Linked Life Data

19733230

Source:http://linkedlifedata.com/resource/pubmed/id/19733230

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2009-11-16
pubmed:abstractText
The wealth of genomic technologies has enabled biologists to rapidly ascribe phenotypic characters to biological substrates. Central to effective biological investigation is the operational definition of the process under investigation. We propose an elucidation of categories of biological characters, including disease relevant traits, based on natural endogenous processes and experimentally observed biological networks, pathways and systems rather than on externally manifested constructs and current semantics such as disease names and processes. The Ontological Discovery Environment (ODE) is an Internet accessible resource for the storage, sharing, retrieval and analysis of phenotype-centered genomic data sets across species and experimental model systems. Any type of data set representing gene-phenotype relationships, such quantitative trait loci (QTL) positional candidates, literature reviews, microarray experiments, ontological or even meta-data, may serve as inputs. To demonstrate a use case leveraging the homology capabilities of ODE and its ability to synthesize diverse data sets, we conducted an analysis of genomic studies related to alcoholism. The core of ODE's gene set similarity, distance and hierarchical analysis is the creation of a bipartite network of gene-phenotype relations, a unique discrete graph approach to analysis that enables set-set matching of non-referential data. Gene sets are annotated with several levels of metadata, including community ontologies, while gene set translations compare models across species. Computationally derived gene sets are integrated into hierarchical trees based on gene-derived phenotype interdependencies. Automated set identifications are augmented by statistical tools which enable users to interpret the confidence of modeled results. This approach allows data integration and hypothesis discovery across multiple experimental contexts, regardless of the face similarity and semantic annotation of the experimental systems or species domain.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1089-8646
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
94
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
377-87
pubmed:dateRevised
2011-3-3
pubmed:meshHeading
pubmed-meshheading:19733230-Alcoholism, pubmed-meshheading:19733230-Animals, pubmed-meshheading:19733230-Brain Injuries, pubmed-meshheading:19733230-Computational Biology, pubmed-meshheading:19733230-Database Management Systems, pubmed-meshheading:19733230-Databases, Genetic, pubmed-meshheading:19733230-Gene Regulatory Networks, pubmed-meshheading:19733230-Genomics, pubmed-meshheading:19733230-Genotype, pubmed-meshheading:19733230-Humans, pubmed-meshheading:19733230-Internet, pubmed-meshheading:19733230-Invertebrates, pubmed-meshheading:19733230-Mental Disorders, pubmed-meshheading:19733230-Mice, pubmed-meshheading:19733230-Models, Genetic, pubmed-meshheading:19733230-Phenotype, pubmed-meshheading:19733230-Quantitative Trait Loci, pubmed-meshheading:19733230-Rats, pubmed-meshheading:19733230-Sequence Homology, Nucleic Acid, pubmed-meshheading:19733230-Species Specificity, pubmed-meshheading:19733230-User-Computer Interface, pubmed-meshheading:19733230-Vertebrates
pubmed:year
2009
pubmed:articleTitle
Ontological Discovery Environment: a system for integrating gene-phenotype associations.
pubmed:affiliation
Department of Computer Science, Baylor University, Waco, TX, USA.
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural