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Most traditional biclustering algorithms focus on biclustering model on non-continuous column, which are not suitable for the analysis of the time series gene expression data. We proposes an effective and exact algorithm, which can be used to mine biclusters with coherent evolution on the contiguous columns as well as the complementary biclusters and time-lagged biclusters for the analysis of time...
Biclustering algorithm is used to find local patterns as an important tool in the analysis of gene expression data. However, most of the biclusters found by existing biclustering algorithms consist of non-continuous columns. It is not suitable for time series gene expression data, which has not been extensively studied. This paper presents an efficient exact algorithm to search contiguous column coherent...
As an effective biclustering model, order-preserving submatrix (OPSM) has been widely applied to biological gene expression data mining. Recently, biologists hope to find deep OPSMs with long patterns and comparatively few support rows, which are not only useful on the interpretation of gene regulatory networks but also have essential biological significance. Unfortunately, the traditional exact mining...
Biclustering technique has become an important tool in analysis of gene expression data. However, most traditional biclustering algorithms focus on biclustering model on non-continuous column. For time series gene expression data, these models neglect the important internal sequential relationship between the time intervals, thus it is not suitable for this kind of data. This paper proposes an effective...
In this paper, we proposed an exact method to discover all order-preserving submatrices (OPSMs) based on frequent sequential pattern mining. Firstly, an existing algorithm calACS is adjusted to disclose all common subsequences between every two row sequences, therefore all the deep OPSMs corresponding to long patterns with few supporting sequences will not be missed. Then an improved data structure...
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