Discovery of continuous coherent evolution biclusters in time series data

Abstract

Most traditional biclustering algorithms focus on the biclustering model of non-continuous columns, which is unsuitable for analysis of time series gene expression data. We propose an effective and exact algorithm that can be used to mine biclusters with coherent evolution on contiguous columns, as well as complementary and time-lagged biclusters in time series gene expression matrices. Experimental results show that the algorithm can detect biclusters with statistical significance and strong biological relevance. The algorithm is also applied to currency data analysis, in which meaningful results are obtained.