Name
Abstract | ||
|---|---|---|
Drug-drug interactions form a significant risk group for adverse effects associated with pharmaceutical treatment. These interactions are often reported in the literature, however, they are sparsely represented in machine-readable resources, 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 domain. While ontologies for drugs and their effects have proliferated in recent years, there is no ontology capable of describing and categorizing drug-drug interactions. Moreover, there is no artifact that represents all the possible mechanisms 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/ |
Year | Venue | Field |
|---|---|---|
2013 | SWAT4LS | Ontology (information science),Ontology,Ontology-based data integration,World Wide Web,Knowledge representation and reasoning,Process ontology,Open Biomedical Ontologies,Computer science |
DocType | Citations | PageRank |
Conference | 2 | 0.42 |
References | Authors | |
5 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| María Herrero-Zazo | 1 | 88 | 4.76 |
| Janna Hastings | 2 | 714 | 62.06 |
| Isabel Segura-Bedmar | 3 | 435 | 30.96 |
| Samuel Croset | 4 | 18 | 4.51 |
| Paloma Martínez | 5 | 717 | 85.63 |
| Christoph Steinbeck | 6 | 1092 | 94.06 |