An algorithm for discovering deep order preserving submatrix in gene expression data

Abstract

Recently, order preserving submatrix (OPSM) model has been widely applied in many fields, such as biological gene expression data analysis, finance data mining and recommendation system. OPSM model is widely used in gene data expression analysis because of its biological significance and noise robustness. However, most of existing algorithms for OPSMs mining are based on greedy strategy or Apriori principle, which will miss some meaningful OPSMs, especially Deep OPSMs that the biologists are interested in. In this paper, an algorithm for accurate OPSMs searching based on sequential pattern mining was proposed, which could find all OPSMs, especially those Deep OPSMs. The idea of dynamic programming, data structure of suffix tree and the branch and bound rules were combined to improve efficiency of the algorithm. The proposed algorithm was verified by real gene data through experiments on biological significance and algorithm performance. Experimental results demonstrated that it is a high-efficiency algorithm and can find meaningful results.