Expression cartography of human tissues using self organizing maps by Henry Wirth; Markus Löffler; Martin von Bergen; Hans Binder. (BMC Bioinformatics. 2011;12:306)
Parallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge is the machine learning technique known as self organizing maps (SOMs). SOMs enable a parallel sample- and gene-centered view of genomic data combined with strong visualization and second-level analysis capabilities. The paper aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and practical applications with special emphasis on gene expression analysis on the other hand.
A nice introduction to self organizing maps (SOMs) in a bioinformatics context. Think of them as being yet another way to discover subjects about which people want to make statements and to attach data and analysis.