GDPKG-LLM: Integrating Gene, Disease, and Pharmacogenomics Knowledge Graphs for Cognitive Neuroscience Using Large Language Models

Using the structures of large language models (LLMs) in creating knowledgediagrams to understand more about the relationship between the entities ofcognitive and biological sciences has become a hot point of research. Due to thegreat knowledge behind the curtain and the deep connections of this research,it is not possible to use the traditional approaches of machine learning and deeplearning. In this study,the main goal is to create a comprehensive and integratedknowledge graph(KG) from the combination of three knowledge sources: GeneOntology (GO), Disease Ontology (DO), and PharmKG. Large language models(LLMs) have been used to create this knowledge base. The main purpose ofthis KG is to understand the relationships between genes, diseases and drugs.The pro- posed approach was called GDPKG-LLM. It has several key steps,including entity matching, similarity analysis, graph alignment and using GPT-4. GDPKG-LLM was able to extract more than 16,800 nodes and 838,000 edgesfrom these three knowledge bases and provide a rich KG. This graph providesmeaningful relationships, making it a valuable resource for future research inpersonalized medicine and neuroscience. The reviewed evaluation criteria showthe superiority of GDPKG-LLM, which strengthens the validity of this model.