document: Discovering significant OPSM subspace clusters in massive gene expression data Order-preserving submatrixes ( OPSMs ) have been accepted as a biologically meaningful subspace cluster model , capturing the general tendency of gene expressions across a subset of conditions . In an OPSM , the expression levels of all genes induce the same linear ordering of the conditions . OPSM mining is reducible to a special case of the sequential pattern mining problem , in which a pattern and its supporting sequences uniquely specify an OPSM cluster . Those small twig clusters , specified by long patterns with naturally low support , incur explosive computational costs and would be completely pruned off by most existing methods for massive datasets containing thousands of conditions and hundreds of thousands of genes , which are common in today 's gene expression analysis . However , it is in particular interest of biologists to reveal such small groups of genes that are tightly coregulated under many conditions , and some pathways or processes might require only two genes to act in concert . In this paper , we introduce the KiWi mining framework for massive datasets , that exploits two parameters k and w to provide a biased testing on a bounded number of candidates , substantially reducing the search space and problem scale , targeting on highly promising seeds that lead to significant clusters and twig clusters . Extensive biological and computational evaluations on real datasets demonstrate that KiWi can effectively mine biologically meaningful OPSM subspace clusters with good efficiency and scalability .
doc_bio_tags: O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O