Biological and medical databases continue to grow in size, volume, and dimension that lead to facing big data issues. The data obtained as a result of complex computer modeling, as well as in analyzing various sources of big data are complex and poorly structured. Visualization of such data is an important task for their interpretation that affects a final obtained decision from data. Since traditional approaches such as projection, the use of pictograms, colors, shapes, etc., are not enough to demonstrate the multidimensional relationship, it is necessary to develop a visualization system that is flexible to represent the desired visualization for an expert, medical professional or researcher. The aim of the current paper is the development of visualization systems for multidimensional medical and biological data with additional reality. The main idea is the set of projections from multidimensional space to three- dimensional cube and representation of patients’ data in the form of points cloud. The remarkable advantage is that the proposed system is user-friendly and flexible to define visualization axes. Moreover, additional reality provides a better visualization of the information content. In case of clustering of proteins by genomic signal processing techniques, physico-chemical properties of amino acids can be used to convert an alphabetical sequence to numerical. Since there are many possible conversions using AAindex database, we suggest to use dimensional reduction methods before genomic signal processing. This decreases the time of computation, provides the overall picture of physico-chemical changes and increases the quality of visualization. A wavelet-based algorithm can represent the relationship between proteins in different scales. Using this idea, a user is able to define the visualization scale to see small or large differences between protein sequences.

• https://www.researchgate.net/publication/341655983_3D_Visualization_to_Analyze_Multidimensional_Biological_and_Medical_Data/stats
• https://link.springer.com/chapter/10.1007%2F978-3-030-42176-2_24