Constrained query of order-preserving submatrix in gene expression data

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 conditions. With advance microarray and analysis techniques, big volume expression datasets OPSM mining results are produced. query can efficiently retrieve relevant OPSMs from huge amount datasets. However, improving relevancy remains difficult task real life exploratory data processing. First, it is hard to capture subjective interestingness aspects, e.g., analyst's expectation given her/his domain knowledge. Second, when these expectations be declaratively specified, still challenging use them during computational process queries. best our knowledge, existing methods mainly focus on batch mining, while few works involve query. To solve above problems, paper proposes two constrained methods, which exploit userdefined constraints search kinds indices introduced. In this paper, extensive experiments conducted datasets, experiment demonstrate that multi-dimension index (cIndex) enumerating sequence (esIndex) based queries have better performance than brute force search.