Biomedical Information Visualization

  • Mircea Lungu
  • Kai Xu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4417)


The ongoing developments in the fields of molecular biology, genetics, and medical sciences have recently generated an explosion of information collected from living organisms. For the scientist who analyzes these huge amounts of data, information visualization can be a very useful tool.

Because an exhaustive survey of the use of information visualization in the molecular biology, genetics and medical science is beyond the scope or space of this chapter, the chapter will present several highlights on the use of information visualization in the aforementioned domains.

The reader is not expected to be an expert in any of the analyzed fields, in fact, the material is organized in such a way that it does not make any assumptions on reader’s domain knowledge. This means that for the domain expert, the level of discourse might be superficial, but one can always refer to the original papers for more details. The assumption that the reader is not an expert has also determined our organization of the material: the analyzed tools are grouped according to problem domains, and each section begins by providing a brief introduction to the domain before presenting the tools and techniques. The problem domains that are addressed are the following:

  • Phylogenetic Tree Visualization (Section 8.1)

  • Sequence Alignment (Section 8.2)

  • Biochemical Network Analysis (Section 8.3)

  • Microarray Data Visualization (Section 8.4)

  • Medical Records Visualization (Section 8.5)

The analysis of each tool described in this chapter is based on several of the following information visualization issues which are partially inspired by the work of Card et al. [145]:

  • Space and Colors. The most basic decision to be taken for the design of a visual representation is how to use the spatial axes. Some data is inherently linear (e.g., DNA sequences), and the reader will see that this has a strong effect on the associated visualizations. Other data are naturally represented using two or three dimensions. Is the space used in an adequate way for the given data? How do the colors provide feedback? Do they reinforce spatial information?

  • Data Magnitude. The magnitude of the visualized data is important for the techniques that are used. For the same type of data, different magnitudes might enforce the use of different visualization techniques, as the reader will see in Section 8.1.

  • Interaction. Most of the times, interaction plays a critical role in the process of visual analysis. Some of the analyzed tools employ specific and sometimes innovative interaction modes while others provide only the traditional navigation primitives (e.g., navigating in 3D space).

  • Limitations. Where limitations of the techniques are apparent (e.g., the visualization does not scale, the tool can be used only in a particular context, ...) they are mentioned together with possible improvements.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Mircea Lungu
    • 1
  • Kai Xu
    • 2
  1. 1.University of LuganoSwitzerland
  2. 2.National ICTAustralia

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