Visual exploration is a basic tool to inspect or discover data structures. Using visualization techniques relationships among data are displayed as neighborhood relationships that are very easy to understand. In the past years many techniques were developed to represent data and their relationships in many different graphical ways: from the simple dimensionality reduction or projection if the data are in a small dimensional vector space, to mapping techniques or multi dimensional scaling useful if the data are in a high dimensional space; many of these technique are based on machine learning techniques or neural network algorithms. Visualization techniques are even more interesting when a vector space representation is not available and the only suitable data are dissimilarity or similarity measures. In this case the visualization techniques can help to manage a very large amount of data.
The large amount of biological information and the complexity of life sciences creates a difficult problem for information visualization, however application of visualization techniques are beginning to emerge and we want to gather these applications in this special session. We are interested in all the intelligent visualization techniques for biological data; a list of possible application fields is the following:
Riccardo Rizzo, firstname.lastname@example.org
Alfonso Urso, email@example.com
The papers should be in a short format (4 pages of the same format of the CIBB 2008 conference). A cover sheet with the authors names and affiliations is also requested, with the complete address of the corresponding author.
Submission implies the willingness of at least one author per paper to register, attend the workshop, and present the paper.
Important datesJuly 10, 2008: July 21 2008 Paper Submission