2,179 research outputs found
From Questions to Effective Answers: On the Utility of Knowledge-Driven Querying Systems for Life Sciences Data
We compare two distinct approaches for querying data in the context of the
life sciences. The first approach utilizes conventional databases to store the
data and intuitive form-based interfaces to facilitate easy querying of the
data. These interfaces could be seen as implementing a set of "pre-canned"
queries commonly used by the life science researchers that we study. The second
approach is based on semantic Web technologies and is knowledge (model) driven.
It utilizes a large OWL ontology and same datasets as before but associated as
RDF instances of the ontology concepts. An intuitive interface is provided that
allows the formulation of RDF triples-based queries. Both these approaches are
being used in parallel by a team of cell biologists in their daily research
activities, with the objective of gradually replacing the conventional approach
with the knowledge-driven one. This provides us with a valuable opportunity to
compare and qualitatively evaluate the two approaches. We describe several
benefits of the knowledge-driven approach in comparison to the traditional way
of accessing data, and highlight a few limitations as well. We believe that our
analysis not only explicitly highlights the specific benefits and limitations
of semantic Web technologies in our context but also contributes toward
effective ways of translating a question in a researcher's mind into precise
computational queries with the intent of obtaining effective answers from the
data. While researchers often assume the benefits of semantic Web technologies,
we explicitly illustrate these in practice
The OBO Foundry: Coordinated Evolution of Ontologies to Support Biomedical Data Integration
The value of any kind of data is greatly enhanced when it exists in a form that allows it to be integrated with other data. One approach to integration is through the annotation of multiple bodies of data using common controlled vocabularies or ‘ontologies’. Unfortunately, the very success of this approach has led to a proliferation of ontologies, which itself creates obstacles to integration. The Open Biomedical Ontologies (OBO) consortium has set in train a strategy to overcome this problem. Existing OBO ontologies, including the Gene Ontology, are undergoing a process of coordinated reform, and new ontologies being created, on the basis of an evolving set of shared principles governing ontology development. The result is an expanding family of ontologies designed to be interoperable, logically well-formed, and to incorporate accurate representations of biological reality. We describe the OBO Foundry initiative, and provide guidelines for those who might wish to become involved in the future
Annotation of SBML Models Through Rule-Based Semantic Integration
*Motivation:* The creation of accurate quantitative Systems Biology Markup Language (SBML) models is a time-intensive, manual process often complicated by the many data sources and formats required to annotate even a small and well-scoped model. Ideally, the retrieval and integration of biological knowledge for model annotation should be performed quickly, precisely, and with a minimum of manual effort. Here, we present a method using off-the-shelf semantic web technology which enables this process: the heterogeneous data sources are first syntactically converted into ontologies; these are then aligned to a small domain ontology by applying a rule base. Integrating resources in this way can accommodate multiple formats with different semantics; it provides richly modelled biological knowledge suitable for annotation of SBML models.
*Results:* We demonstrate proof-of-principle for this rule-based mediation with two use cases for SBML model annotation. This was implemented with existing tools, decreasing development time and increasing reusability. This initial work establishes the feasibility of this approach as part of an automated SBML model annotation system.
*Availability:* Detailed information including download and mapping of the ontologies as well as integration results is available from "http://www.cisban.ac.uk/RBM":http://www.cisban.ac.uk/RB
Modularization for the Cell Ontology
One of the premises of the OBO Foundry is that development of an orthogonal set of ontologies will increase domain expert contributions and logical interoperability, and decrease maintenance workload. For these reasons, the Cell Ontology (CL) is being re-engineered. This process requires the extraction of sub-modules from existing OBO ontologies, which presents a number of practical engineering challenges. These extracted modules may be intended to cover a narrow or a broad set of species. In addition, applications and resources that make use of the Cell Ontology have particular modularization requirements, such as the ability to extract custom subsets or unions of the Cell Ontology with other OBO ontologies. These extracted modules may be intended to cover a narrow or a broad set of species, which presents unique complications.

We discuss some of these requirements, and present our progress towards a customizable simple-to-use modularization tool that leverages existing OWL-based tools and opens up their use for the CL and other ontologies
Populous: A tool for populating ontology templates
We present Populous, a tool for gathering content with which to populate an
ontology. Domain experts need to add content, that is often repetitive in its
form, but without having to tackle the underlying ontological representation.
Populous presents users with a table based form in which columns are
constrained to take values from particular ontologies; the user can select a
concept from an ontology via its meaningful label to give a value for a given
entity attribute. Populated tables are mapped to patterns that can then be used
to automatically generate the ontology's content. Populous's contribution is in
the knowledge gathering stage of ontology development. It separates knowledge
gathering from the conceptualisation and also separates the user from the
standard ontology authoring environments. As a result, Populous can allow
knowledge to be gathered in a straight-forward manner that can then be used to
do mass production of ontology content.Comment: in Adrian Paschke, Albert Burger begin_of_the_skype_highlighting
end_of_the_skype_highlighting, Andrea Splendiani, M. Scott Marshall, Paolo
Romano: Proceedings of the 3rd International Workshop on Semantic Web
Applications and Tools for the Life Sciences, Berlin,Germany, December 8-10,
201
Analysis of the human diseasome reveals phenotype modules across common, genetic, and infectious diseases
Phenotypes are the observable characteristics of an organism arising from its
response to the environment. Phenotypes associated with engineered and natural
genetic variation are widely recorded using phenotype ontologies in model
organisms, as are signs and symptoms of human Mendelian diseases in databases
such as OMIM and Orphanet. Exploiting these resources, several computational
methods have been developed for integration and analysis of phenotype data to
identify the genetic etiology of diseases or suggest plausible interventions. A
similar resource would be highly useful not only for rare and Mendelian
diseases, but also for common, complex and infectious diseases. We apply a
semantic text- mining approach to identify the phenotypes (signs and symptoms)
associated with over 8,000 diseases. We demonstrate that our method generates
phenotypes that correctly identify known disease-associated genes in mice and
humans with high accuracy. Using a phenotypic similarity measure, we generate a
human disease network in which diseases that share signs and symptoms cluster
together, and we use this network to identify phenotypic disease modules
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