Self-organizing maps for drawing large graphs
Information Processing Letters
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Computational Intelligence in Bioinformatics
Computational Intelligence in Bioinformatics
Neural network model for integration and visualization of introgressed genome and metabolite data
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
Identification of trends from patents using self-organizing maps
Expert Systems with Applications: An International Journal
Clustering and visualization of bankruptcy trajectory using self-organizing map
Expert Systems with Applications: An International Journal
A visual analytics framework for cluster analysis of DNA microarray data
Expert Systems with Applications: An International Journal
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Dimensional reduction is a widely used technique for exploratory analysis of large volume of data. In biological datasets, each object is described by a large number of variables (or dimensions) and it is crucial to perform their analyses in a smaller space, to extract useful information. Kohonen self-organizing maps (SOMs) have been recently proposed in systems biology as a useful tool for exploratory analysis, data integration and discovery of new relationships in *omics datasets. SOMs have been traditionally used for clustering in several data mining problems, mainly due to their ability to preserve input data topology and reduce a high dimensional input space into a 2-D map. In spite of this, the above-mentioned dimensional reduction can lead to counterintuitive results. Sometimes, maps having almost the same size, trained on the same dataset, and with identical learning algorithms and parameters, may find different clusters. However, one would expect that small changes in map sizes or another training condition would not result in an abrupt different location of any of the grouped patterns. The aim of this work is to analyze and explain this issue through a real case study involving transcriptomic and metabolomic data, since it might have an important impact when interpreting clustering results over a biological dataset.