Fuzzy soft subspace clustering method for gene co-expression network analysis

AbstractGene expression clustering methods for building gene co-expression networks suffer greatly from the biological complexity of cells. This paper proposes a fuzzy soft subspace clustering method for detecting overlapped clusters of locally co-expressed genes that may participate in multiple cellular processes and take on different biological functions. Process-specific cluster subspaces and interactions among different gene clusters can be extracted by this method, providing useful information for gene co-expression networks analysis. Experiments on the yeast cell cycle benchmark microarray data have shown that this method is effective in extracting underlying biological relationships between genes, and enhancing gene co-expression network inference.

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