DNA microarrays have been applied successfully in diverse research fields such as gene discovery, disease diagnosis and drug discovery. The roles of the genes and the mechanisms of the underlying diseases can be identified using microarrays. Biclustering is a two dimensional clustering problem, where we group the genes and samples simultaneously. It has a great potential in detecting marker genes that are associated with certain tissues or diseases. The proposed work finds the significant biclusters in large expression data using the Cuckoo Search with Mutation (CSM). The cuckoo imitates its egg similar to host bird’s egg using a mutation operator. Mutation is used for exploration of search space, more precisely to allow candidates to escape from local minima. It focuses on finding maximum biclusters with lower Mean Squared Residue (MSR) and higher gene variance. A qualitative measurement of the formed biclusters with a comparative assessment of results is provided on four benchmark gene expression dataset. To demonstrate the effectiveness of the proposed method, the results are compared with the swarm intelligence techniques Binary Particle Swarm Optimization (BPSO), Shuffled Frog Leaping (SFL), and Cuckoo Search with Levy flight (CS) algorithm. The results show that there is significant improvement in the fitness value. © 2017, Zarka Private University. All rights reserved.