Independent Subspaces of Gene Expression Data

Independent subspace anlaysis (ISA) is a linear modelbased method which generalizes independent component analysis (ICA) by incorporating the invariant feature subspace into multidimensional ICA. In this paper we apply ISA to the problem of gene expression data analysis and show the useful behavior of the independent subspaces of gene expression data in the task of gene clustering and gene-gene interaction analysis.

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