Abstract
Order-Preserving SubMatrix (OPSM) has been accepted as a significant tool in modelling biologically meaningful subspace cluster, to discover the general tendency of gene expressions across a subset of conditions. Existing OPSM processing tools focus on giving a or some batch mining techniques, and are time-consuming and do not consider to support OPSM queries. To address the problems, the paper presents and implements a prototype system for OPSM queries, which is called OMEGA (Order-preserving subMatrix mining, indExinG and seArch tool for biologists). It uses Butterfly Network based BSP model to mine OPSMs in parallel. Further, it builds index based on prefix-tree associated with two header tables for gene expression data or OPSM mining results. Then, it processes exact and fuzzy queries based on keywords. Meanwhile, the vital query results are saved for later use. It is demonstrated that OMEGA can improve the effectiveness of OPSM batch mining and queries.
This work was supported in part by the National Basic Research Program 973 of China (No. 2012CB316203), the Natural Science Foundation of China (Nos. 61033007, 61272121, 61332006, 61472321), the National High Technology Research and Development Program 863 of China (No. 2013AA01A215), the Graduate Starting Seed Fund of Northwestern Polytechnical University (No. Z2012128).
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Jiang, T., Li, Z., Chen, Q., Wang, Z., Li, K., Pan, W. (2015). OMEGA: An Order-Preserving SubMatrix Mining, Indexing and Search Tool. In: Bifet, A., et al. Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science(), vol 9286. Springer, Cham. https://doi.org/10.1007/978-3-319-23461-8_35
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DOI: https://doi.org/10.1007/978-3-319-23461-8_35
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