Self-organizing maps for the analysis of NMR spectra

Abstract The ability of the human brain to form topological maps by self-organizing neuronal connectivity in an unsupervised manner inspired the development of a powerful computational tool, the self-organizing map (SOM). SOMs are used to cluster large amounts of data, simplifying and streamlining the laborious process of data interpretation. High-throughput screening techniques are a fixture in today's pharmaceutical industry, but mining the wealth of data afforded by such tools is an evolving process. In the past few years, SOMs have been married with nuclear magnetic resonance (NMR) to cluster NMR spectra in an unsupervised manner, finding new relationships and greatly reducing the time scientists must spend interpreting the spectra.

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