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*'''Paper Submission Deadline:''' February 15, 2007
*'''Paper Submission Deadline:''' February 15, 2007
jointly with:
* 11th International Conference on Information Visualisation IV07 in Zurich, Switzerland -- 4-6 July, 2007
We are soliciting original papers in the area of information visualization applied to biomedical and biomolecular data, including genomic and proteomic data. One of the great challenges is the presentation of and interaction with large and complex data sets. The need for integrated methods and creative strategies has provided an opportunity for novel solutions. We encourage the submission of new techniques, old techniques applied in novel ways, new methods, interesting applications and in-depth surveys.


*'''Main topic areas:'''  
*'''Main topic areas:'''  

Revision as of 09:33, 30 December 2006

  • Paper Submission Deadline: February 15, 2007

jointly with:

  • 11th International Conference on Information Visualisation IV07 in Zurich, Switzerland -- 4-6 July, 2007

We are soliciting original papers in the area of information visualization applied to biomedical and biomolecular data, including genomic and proteomic data. One of the great challenges is the presentation of and interaction with large and complex data sets. The need for integrated methods and creative strategies has provided an opportunity for novel solutions. We encourage the submission of new techniques, old techniques applied in novel ways, new methods, interesting applications and in-depth surveys.

  • Main topic areas:
  • Examples of application topics include, but are not limited to
    • Microarray data, design and analysis
    • Nucleotide and protein sequence alignment
    • Sequence analysis, comparison and alignment networks
    • Evolutionary phylogenetic trees
    • Gene expression and gene discovery
    • Signaling pathways, biochemical networks
    • Biochemical and cellular simulations and models
    • Mass spectrometry data analysis
  • Examples of visualization topics include
    • Interactions with large data sets
    • Handling labels in high-dimensional visualizations
    • Linking literature and semantics in pathway visualizations
    • Systems-level analysis of experimental data
    • Overview and detail presentation of predictive or uncertain data
    • Identification of correlated and anomalous relationships in disparate data sets