Dataset Information

Towards improving phenotype representation in OWL.

ABSTRACT: BACKGROUND: Phenotype ontologies are used in species-specific databases for the annotation of mutagenesis experiments and to characterize human diseases. The Entity-Quality (EQ) formalism is a means to describe complex phenotypes based on one or more affected entities and a quality. EQ-based definitions have been developed for many phenotype ontologies, including the Human and Mammalian Phenotype ontologies. METHODS: We analyze formalizations of complex phenotype descriptions in the Web Ontology Language (OWL) that are based on the EQ model, identify several representational challenges and analyze potential solutions to address these challenges. RESULTS: In particular, we suggest a novel, role-based approach to represent relational qualities such as concentration of iron in spleen, discuss its ontological foundation in the General Formal Ontology (GFO) and evaluate its representation in OWL and the benefits it can bring to the representation of phenotype annotations. CONCLUSION: Our analysis of OWL-based representations of phenotypes can contribute to improving consistency and expressiveness of formal phenotype descriptions.

SUBMITTER: Loebe F

PROVIDER: S-EPMC3448528 | biostudies-literature | 2012 Sep

REPOSITORIES: biostudies-literature

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Towards improving phenotype representation in OWL.

Loebe Frank F Stumpf Frank F Hoehndorf Robert R Herre Heinrich H

Journal of biomedical semantics 20120921

<h4>Background</h4>Phenotype ontologies are used in species-specific databases for the annotation of mutagenesis experiments and to characterize human diseases. The Entity-Quality (EQ) formalism is a means to describe complex phenotypes based on one or more affected entities and a quality. EQ-based definitions have been developed for many phenotype ontologies, including the Human and Mammalian Phenotype ontologies.<h4>Methods</h4>We analyze formalizations of complex phenotype descriptions in the ...[more]

PMID: 23046625

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Project description:BackgroundEvery year, hundreds of thousands of patients experience treatment failure or adverse drug reactions (ADRs), many of which could be prevented by pharmacogenomic testing. However, the primary knowledge needed for clinical pharmacogenomics is currently dispersed over disparate data structures and captured in unstructured or semi-structured formalizations. This is a source of potential ambiguity and complexity, making it difficult to create reliable information technology systems for enabling clinical pharmacogenomics.MethodsWe developed Web Ontology Language (OWL) ontologies and automated reasoning methodologies to meet the following goals: 1) provide a simple and concise formalism for representing pharmacogenomic knowledge, 2) finde errors and insufficient definitions in pharmacogenomic knowledge bases, 3) automatically assign alleles and phenotypes to patients, 4) match patients to clinically appropriate pharmacogenomic guidelines and clinical decision support messages and 5) facilitate the detection of inconsistencies and overlaps between pharmacogenomic treatment guidelines from different sources. We evaluated different reasoning systems and test our approach with a large collection of publicly available genetic profiles.ResultsOur methodology proved to be a novel and useful choice for representing, analyzing and using pharmacogenomic data. The Genomic Clinical Decision Support (Genomic CDS) ontology represents 336 SNPs with 707 variants; 665 haplotypes related to 43 genes; 22 rules related to drug-response phenotypes; and 308 clinical decision support rules. OWL reasoning identified CDS rules with overlapping target populations but differing treatment recommendations. Only a modest number of clinical decision support rules were triggered for a collection of 943 public genetic profiles. We found significant performance differences across available OWL reasoners.ConclusionsThe ontology-based framework we developed can be used to represent, organize and reason over the growing wealth of pharmacogenomic knowledge, as well as to identify errors, inconsistencies and insufficient definitions in source data sets or individual patient data. Our study highlights both advantages and potential practical issues with such an ontology-based approach.

Project description:BACKGROUND: There is great concern within health surveillance, on how to grapple with environmental degradation, rapid urbanization, population mobility and growth. The Internet has emerged as an efficient way to share health information, enabling users to access and understand data at their fingertips. Increasingly complex problems in the health field require increasingly sophisticated computer software, distributed computing power, and standardized data sharing. To address this need, Web-based mapping is now emerging as an important tool to enable health practitioners, policy makers, and the public to understand spatial health risks, population health trends and vulnerabilities. Today several web-based health applications generate dynamic maps; however, for people to fully interpret the maps they need data source description and the method used in the data analysis or statistical modeling. For the representation of health information through Web-mapping applications, there still lacks a standard format to accommodate all fixed (such as location) and variable (such as age, gender, health outcome, etc) indicators in the representation of health information. Furthermore, net-centric computing has not been adequately applied to support flexible health data processing and mapping online. RESULTS: The authors of this study designed a HEalth Representation XML (HERXML) schema that consists of the semantic (e.g., health activity description, the data sources description, the statistical methodology used for analysis), geometric, and cartographical representations of health data. A case study has been carried on the development of web application and services within the Canadian Geospatial Data Infrastructure (CGDI) framework for community health programs of the New Brunswick Lung Association. This study facilitated the online processing, mapping and sharing of health information, with the use of HERXML and Open Geospatial Consortium (OGC) services. It brought a new solution in better health data representation and initial exploration of the Web-based processing of health information. CONCLUSION: The designed HERXML has been proven to be an appropriate solution in supporting the Web representation of health information. It can be used by health practitioners, policy makers, and the public in disease etiology, health planning, health resource management, health promotion and health education. The utilization of Web-based processing services in this study provides a flexible way for users to select and use certain processing functions for health data processing and mapping via the Web. This research provides easy access to geospatial and health data in understanding the trends of diseases, and promotes the growth and enrichment of the CGDI in the public health sector.

Project description:The continuous p-median approach to environmental diversity (ED) is a reliable way to identify sites that efficiently represent species. A recently developed maximum dispersion (maxdisp) approach to ED is computationally simpler, does not require the user to reduce environmental space to two dimensions, and performed better than continuous p-median for datasets of South African animals. We tested whether maxdisp performs as well as continuous p-median for 12 datasets that included plants and other continents, and whether particular types of environmental variables produced consistently better models of ED. We selected 12 species inventories and atlases to span a broad range of taxa (plants, birds, mammals, reptiles, and amphibians), spatial extents, and resolutions. For each dataset, we used continuous p-median ED and maxdisp ED in combination with five sets of environmental variables (five combinations of temperature, precipitation, insolation, NDVI, and topographic variables) to select environmentally diverse sites. We used the species accumulation index (SAI) to evaluate the efficiency of ED in representing species for each approach and set of environmental variables. Maxdisp ED represented species better than continuous p-median ED in five of 12 biodiversity datasets, and about the same for the other seven biodiversity datasets. Efficiency of ED also varied with type of variables used to define environmental space, but no particular combination of variables consistently performed best. We conclude that maxdisp ED performs at least as well as continuous p-median ED, and has the advantage of faster and simpler computation. Surprisingly, using all 38 environmental variables was not consistently better than using subsets of variables, nor did any subset emerge as consistently best or worst; further work is needed to identify the best variables to define environmental space. Results can help ecologists and conservationists select sites for species representation and assist in conservation planning.

Dataset Information

Towards improving phenotype representation in OWL.

Publications

Towards improving phenotype representation in OWL.

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