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DDI: Knowledge Graph Embedding for Drug-Drug Interaction

Supervised By:
Imran Ihsan
Duration:
2020 - 2021
Client:
Team:
Completed:
Project completed in 34%.
Initial Design

Project description

To find Drug-Drug Interaction using Knowledge Graph and Ontologies

A knowledge graph acquires and integrates information into an ontology and applies a reasoner to derive new knowledge. In other words, a knowledge graph is a programmatic way to model a knowledge domain with the help of subject-matter experts, data interlinking, and machine learning algorithms. It is a powerful way of representing data because it can be built automatically and can then be explored to reveal new insights about the domain and data retrieval can become fast. The characteristics of knowledge graph are: it mainly describes real world entities and their interrelations, organized in a graph; defines possible classes and relations of entities in a schema; allows for potentially interrelating arbitrary entities with each other and covers various topical domains. *Knowledge graphs on the Semantic Web are usually provided using Linked Data Linked Open Data allows the extension of the data models and easy updates. It makes data integration and browsing through complex data become easier and much more efficient. We will be Constructing Knowledge Graphs using Linked Open Data because in our problem we need live and updated information about drugs and its relations as new drugs are discovered more frequently. Drug-Drug Interaction (DDI) is defined as a change in the effects of one drug by the presence of another drug. Drug interactions are very dangerous and fatal, and they need to be detected. The problem is that one patient may see many different doctors and chances are they are not aware of the possible drug interactions prescribed to them. Also, patient groups such as elderly patients and cancer patients are more likely to take multiple drugs at the same time and it increases their risk of DDIs.

Tools and Technologies

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