The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform
Eric Olinger - One of the best experts on this subject based on the ideXlab platform.
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clinical and genetic spectra of autosomal dominant tubulointerstitial kidney Disease due to mutations in umod and muc1
Kidney International, 2020Co-Authors: Eric Olinger, Patrick Hofmann, Kendrah Kidd, Ines Dufour, Hendrica Belge, Celine SchaefferAbstract:Autosomal dominant tubulointerstitial kidney Disease (ADTKD) is an increasingly recognized cause of end-stage kidney Disease, primarily due to mutations in UMOD and MUC1. The lack of clinical recognition and the small size of cohorts have slowed the understanding of Disease Ontology and development of diagnostic algorithms. We analyzed two registries from Europe and the United States to define genetic and clinical characteristics of ADTKD-UMOD and ADTKD-MUC1 and develop a practical score to guide genetic testing. Our study encompassed 726 patients from 585 families with a presumptive diagnosis of ADTKD along with clinical, biochemical, genetic and radiologic data. Collectively, 106 different UMOD mutations were detected in 216/562 (38.4%) of families with ADTKD (303 patients), and 4 different MUC1 mutations in 72/205 (35.1%) of the families that are UMOD-negative (83 patients). The median kidney survival was significantly shorter in patients with ADTKD-MUC1 compared to ADTKD-UMOD (46 vs. 54 years, respectively), whereas the median gout-free survival was dramatically reduced in patients with ADTKD-UMOD compared to ADTKD-MUC1 (30 vs. 67 years, respectively). In contrast to patients with ADTKD-UMOD, patients with ADTKD-MUC1 had normal urinary excretion of uromodulin and distribution of uromodulin in tubular cells. A diagnostic algorithm based on a simple score coupled with urinary uromodulin measurements separated patients with ADTKD-UMOD from those with ADTKD-MUC1 with a sensitivity of 94.1%, a specificity of 74.3% and a positive predictive value of 84.2% for a UMOD mutation. Thus, ADTKD-UMOD is more frequently diagnosed than ADTKD-MUC1, ADTKD subtypes present with distinct clinical features, and a simple score coupled with urine uromodulin measurements may help prioritizing genetic testing.
Helene Dollfus - One of the best experts on this subject based on the ideXlab platform.
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An ontological foundation for ocular phenotypes and rare eye Diseases.
Orphanet Journal of Rare Diseases, 2019Co-Authors: Panagiotis I. Sergouniotis, Emmanuel Maxime, Dorothée Leroux, Ana Rath, Peter N Robinson, Rachel Thompson, Annie Olry, Helene DollfusAbstract:Background The optical accessibility of the eye and technological advances in ophthalmic diagnostics have put ophthalmology at the forefront of data-driven medicine. The focus of this study is rare eye disorders, a group of conditions whose clinical heterogeneity and geographic dispersion make data-driven, evidence-based practice particularly challenging. Inter-institutional collaboration and information sharing is crucial but the lack of standardised terminology poses an important barrier. Ontologies are computational tools that include sets of vocabulary terms arranged in hierarchical structures. They can be used to provide robust terminology standards and to enhance data interoperability. Here, we discuss the development of the ophthalmology-related component of two well-established biomedical ontologies, the Human Phenotype Ontology (HPO; includes signs, symptoms and investigation findings) and the Orphanet Rare Disease Ontology (ORDO; includes rare Disease nomenclature/nosology).
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An ontological foundation for ocular phenotypes and rare eye Diseases
Orphanet Journal of Rare Diseases, 2019Co-Authors: Panagiotis I. Sergouniotis, Emmanuel Maxime, Dorothée Leroux, Ana Rath, Peter N Robinson, Rachel Thompson, Annie Olry, Helene DollfusAbstract:Background The optical accessibility of the eye and technological advances in ophthalmic diagnostics have put ophthalmology at the forefront of data-driven medicine. The focus of this study is rare eye disorders, a group of conditions whose clinical heterogeneity and geographic dispersion make data-driven, evidence-based practice particularly challenging. Inter-institutional collaboration and information sharing is crucial but the lack of standardised terminology poses an important barrier. Ontologies are computational tools that include sets of vocabulary terms arranged in hierarchical structures. They can be used to provide robust terminology standards and to enhance data interoperability. Here, we discuss the development of the ophthalmology-related component of two well-established biomedical ontologies, the Human Phenotype Ontology (HPO; includes signs, symptoms and investigation findings) and the Orphanet Rare Disease Ontology (ORDO; includes rare Disease nomenclature/nosology). Methods A variety of approaches were used including automated matching to existing resources and extensive manual curation. To achieve the latter, a study group including clinicians, patient representatives and Ontology developers from 17 countries was formed. A broad range of terms was discussed and validated during a dedicated workshop attended by 60 members of the group. Results A comprehensive, structured and well-defined set of terms has been agreed on including 1106 terms relating to ocular phenotypes (HPO) and 1202 terms relating to rare eye Disease nomenclature (ORDO). These terms and their relevant annotations can be accessed in http://www.human-phenotype-Ontology.org/ and http://www.orpha.net/ ; comments, corrections, suggestions and requests for new terms can be made through these websites. This is an ongoing, community-driven endeavour and both HPO and ORDO are regularly updated. Conclusions To our knowledge, this is the first effort of such scale to provide terminology standards for the rare eye Disease community. We hope that this work will not only improve coding and standardise information exchange in clinical care and research, but also it will catalyse the transition to an evidence-based precision ophthalmology paradigm.
