Rough fuzzy cuckoo search for triclustering microarray gene expression data

Abstract

Analyzing time series microarray gene expression data is a computational challenge due to its threedimensional characteristics. Triclustering techniques are applied to three-dimensional data for mining similarly expressedgenes under a subset of conditions and time points. In this work, a novel rough fuzzy cuckoo search algorithm is proposedfor triclustering genes across samples and time points simultaneously. By applying the upper and lower approximation ofrough set theory and the objective function of fuzzy k-means, rough fuzzy k-means was incorporated into a cuckoo searchto handle the uncertainty of the data. The proposed method was applied to three real-life time series gene expressiondatasets. This work was evaluated using four validation indices and correlation analysis was performed to indicate thecluster quality. The proposed work was also compared with the existing triclustering algorithms and it outperformedthe other methods.