Knowledge4COVID-19

Knowledge4COVID-19 integrates and analyzes diverse biomedical data to construct a Knowledge Graph (KG) for discovering and predicting adverse drug effects arising from drug-drug interactions among COVID-19 treatments and medications for pre-existing conditions.

Key Features:

• Knowledge Graph Construction: Constructs a Knowledge Graph using the RDF Mapping Language and declarative mapping rules to integrate data from sources including DrugBank and CORD-19.
• Natural Language Processing (NLP): Applies NLP techniques to extract entities and predicates from scientific databases and literature for fine-grained descriptions of COVID-19 treatments and associated adverse events.
• Adverse Drug Effect Discovery: Discovers and predicts drug-drug interactions and potential adverse effects among COVID-19 treatments and medications for comorbidities such as hypertension, diabetes, and asthma.

Scientific Applications:

• Adverse interaction analysis: Enables discovery and prediction of drug-drug interactions and their associated adverse events for COVID-19 therapies.
• Treatment regimen assessment: Supports analysis of interaction risks to inform treatment considerations for patients with comorbidities such as hypertension, diabetes, or asthma.

Methodology:

Constructs a KG using RDF Mapping Language with declarative mapping rules integrating sources such as DrugBank and CORD-19; employs NLP to extract entities and predicates; and applies computational techniques for interaction discovery and prediction.