Warren A Kibbe - One of the best experts on this subject based on the ideXlab platform.
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Disease Ontology 2015 update an expanded and updated database of human Diseases for linking biomedical knowledge through Disease data
Nucleic Acids Research, 2015Co-Authors: Warren A Kibbe, Janos X Binder, Elvira Mitraka, Christopher J Mungall, Gang Fu, James Malone, Cesar Arze, Victor Felix, Evan E Bolton, Drashtti VasantAbstract:The current version of the Human Disease Ontology (DO) (http://www.Disease-Ontology.org) database expands the utility of the Ontology for the examination and comparison of genetic variation, phenotype, protein, drug and epitope data through the lens of human Disease. DO is a biomedical resource of standardized common and rare Disease concepts with stable identifiers organized by Disease etiology. The content of DO has had 192 revisions since 2012, including the addition of 760 terms. Thirty-two percent of all terms now include definitions. DO has expanded the number and diversity of research communities and community members by 50+ during the past two years. These community members actively submit term requests, coordinate biomedical resource Disease representation and provide expert curation guidance. Since the DO 2012 NAR paper, there have been hundreds of term requests and a steady increase in the number of DO listserv members, twitter followers and DO website usage. DO is moving to a multi-editor model utilizing Protege to curate DO in web Ontology language. This will enable closer collaboration with the Human Phenotype Ontology, EBI's Ontology Working Group, Mouse Genome Informatics and the Monarch Initiative among others, and enhance DO's current asserted view and multiple inferred views through reasoning.
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Disease Ontology 2015 update an expanded and updated database of human Diseases for linking biomedical knowledge through Disease data
Nucleic Acids Research, 2015Co-Authors: Warren A Kibbe, Janos X Binder, Elvira Mitraka, Christopher J Mungall, Gang Fu, James Malone, Cesar Arze, Victor Felix, Evan E Bolton, Drashtti VasantAbstract:The current version of the Human Disease Ontology (DO) (http://www.Disease-Ontology.org) database expands the utility of the Ontology for the examination and comparison of genetic variation, phenotype, protein, drug and epitope data through the lens of human Disease. DO is a biomedical resource of standardized common and rare Disease concepts with stable identifiers organized by Disease etiology. The content of DO has had 192 revisions since 2012, including the addition of 760 terms. Thirty-two percent of all terms now include definitions. DO has expanded the number and diversity of research communities and community members by 50+ during the past two years. These community members actively submit term requests, coordinate biomedical resource Disease representation and provide expert curation guidance. Since the DO 2012 NAR paper, there have been hundreds of term requests and a steady increase in the number of DO listserv members, twitter followers and DO website usage. DO is moving to a multi-editor model utilizing Protege to curate DO in web Ontology language. This will enable closer collaboration with the Human Phenotype Ontology, EBI's Ontology Working Group, Mouse Genome Informatics and the Monarch Initiative among others, and enhance DO's current asserted view and multiple inferred views through reasoning.
