title: Computable visually observed phenotype ontological framework for plants.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Computer Science, University of Missouri, 201 EBW, Columbia, MO 65211, USA.
authors.forename: Jaturon
authors.initials: J
authors.lastname: Harnsomburana
authors.forename: Jason M
authors.initials: JM
authors.lastname: Green
authors.forename: Adrian S
authors.initials: AS
authors.lastname: Barb
authors.forename: Mary
authors.initials: M
authors.lastname: Schaeffer
authors.forename: Leszek
authors.initials: L
authors.lastname: Vincent
authors.forename: Chi-Ren
authors.initials: CR
authors.lastname: Shyu
mesh_terms: D000465:Algorithms; D030541:Databases, Genetic; D005638:Fruit / Q000033:anatomy & histology; D023281:Genomics; D005838:Genotype; D010641:Phenotype; D010944:Plants / Q000033:anatomy & histology* / Q000235:genetics*; D012660:Semantics; D018875:Vocabulary, Controlled; D003313:Zea mays / Q000033:anatomy & histology / Q000235:genetics