Abstract:An erythrodermic skin disease occurring in a patient with an already existing erythroderma of different cause is a rare phenomenon. A 13 year old girl with congenital lamellar ichthyosis suffered from both erythroderma and generalized scaling. Probably at the age of 11, a clinically not recognized psoriatic erythroderma appeared associated with a pustular palmoplantar psoriasis of the Barber-Königsbeck type as well as a psoriatic osteoarthropathy. The identification off such overlapping disorders is of great i… Show more
“…Such connections can reveal new findings, thereby providing new hypotheses for investigation, or confirm known findings, and providing additional evidence for common mechanisms. Examples from our data include connections for which publications exist, e.g., pruritus which connects both psoriasis and lamella ichthyosis [ 36 ], and also those for which publications are harder to find, such as the association between Crohn’s disease and Bannayan-Riley-Ruvalcaba syndrome via cachexia (a syndromic group of symptoms describing the combination of weakness, muscle atrophy, loss of weight, and fatigue). Fig.…”
BackgroundThe Centre for Therapeutic Target Validation (CTTV - https://www.targetvalidation.org/) was established to generate therapeutic target evidence from genome-scale experiments and analyses. CTTV aims to support the validity of therapeutic targets by integrating existing and newly-generated data. Data integration has been achieved in some resources by mapping metadata such as disease and phenotypes to the Experimental Factor Ontology (EFO). Additionally, the relationship between ontology descriptions of rare and common diseases and their phenotypes can offer insights into shared biological mechanisms and potential drug targets. Ontologies are not ideal for representing the sometimes associated type relationship required. This work addresses two challenges; annotation of diverse big data, and representation of complex, sometimes associated relationships between concepts.MethodsSemantic mapping uses a combination of custom scripting, our annotation tool ‘Zooma’, and expert curation. Disease-phenotype associations were generated using literature mining on Europe PubMed Central abstracts, which were manually verified by experts for validity. Representation of the disease-phenotype association was achieved by the Ontology of Biomedical AssociatioN (OBAN), a generic association representation model. OBAN represents associations between a subject and object i.e., disease and its associated phenotypes and the source of evidence for that association. The indirect disease-to-disease associations are exposed through shared phenotypes. This was applied to the use case of linking rare to common diseases at the CTTV.ResultsEFO yields an average of over 80 % of mapping coverage in all data sources. A 42 % precision is obtained from the manual verification of the text-mined disease-phenotype associations. This results in 1452 and 2810 disease-phenotype pairs for IBD and autoimmune disease and contributes towards 11,338 rare diseases associations (merged with existing published work [Am J Hum Genet 97:111-24, 2015]). An OBAN result file is downloadable at http://sourceforge.net/p/efo/code/HEAD/tree/trunk/src/efoassociations/. Twenty common diseases are linked to 85 rare diseases by shared phenotypes. A generalizable OBAN model for association representation is presented in this study.ConclusionsHere we present solutions to large-scale annotation-ontology mapping in the CTTV knowledge base, a process for disease-phenotype mining, and propose a generic association model, ‘OBAN’, as a means to integrate disease using shared phenotypes.AvailabilityEFO is released monthly and available for download at http://www.ebi.ac.uk/efo/.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-016-0051-7) contains supplementary material, which is available to authorized users.
“…Such connections can reveal new findings, thereby providing new hypotheses for investigation, or confirm known findings, and providing additional evidence for common mechanisms. Examples from our data include connections for which publications exist, e.g., pruritus which connects both psoriasis and lamella ichthyosis [ 36 ], and also those for which publications are harder to find, such as the association between Crohn’s disease and Bannayan-Riley-Ruvalcaba syndrome via cachexia (a syndromic group of symptoms describing the combination of weakness, muscle atrophy, loss of weight, and fatigue). Fig.…”
BackgroundThe Centre for Therapeutic Target Validation (CTTV - https://www.targetvalidation.org/) was established to generate therapeutic target evidence from genome-scale experiments and analyses. CTTV aims to support the validity of therapeutic targets by integrating existing and newly-generated data. Data integration has been achieved in some resources by mapping metadata such as disease and phenotypes to the Experimental Factor Ontology (EFO). Additionally, the relationship between ontology descriptions of rare and common diseases and their phenotypes can offer insights into shared biological mechanisms and potential drug targets. Ontologies are not ideal for representing the sometimes associated type relationship required. This work addresses two challenges; annotation of diverse big data, and representation of complex, sometimes associated relationships between concepts.MethodsSemantic mapping uses a combination of custom scripting, our annotation tool ‘Zooma’, and expert curation. Disease-phenotype associations were generated using literature mining on Europe PubMed Central abstracts, which were manually verified by experts for validity. Representation of the disease-phenotype association was achieved by the Ontology of Biomedical AssociatioN (OBAN), a generic association representation model. OBAN represents associations between a subject and object i.e., disease and its associated phenotypes and the source of evidence for that association. The indirect disease-to-disease associations are exposed through shared phenotypes. This was applied to the use case of linking rare to common diseases at the CTTV.ResultsEFO yields an average of over 80 % of mapping coverage in all data sources. A 42 % precision is obtained from the manual verification of the text-mined disease-phenotype associations. This results in 1452 and 2810 disease-phenotype pairs for IBD and autoimmune disease and contributes towards 11,338 rare diseases associations (merged with existing published work [Am J Hum Genet 97:111-24, 2015]). An OBAN result file is downloadable at http://sourceforge.net/p/efo/code/HEAD/tree/trunk/src/efoassociations/. Twenty common diseases are linked to 85 rare diseases by shared phenotypes. A generalizable OBAN model for association representation is presented in this study.ConclusionsHere we present solutions to large-scale annotation-ontology mapping in the CTTV knowledge base, a process for disease-phenotype mining, and propose a generic association model, ‘OBAN’, as a means to integrate disease using shared phenotypes.AvailabilityEFO is released monthly and available for download at http://www.ebi.ac.uk/efo/.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-016-0051-7) contains supplementary material, which is available to authorized users.
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