Utilizing Graph Database for Inferring Domain-Disease Associations

Graph Databases have been used widely in different areas. Owing to the type of representation they offer, they have gained popularity in disciplines where the interconnection of the data is a substantial matter. With the amount of interconnected data that the era of omics has resulted in, analyzing this data is an important task in medicine, drug design, and many other related fields. This can be done with the help of graph databases. In this paper, a novel multi-bipartite heterogeneous biological graph model is provided. It has been implemented and stored in the graph database Neo4j. Moreover, a new modified version of degree centrality (hereafter ”Disease Degree Centrality”) is adapted to aid in extracting and mining for meaningful insights from the graph model in hand. We calculated the Disease Degree Centrality for the intended node and we reported the most important protein domains. Finally, we analysed our results on a case study of Menkes and Wilson diseases using DAVID and InterPro databases.