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KERNEL-BASED SELF-ORGANIZED MAPS TRAINED WITH SUPERVISED BIAS FOR GENE EXPRESSION DATA ANALYSIS
Journal of Bioinformatics and Computational Biology
Self-Organized Maps (SOMs) are a popular approach for clustering data. However, most SOM based approaches ignore prior knowledge about potential categories. Also, Self Organized Map (SOM) based approaches usually develop topographic maps with disjoint and uniform activation regions that correspond to a hard clustering of the patterns at their nodes. We present a novel Self-Organizing map, the Kernel Supervised Dynamic Grid Self-Organized Map (KSDG-SOM). This model adapts its parameters in adoi:10.1142/s021972000400034x pmid:15290758 fatcat:bunjwqwwpzawbj262daulsjkhi