2015
DOI: 10.1186/s12976-015-0017-y
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PDON: Parkinson’s disease ontology for representation and modeling of the Parkinson’s disease knowledge domain

Abstract: BackgroundDespite the unprecedented and increasing amount of data, relatively little progress has been made in molecular characterization of mechanisms underlying Parkinson’s disease. In the area of Parkinson’s research, there is a pressing need to integrate various pieces of information into a meaningful context of presumed disease mechanism(s). Disease ontologies provide a novel means for organizing, integrating, and standardizing the knowledge domains specific to disease in a compact, formalized and compute… Show more

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Cited by 36 publications
(24 citation statements)
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“…In the presence of other terminology sources, powerful filtering for faceted searches can be implemented. For instance, we can combine NIFT with HypothesisFinder [ 74 ] to systematically harvest speculative statements linked to imaging features; or combination of NIFT terms with ADO terms will allow us to systematically harvest factual statements that link imaging readouts to aspects of AD progression in literature; and finally, we also have the possibility of mining “shared imaging features” amongst other diseases by making use of the already integrated Parkinson Disease Ontology [ 75 ] and Multiple Sclerosis Ontology [ 76 ]. This could lead to domain specific imaging feature identification across disease scales.…”
Section: Discussionmentioning
confidence: 99%
“…In the presence of other terminology sources, powerful filtering for faceted searches can be implemented. For instance, we can combine NIFT with HypothesisFinder [ 74 ] to systematically harvest speculative statements linked to imaging features; or combination of NIFT terms with ADO terms will allow us to systematically harvest factual statements that link imaging readouts to aspects of AD progression in literature; and finally, we also have the possibility of mining “shared imaging features” amongst other diseases by making use of the already integrated Parkinson Disease Ontology [ 75 ] and Multiple Sclerosis Ontology [ 76 ]. This could lead to domain specific imaging feature identification across disease scales.…”
Section: Discussionmentioning
confidence: 99%
“…The ontology of heart electrophysiology has been developed based on METHONTOLOGY and according to the ontology life cycle [25]. This is an accepted method in the construction of many developed ontologies [26][27][28]. In this study, Initially, EP ontology domain and scope were clearly defined using needs assessment techniques in some focus group meetings with cardiologists and Health Information Management (HIM) experts.…”
Section: Methodsmentioning
confidence: 99%
“…OpenBEL focuses on causal and correlative relationships; use of the language is supported by a dedicated OpenBEL framework that provides essential analysis tools and algorithms [ 63 ]. One advantage of BEL over existing modeling languages is explicit support of semantics using domain-specific ontologies, which makes the encoding of biomedical features specific to neurodegeneration feasible by utilizing resources such as Alzheimer’s disease ontology [ 64 ] and Parkinson’s disease ontology [ 65 ].…”
Section: Bioinformatics Methods For the Identification Of Disease mentioning
confidence: 99%
“…Curated data represent molecular (“omics”) entities and their interaction and clinical data (including trial data). A comprehensive, semantic framework representing and formalising knowledge about anatomy (FMA [ 113 ], BRCO [ 114 ], NIF [ 115 ]), about major neurodegenerative diseases (ADO [ 64 ], PDON [ 65 ], MSO [ 116 ]) and about assays and readouts (variables) used in clinical trials (NDD-CTO [ 117 ]; NIFT [ 118 ]; clinicaltrials.gov [ 119 ]) enables “shared semantics” over all scales (from the molecular via the cellular and organ level to the cohort and population level) and all entity types (ranging from genes and proteins to cognitive testing and neuro-imaging).…”
Section: Bioinformatics Methods For the Identification Of Disease mentioning
confidence: 99%