Decentralized load balancing for highly irregular search problems
Microprocessors & Microsystems
A New Linear Initialization in SOM for Biomolecular Data
Computational Intelligence Methods for Bioinformatics and Biostatistics
Improved SOM learning using simulated annealing
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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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.