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Data visualization algorithms and feature selection techniques are both widely used in bioinformatics but as distinct analytical approaches. Until now there has been no method of measuring feature saliency while training a data visualization model. We derive a generative topographic mapping (GTM) based data visualization approach which estimates feature saliency simultaneously with the training of...
The visualization of support vector machines in realistic settings is a difficult problem due to the high dimensionality of the typical datasets involved. However, such visualizations usually aid the understanding of the model and the underlying processes, especially in the biosciences. Here we propose a novel visualization technique of support vector machines based on unsupervised learning, specifically...
Hierarchical methods like Ward's and group average (Gave) and nonhierarchical methods like Jarvis Patrick's and k-means are preferred methods to cluster a diverse set of compounds for a number of drug targets (using fingerprints based descriptors). In this work the applications of fuzzy Kohonen neural network and other self-organizing map (SOM) algorithms to the clustering of chemical datasets are...
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