Ashutosh Malhotra1, Erfan Younesi1, Michaela Gündel2, Bernd Müller2, Michael T Heneka3, Martin Hofmann-Apitius4. 1. Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany; Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany. 2. Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany. 3. Department of Neurology, Clinical Neurosciences Unit, University of Bonn, Bonn, Germany. 4. Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany; Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn-Aachen International Center for IT, Bonn, Germany. Electronic address: martin.hofmann-apitius@scai.fraunhofer.de.
Abstract
BACKGROUND: Biomedical ontologies offer the capability to structure and represent domain-specific knowledge semantically. Disease-specific ontologies can facilitate knowledge exchange across multiple disciplines, and ontology-driven mining approaches can generate great value for modeling disease mechanisms. However, in the case of neurodegenerative diseases such as Alzheimer's disease, there is a lack of formal representation of the relevant knowledge domain. METHODS: Alzheimer's disease ontology (ADO) is constructed in accordance to the ontology building life cycle. The Protégé OWL editor was used as a tool for building ADO in Ontology Web Language format. RESULTS: ADO was developed with the purpose of containing information relevant to four main biological views-preclinical, clinical, etiological, and molecular/cellular mechanisms-and was enriched by adding synonyms and references. Validation of the lexicalized ontology by means of named entity recognition-based methods showed a satisfactory performance (F score = 72%). In addition to structural and functional evaluation, a clinical expert in the field performed a manual evaluation and curation of ADO. Through integration of ADO into an information retrieval environment, we show that the ontology supports semantic search in scientific text. The usefulness of ADO is authenticated by dedicated use case scenarios. CONCLUSIONS: Development of ADO as an open ADO is a first attempt to organize information related to Alzheimer's disease in a formalized, structured manner. We demonstrate that ADO is able to capture both established and scattered knowledge existing in scientific text.
BACKGROUND: Biomedical ontologies offer the capability to structure and represent domain-specific knowledge semantically. Disease-specific ontologies can facilitate knowledge exchange across multiple disciplines, and ontology-driven mining approaches can generate great value for modeling disease mechanisms. However, in the case of neurodegenerative diseases such as Alzheimer's disease, there is a lack of formal representation of the relevant knowledge domain. METHODS:Alzheimer's disease ontology (ADO) is constructed in accordance to the ontology building life cycle. The Protégé OWL editor was used as a tool for building ADO in Ontology Web Language format. RESULTS:ADO was developed with the purpose of containing information relevant to four main biological views-preclinical, clinical, etiological, and molecular/cellular mechanisms-and was enriched by adding synonyms and references. Validation of the lexicalized ontology by means of named entity recognition-based methods showed a satisfactory performance (F score = 72%). In addition to structural and functional evaluation, a clinical expert in the field performed a manual evaluation and curation of ADO. Through integration of ADO into an information retrieval environment, we show that the ontology supports semantic search in scientific text. The usefulness of ADO is authenticated by dedicated use case scenarios. CONCLUSIONS: Development of ADO as an open ADO is a first attempt to organize information related to Alzheimer's disease in a formalized, structured manner. We demonstrate that ADO is able to capture both established and scattered knowledge existing in scientific text.
Authors: Harald Hampel; Nicola Toschi; Claudio Babiloni; Filippo Baldacci; Keith L Black; Arun L W Bokde; René S Bun; Francesco Cacciola; Enrica Cavedo; Patrizia A Chiesa; Olivier Colliot; Cristina-Maria Coman; Bruno Dubois; Andrea Duggento; Stanley Durrleman; Maria-Teresa Ferretti; Nathalie George; Remy Genthon; Marie-Odile Habert; Karl Herholz; Yosef Koronyo; Maya Koronyo-Hamaoui; Foudil Lamari; Todd Langevin; Stéphane Lehéricy; Jean Lorenceau; Christian Neri; Robert Nisticò; Francis Nyasse-Messene; Craig Ritchie; Simone Rossi; Emiliano Santarnecchi; Olaf Sporns; Steven R Verdooner; Andrea Vergallo; Nicolas Villain; Erfan Younesi; Francesco Garaci; Simone Lista Journal: J Alzheimers Dis Date: 2018 Impact factor: 4.472
Authors: Erfan Younesi; Ashutosh Malhotra; Michaela Gündel; Phil Scordis; Alpha Tom Kodamullil; Matt Page; Bernd Müller; Stephan Springstubbe; Ullrich Wüllner; Dieter Scheller; Martin Hofmann-Apitius Journal: Theor Biol Med Model Date: 2015-09-22 Impact factor: 2.432