These are my notes for the seventh session of talks at the UK Ontology Network Meeting on 14 April, 2016.
DReNIn_O: An application ontology for drug repositioning
Joseph Mullen, Anil Wipat, Simon Cockell
Drug repositioning is identifying new uses for existing drugs. Most marketed examples are found due to luck, and therefore there is a need for more systematic approaches to enable a more holistic view of drug interactions. To do this automatically, you would want to infer the link from drug to disease by looking at the targets for the drug.
A very high-level ontology was used in the project with 25 classes (http://drenin.ncl.ac.uk). The database has over 8 million triples, and there is a SPARQL endpoint.
Pharmacological Data Integration for Drug-Drug Interactions: Recent Developments and Future Challenges
Maria Herrero-Zazo, Isabel Segura-Bedmar, Paloma Martinez
DDI information resources are numerous, but hard to keep everything updated and integrated. There is a need for a tool that can predict DDIs and that can filter and find the desired information. Therefore they created DINTO (The DDI Ontology). They followed the Neon methodology to create the new ontology. They have integrated ontologies such as ChEBI and PKO, BRO, OAE. They also used SWRL (over 100 rules) to help them infer new interactions. DINTO is the largest and most comprehensive ontology in the DDI domain.
Exploring modelling choices: using non-functional characteristics to test an ontology add-on
Jennifer Warrender, Phillip Lord
Developed the Karyotype Ontology to address problems of karyotype representation. Karyotypes are complicated, not computationally amenable (images), and there are lots of them.
Karyotype Ontology was built using Tawny-OWL. It was built with a pattern-driven approach, allowing them to rapidly change the ontology even if it’s very large. One complex question was how to model the “affects” relationship, as it is difficult to determin a priori which representation would work the best. They investigated 3 “affects” models. With each, you implement the model in Tawny-OWL, generate multiple versions (1600 of various sizes) of the Karyotype ontology and then reason over them and examine how the ontologies scaled.
In this way you can determine which model for “affects” is best for the ontology wrt reasoning and scalability. Obvious result – increase in karyotypes == increase in reasoning time. Each model for “affects” is better for different purposes. Therefore, with Tawny-OWL, you can allow your users to choose which model is best suited to their needs.
Please note that this post is merely my notes on the presentation. I may have made mistakes: these notes are not guaranteed to be correct. Unless explicitly stated, they represent neither my opinions nor the opinions of my employers. Any errors you can assume to be mine and not the speaker’s. I’m happy to correct any errors you may spot – just let me know!