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Constrained query of order-preserving submatrix in gene expression data

Content Provider	Springer Nature Link
Author	Jiang, Tao Li, Zhanhuai Shang, Xuequn Chen, Bolin Li, Weibang Yin, Zhilei
Copyright Year	2016
Abstract	Order-preserving submatrix (OPSM) has become important in modelling biologically meaningful subspace cluster, capturing the general tendency of gene expressions across a subset of conditions. With the advance of microarray and analysis techniques, big volume of gene expression datasets and OPSM mining results are produced. OPSM query can efficiently retrieve relevant OPSMs from the huge amount of OPSM datasets. However, improving OPSM query relevancy remains a difficult task in real life exploratory data analysis processing. First, it is hard to capture subjective interestingness aspects, e.g., the analyst’s expectation given her/his domain knowledge. Second, when these expectations can be declaratively specified, it is still challenging to use them during the computational process of OPSM queries. With the best of our knowledge, existing methods mainly focus on batch OPSM mining, while few works involve OPSM query. To solve the above problems, the paper proposes two constrained OPSM query methods, which exploit userdefined constraints to search relevant results from two kinds of indices introduced. In this paper, extensive experiments are conducted on real datasets, and experiment results demonstrate that the multi-dimension index (cIndex) and enumerating sequence index (esIndex) based queries have better performance than brute force search.
Starting Page	1052
Ending Page	1066
Page Count	15
File Format	PDF
ISSN	20952228
Journal	Frontiers of Computer Science in China
Volume Number	10
Issue Number	6
e-ISSN	20952236
Language	English
Publisher	Higher Education Press
Publisher Date	2016-05-25
Publisher Institution	Chinese Universities
Publisher Place	Beijing
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	gene expression data OPSM constrained query brute-force search feature sequence cIndex Computer Science
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computer Science

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