Multi-objective evolutionary triclustering with constraints of time-series gene expression data
Lei Chen, Hailin Liu, Weiseng Tang · Integrated Computer-Aided Engineering · 2019
The rapid development of gene expression profiling experiments in recent years has created a great demand for triclustering, i.e., simultaneously clustering genes, conditions or samples and time points of the time-series gene expression data. Triclustering of the time-series gene expression data is of significant importance in biological engineering due to its great potential in identifying key genes in uncharted genome regions. In this paper, a new multi-objective constrained triclustering model is formulated to detect the key genes for time-series gene expression data, where a new objective based on the Wilcoxon sign-rank test is developed to measure the fluctuation of the gene expression values across different time points. A novel population decomposition based evolutionary multi-objective algorithm with customized three-point crossover and mutation operators is proposed for the formulated model. To validate the effectiveness of the proposed method, a series of experimental comparisons are first conducted on a set of artificial benchmark datasets, and then the proposed method is applied on real-life human gene engineering problems of detecting the key gene with similar functionality in biological processes. Experimental results, compared with three previous well-established triclustering algorithms, demonstrate the effectiveness of the proposed method. Furthermore, applications of the proposed triclustering method on biological and computer engineering problems are conducted.