Visual Data Mining by Virtual Reality for Protein-Protein Interaction Networks
Currently, visualization techniques in the genetic field require a very important modeling phase in terms of resources. Traditional modeling techniques (in two dimensions) are rarely adapted to manage and process this mass of information. To overcome this kind of problem, we propose to use a new graph modeling technique that, used in conjunction with the concept of virtual reality, allows biologists to have a wide visibility through several points of view, thus facilitating them the exploration of massive data. The general principle of our approach is to build a biological network model in the form of a graph with a spatial representation adapted to the visualization of biological networks in a virtual environment. The results show that the improvement of the node distribution algorithm allows a better and more intuitive visualization, compared to the equivalent two-dimensional representations.
virtual reality, information visualization, data mining, massive data, interaction networks, classification method.