观察表型本体论框架已被开发用于更有效地和准确地管理视觉观察表型，这些表型在植物基因组学研究中起着重要作用。该框架的独特之处在于它能够通过将视觉观察表型的描述转化为标准化的、机器可理解的表示形式，将信息学家和植物科学研究人员的知识联系起来，从而为植物科学社区开发先进的信息检索和表型注释分析工具。
