Clustering biological data with SOMs: on topology preservation in non-linear dimensional reduction
- Milone, Diego Humberto
- Stegmayer, Georgina
- Kamenetzky, Laura
- Lopez, Mariana Gabriela
- Carrari, Fernando Oscar
DOIAbstract
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...
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