Constrained query of order-preserving submatrix in gene expression data  

作　　者：Tao JIANG Zhanhuai LI Xuequn SHANG Bolin CHEN Weibang LI Zhilei YIN 

机构地区：[1]School of Computer Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China

出　　处：《Frontiers of Computer Science》2016年第6期1052-1066,共15页中国计算机科学前沿（英文版）

基　　金：The authors thank the anonymous referees for their useful comments that greatly improved the quality of the paper. This work was supported in part by the National Basic Research Program 973 of China （2012CB316203）, the Natural Science Foundation of China （Grant Nos. 61033007, 61272121, 61332014, 61572367, 61332006, 61472321, and 61502390）, the National High Technology Research and Development Program 863 of China （2015AA015307）, the Fundational Research Funds for the Central Universities （3102015JSJ0011, 3102014JSJ0005, and 3102014JSJ0013）, and the Graduate Starting Seed Fund of Northwestern Polytechnical University （Z2012128）.

摘　　要：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 fo- cus 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.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 fo- cus 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.

关 键 词：gene expression data OPSM constrained query brute-force search feature sequence cIndex 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] Q78[自动化与计算机技术—计算机科学与技术]