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generating a focused view of Disease Ontology cancer terms for pan cancer data integration and analysis
Database, 2015Co-Authors: Lynn M Schriml, Warren A Kibbe, Elvira Mitraka, Qingrong Chen, Maureen Colbert, Daniel J Crichton, Richard Finney, Heather Kincaid, Daoud Meerzaman, Yang PanAbstract:Bio-ontologies provide terminologies for the scientific community to describe biomedical entities in a standardized manner. There are multiple initiatives that are developing biomedical terminologies for the purpose of providing better annotation, data integration and mining capabilities. Terminology resources devised for multiple purposes inherently diverge in content and structure. A major issue of biomedical data integration is the development of overlapping terms, ambiguous classifications and inconsistencies represented across databases and publications. The Disease Ontology (DO) was developed over the past decade to address data integration, standardization and annotation issues for human Disease data. We have established a DO cancer project to be a focused view of cancer terms within the DO. The DO cancer project mapped 386 cancer terms from the Catalogue of Somatic Mutations in Cancer (COSMIC), The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium, Therapeutically Applicable Research to Generate Effective Treatments, Integrative Oncogenomics and the Early Detection Research Network into a cohesive set of 187 DO terms represented by 63 top-level DO cancer terms. For example, the COSMIC term ‘kidney, NS, carcinoma, clear_cell_renal_cell_carcinoma’ and TCGA term ‘Kidney renal clear cell carcinoma’ were both grouped to the term ‘Disease Ontology Identification (DOID):4467 / renal clear cell carcinoma’ which was mapped to the TopNodes_DOcancerslim term ‘DOID:263 / kidney cancer’. Mapping of diverse cancer terms to DO and the use of top level terms (DO slims) will enable pan-cancer analysis across datasets generated from any of the cancer term sources where pan-cancer means including or relating to all or multiple types of cancer. The terms can be browsed from the DO web site (http://www.Disease-Ontology.org) and downloaded from the DO’s Apache Subversion or GitHub repositories. Database URL: http://www.Disease-Ontology.org
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Disease Ontology a backbone for Disease semantic integration
Nucleic Acids Research, 2012Co-Authors: Lynn M Schriml, Cesar Arze, Victor Felix, Suvarna Nadendla, Yuwei Wayne Chang, Mark Mazaitis, Gang Feng, Warren A KibbeAbstract:The Disease Ontology (DO) database (http://Disease-Ontology.org) represents a comprehensive knowledge base of 8043 inherited, developmental and acquired human Diseases (DO version 3, revision 2510). The DO web browser has been designed for speed, efficiency and robustness through the use of a graph database. Full-text contextual searching functionality using Lucene allows the querying of name, synonym, definition, DOID and cross-reference (xrefs) with complex Boolean search strings. The DO semantically integrates Disease and medical vocabularies through extensive cross mapping and integration of MeSH, ICD, NCI's thesaurus, SNOMED CT and OMIM Disease-specific terms and identifiers. The DO is utilized for Disease annotation by major biomedical databases (e.g. Array Express, NIF, IEDB), as a standard representation of human Disease in biomedical ontologies (e.g. IDO, Cell line Ontology, NIFSTD Ontology, Experimental Factor Ontology, Influenza Ontology), and as an ontological cross mappings resource between DO, MeSH and OMIM (e.g. GeneWiki). The DO project (http://DiseaseOntology.sf.net) has been incorporated into open source tools (e.g. Gene Answers, FunDO) to connect gene and Disease biomedical data through the lens of human Disease. The next iteration of the DO web browser will integrate DO's extended relations and logical definition representation along with these biomedical resource cross-mappings.
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annotating the human genome with Disease Ontology
BMC Genomics, 2009Co-Authors: John D Osborne, Warren A Kibbe, Gang Feng, Jared M Flatow, Michelle Holko, Maria I Danila, Rex L ChisholmAbstract:The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for Diseases. We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to discover gene-Disease relationships from the GeneRIF database. We utilized a comprehensive subset of UMLS, which is Disease-focused and structured as a directed acyclic graph (the Disease Ontology), to filter and interpret results from MMTx. The results were validated against the Homayouni gene collection using recall and precision measurements. We compared our results with the widely used Online Mendelian Inheritance in Man (OMIM) annotations. The validation data set suggests a 91% recall rate and 97% precision rate of Disease annotation using GeneRIF, in contrast with a 22% recall and 98% precision using OMIM. Our thesaurus-based approach allows for comparisons to be made between Disease containing databases and allows for increased accuracy in Disease identification through synonym matching. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for Diseases dramatically increases the coverage of the Disease annotation of human genome.
Panagiotis I. Sergouniotis - One of the best experts on this subject based on the ideXlab platform.
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An ontological foundation for ocular phenotypes and rare eye Diseases.
Orphanet Journal of Rare Diseases, 2019Co-Authors: Panagiotis I. Sergouniotis, Emmanuel Maxime, Dorothée Leroux, Ana Rath, Peter N Robinson, Rachel Thompson, Annie Olry, Helene DollfusAbstract:Background The optical accessibility of the eye and technological advances in ophthalmic diagnostics have put ophthalmology at the forefront of data-driven medicine. The focus of this study is rare eye disorders, a group of conditions whose clinical heterogeneity and geographic dispersion make data-driven, evidence-based practice particularly challenging. Inter-institutional collaboration and information sharing is crucial but the lack of standardised terminology poses an important barrier. Ontologies are computational tools that include sets of vocabulary terms arranged in hierarchical structures. They can be used to provide robust terminology standards and to enhance data interoperability. Here, we discuss the development of the ophthalmology-related component of two well-established biomedical ontologies, the Human Phenotype Ontology (HPO; includes signs, symptoms and investigation findings) and the Orphanet Rare Disease Ontology (ORDO; includes rare Disease nomenclature/nosology).
