Abstract: Local pattern mining plays an important role in gene expression data analysis.One classical model in local pattern mining is order-preserving subMatrix (OPSM),which captures the general tendency of subset of genes in subset of conditions.With the development of high-throughput gene microarray techniques,it produces massive of gene expression datasets.In this situation,it is urgent to design high performance algorithms.Most of the existing methods are batch mining technique,even though it can be addressed by query method,the comprehensiveness and behaviors still should be improved.To make data analysis efficient and accurate,we first proposed a prefix-tree based indexing method for gene expression data,then gave a column keyword based OPSM query methods.It uses index and search method instead of batch mining to query positive,negative and time-delayed OPSMs.We conducted extensive experiments and compared our method with existing methods.The experimental results demonstrate that the proposed method is efficient and scalable.

Key words: Gene expression data,Local pattern,Order-preserving submatrix,Keyword-based query