Presentation by Christoph Steinbeck at the workshop hosted by the PhenoMeNal consortium to define the state-of-the-art in Metabolic Phenotyping in the Clinic. The event was attended by a mixed audience from medical/clinical, industry or commercial, government, working or collaborating using metabolomics data and tools and technologies related to clinic.
Proceedings of: The 6th International Workshop on Semantic Web Applications and Tools for Life Sciences (SWAT4LS 2013). Took place 2013, December 11-12, in Edinburgh, UK. The evnt Web site http://www.swat4ls.org/workshops/edinburgh2013/ ; Drug-drug interactions form a significant risk group for adverse effects associ-ated with pharmaceutical treatment. These interactions are often reported in the literature, however, they are sparsely represented in machine-readable re-sources, such as online databases, thesauri or ontologies. These knowledge sources play a pivotal role in Natural Language Processing (NLP) systems since they provide a knowledge representation about the world or a particular do-main. While ontologies for drugs and their effects have proliferated in recent years, there is no ontology capable of describing and categorizing drug-drug in-teractions. Moreover, there is no artifact that represents all the possible mecha-nisms that can lead to a DDI. To fill this gap we propose DINTO, an ontology for drug-drug interactions and their mechanisms. In this paper we describe the classes, relationships and overall structure of DINTO. The ontology is free for use and available at https://code.google.com/p/dinto/ ; This work was supported by the Regional Government of Madrid under the Research Network MA2VICMR [S2009/TIC-1542], by the Spanish Ministry of Education under the project MULTIMEDICA [TIN2010-20644-C03-01] and by the European Commission Seventh Framework Programme under the project TrendMiner_Enlarged (EU FP7-ICT 612336). ; Publicado