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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 deciding 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 model. The approach not only provides a better projection by modeling irrelevantdoi:10.1109/cibcb.2006.330985 dblp:conf/cibcb/ManiyarN06 fatcat:dxknnbw52ne5dj25xl2sibapya