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An ontological foundation for ocular phenotypes and rare eye Diseases
Orphanet Journal of Rare Diseases, 2019Co-Authors: Panagiotis I. Sergouniotis, Emmanuel Maxime, Dorothée Leroux, Ana Rath, Peter N Robinson, Rachel Thompson, Annie Olry, Helene DollfusAbstract:Background The optical accessibility of the eye and technological advances in ophthalmic diagnostics have put ophthalmology at the forefront of data-driven medicine. The focus of this study is rare eye disorders, a group of conditions whose clinical heterogeneity and geographic dispersion make data-driven, evidence-based practice particularly challenging. Inter-institutional collaboration and information sharing is crucial but the lack of standardised terminology poses an important barrier. Ontologies are computational tools that include sets of vocabulary terms arranged in hierarchical structures. They can be used to provide robust terminology standards and to enhance data interoperability. Here, we discuss the development of the ophthalmology-related component of two well-established biomedical ontologies, the Human Phenotype Ontology (HPO; includes signs, symptoms and investigation findings) and the Orphanet Rare Disease Ontology (ORDO; includes rare Disease nomenclature/nosology). Methods A variety of approaches were used including automated matching to existing resources and extensive manual curation. To achieve the latter, a study group including clinicians, patient representatives and Ontology developers from 17 countries was formed. A broad range of terms was discussed and validated during a dedicated workshop attended by 60 members of the group. Results A comprehensive, structured and well-defined set of terms has been agreed on including 1106 terms relating to ocular phenotypes (HPO) and 1202 terms relating to rare eye Disease nomenclature (ORDO). These terms and their relevant annotations can be accessed in http://www.human-phenotype-Ontology.org/ and http://www.orpha.net/ ; comments, corrections, suggestions and requests for new terms can be made through these websites. This is an ongoing, community-driven endeavour and both HPO and ORDO are regularly updated. Conclusions To our knowledge, this is the first effort of such scale to provide terminology standards for the rare eye Disease community. We hope that this work will not only improve coding and standardise information exchange in clinical care and research, but also it will catalyse the transition to an evidence-based precision ophthalmology paradigm.
Celine Schaeffer - One of the best experts on this subject based on the ideXlab platform.
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clinical and genetic spectra of autosomal dominant tubulointerstitial kidney Disease due to mutations in umod and muc1
Kidney International, 2020Co-Authors: Eric Olinger, Patrick Hofmann, Kendrah Kidd, Ines Dufour, Hendrica Belge, Celine SchaefferAbstract:Autosomal dominant tubulointerstitial kidney Disease (ADTKD) is an increasingly recognized cause of end-stage kidney Disease, primarily due to mutations in UMOD and MUC1. The lack of clinical recognition and the small size of cohorts have slowed the understanding of Disease Ontology and development of diagnostic algorithms. We analyzed two registries from Europe and the United States to define genetic and clinical characteristics of ADTKD-UMOD and ADTKD-MUC1 and develop a practical score to guide genetic testing. Our study encompassed 726 patients from 585 families with a presumptive diagnosis of ADTKD along with clinical, biochemical, genetic and radiologic data. Collectively, 106 different UMOD mutations were detected in 216/562 (38.4%) of families with ADTKD (303 patients), and 4 different MUC1 mutations in 72/205 (35.1%) of the families that are UMOD-negative (83 patients). The median kidney survival was significantly shorter in patients with ADTKD-MUC1 compared to ADTKD-UMOD (46 vs. 54 years, respectively), whereas the median gout-free survival was dramatically reduced in patients with ADTKD-UMOD compared to ADTKD-MUC1 (30 vs. 67 years, respectively). In contrast to patients with ADTKD-UMOD, patients with ADTKD-MUC1 had normal urinary excretion of uromodulin and distribution of uromodulin in tubular cells. A diagnostic algorithm based on a simple score coupled with urinary uromodulin measurements separated patients with ADTKD-UMOD from those with ADTKD-MUC1 with a sensitivity of 94.1%, a specificity of 74.3% and a positive predictive value of 84.2% for a UMOD mutation. Thus, ADTKD-UMOD is more frequently diagnosed than ADTKD-MUC1, ADTKD subtypes present with distinct clinical features, and a simple score coupled with urine uromodulin measurements may help prioritizing genetic testing.