Constanze Schmitt, Matthias Böck, and Stefan Kramer (2011)
SOM Biclustering of Gene Expression Data
In: Proceedings of the Fifth International Workshop on Machine Learning in Systems Biology (MLSB11), ed. by S. Kramer and N. Lawrence, pp. 78-81.
Self-Organising Maps (SOMs) are an unsupervised learning
mechanism chiefly used for dimensionality reduction in high-dimensional
data. This makes them particularly appropriate when dealing with gene
expression microarray data, where they are invaluable for exploratory
data analysis, such as cluster identification. The classical SOM approach
performs clustering in only one dimension. However, with multiple gene
expression chips describing different experimental conditions or individ-
uals, subspace clustering is far more adapted to detect patterns of co-
expressed genes present in only a subset of the samples. So far, Self-
Organising Maps have been very little employed in the biclustering con-
text. This paper describes a probabilistic extension of a SOM-Biclustering
approach by Cottrel et al. [4] and assesses its performance with regard
to both synthetic and biological data.
RECESS
Programming Languages
