Mining Synchronous and Asynchronous Co Regulated Gene Clusters from Time Series Microarray Data
Gene co regulation falls into two major categories, i.e. synchronous and asynchoronous co regulation. This paper proposes a new model Reg Cluster, which groups synchronous and asynchoronous co regulated genes together if they have the same code. Further, an effective clustering algorithm with several efficient pruning rules, namely FBLD, is designed to identify all maximal Reg Clusters in a "First Breadth first and Last Depth first" manner. The resultant clusters contain the detailed and complete co regulation information, which facilitates the study of genetic regulatory networks. Moreover, the method can be extended to the analysis of 3D gene sample time microarray data. The FBLD algorithm has been implemented on both real and synthetic datasets and the results from the real dataset has been submitted to Gene Ontology. Experimental results prove the effectiveness and efficiency of the proposed method.