VR-Based Visualization for Biological Networks

The current revolution in technology forces most daily life systems to generate large-scale data. Understanding such data is not an easy task due to its size and complexity. Therefore, data visualization has played a key role in understanding data more clearly. Moreover, most of the available approaches for data visualization are considered traditional that deal with data in a 2D way. Furthermore, one of the most modern methods for data visualization is converting data into network models that include nodes and edges. This kind of visualization has been proven as an efficient method of analyzing large-scale data. On the other hand, with the advent of virtual reality technology and the metaverse concept, data can be visualized and analyzed in an interactive and 3D way. However, there is a lack in the literature providing enough information on how to generate such 3D models. This thesis comes to investigate the integration of virtual reality technology and complex networks, and how this integration can generate interactive data visualization models. To this end, a 3D VR-based interactive biological network model is designed and implemented to be adoptable by the metaverse platform. The biological network represents the gene-gene interactions of the sex chromosomes. The developed model enables analysts to dive into data and interact with its objects, which lead to a more professional, deep, and accurate analysis. The model proved its efficiency in terms of usability when tested by experts.