Heat-maps and visualization for heterogeneous biomedical data based on information distance geometry

Systems biology is very much concerned with gaining an overview of what is happening in complex systems, such as in biomedical data sets, for which we need good global visualization tools. This research uses a method based on information distance geometry to create visualizations analogous to heat-maps of prognostic and diagnostic variables. It illustrates the advantages of an informationally self-structuring approach to the understanding of biomedical data.