Accessibility navigation

Context-aware visual exploration of molecular databases

Di Fatta, G., Fiannaca, A., Rizzo, R., Urso, A., Berthold, M. and Gaglio, S. (2006) Context-aware visual exploration of molecular databases. In: Int.l Workshop on Data Mining in Bioinformatics, 6th IEEE Int.l Conference on Data Mining ICDM 2006, 18-22 December 2006,, Hong Kong, China, pp. 136-141,

Text - Accepted Version
· Please see our End User Agreement before downloading.


It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1109/ICDMW.2006.51


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 self organizing map. Experiments on real datasets show that the resulting visual landscape groups molecules of similar chemical properties in densely connected regions.

Item Type:Conference or Workshop Item (Paper)
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:4495
Uncontrolled Keywords:biology computing , data visualisation , molecular biophysics , scientific information systems , self-organising feature maps , singular value decomposition classical visualizations , context-aware visual exploration , life science , molecular databases , scientific data , self-organizing map , singular value decomposition , visual inspection , visual landscape groups molecules


Downloads per month over past year

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation