Diabetes knowledge graph: integrating multi-source data using neo4j

Abstract

Diabetes is a globally prevalent condition with complex causes and severe complications, including Diabetic Nephropathy, Diabetic Retinopathy, Diabetic Neuropathy, Atherosclerosis, Cardiovascular Disease, Tuberculosis Type 2 Diabetes Mellitus (TBT2DM), Dyslipidemia-Associated Atheroscle rosis, Dyslipidemia-Associated Psoriasis, and Insulin Resistance Genes. Understanding the genetic, molecular, and regulatory factors behind these complications is essential for advancing treatments and improving outcomes. Our project introduces a comprehensive Knowledge Graph (KG) that integrates diverse data into an accessible and interactive framework. The Knowledge Graph combines information about Gene, Proteins, transcription factors and their Targets, Diabetes and its complications, miRNAs interactions, drugs, chemicals interactions, and protective genes, providing a detailed perspective on the disease. A user-friendly query system powered by a Neo4j has been developed to make exploring this interconnected network straightfor ward, with future plans to enhance functionality through advanced AI technologies frameworks like Gemini to enhance user interaction and provide intuitive, human-like responses. This initiative is designed to support researchers and clinicians in uncovering valuable insights into diabetes, enabling more effective strategies for understanding and addressing this complex condition.