Title: Discovery of continuous coherent evolution biclusters in time series data

Authors: Meihang Li; Yun Xue; Haolan Zhang; Bo Ma; Jie Luo; Wensheng Chen; Zhengling Liao

Addresses: Laboratory of Quantum Engineering and Quantum Materials, School of Physics and Telecommunication Engineering, South China Normal University, Guangdong, 510006, China ' Laboratory of Quantum Engineering and Quantum Materials, School of Physics and Telecommunication Engineering, South China Normal University, Guangdong, 510006, China ' Center for SCDM, NIT, Zhejiang University, Hangzhou, 310058, China ' School of Physics and Telecommunication Engineering, South China Normal University, Guangdong, 510006, China ' School of Physics and Telecommunication Engineering, South China Normal University, Guangdong, 510006, China ' College of Mathematics and Computational Science, Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen, 518060, China ' School of Physics and Telecommunication Engineering, South China Normal University, Guangdong, 510006, China

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.

Keywords: time series data; bicluster; coherent evolution; complementary; time-lagged.

DOI: 10.1504/IJCSE.2018.094927

International Journal of Computational Science and Engineering, 2018 Vol.17 No.2, pp.170 - 183

Received: 15 Sep 2015
Accepted: 28 May 2016
Published online: 27 Sep 2018 *

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