Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) (2006)
Hong Kong, China
Dec. 18, 2006 to Dec. 22, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2006.51
Giuseppe Di Fatta , University of Reading Whiteknights, Reading, Berkshire, RG6 6AY, United Kingdom
Antonino Fiannaca , ICAR-CNR, National Research Council, 90128 Palermo, Italy
Riccardo Rizzo , ICAR-CNR, National Research Council, 90128 Palermo, Italy
Alfonso Urso , ICAR-CNR, National Research Council, 90128 Palermo, Italy
Michael R. Berthold , University of Konstanz, Germany
Salvatore Gaglio , DINFO, The University of Palermo, 90128 Palermo, Italy
Facilitating the visual exploration of scientific data has received increasing attention in the past decade or so. Especially in life science related application areas the amount of available data has grown at a breath taking pace. In this paper we describe an approach that allows for visual inspection of large collections of molecular compounds. In contrast to classical visualizations of such spaces we incorporate a specific focus of analysis, for example the outcome of a biological experiment such as high throughout screening results. The presented method uses this experimental data to select molecular fragments of the underlying molecules that have interesting properties and uses the resulting space to generate a two dimensional map based on a singular value decomposition algorithm and a selforganizing map. Experiments on real datasets show that the resulting visual landscape groups molecules of similar chemical properties in densely connected regions.
S. Gaglio, A. Fiannaca, A. Urso, G. Di Fatta, R. Rizzo and M. R. Berthold, "Context-Aware Visual Exploration of Molecular Datab," Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)(ICDMW), Hong Kong, China, 2006, pp. 136-141.