Automatic teaching–learning-based optimization: A novel clustering method for gene functional enrichments

Abstract

Multi-objective optimization emerged as a significant research area in engineering studies because most of the real-world problems require optimization with a group of objectives. The most recently developed meta-heuristics called the teaching–learning-based optimization (TLBO) and its variant algorithms belongs to this category. This paper provokes the importance of hybrid methodology by illuminating this meta-heuristic over microarray datasets to attain functional enrichments of genes in the biological process. This paper persuades a novel automatic clustering algorithm (AutoTLBO) with a credible prospect by coalescing automatic assignment of k value in partitioned clustering algorithms and cluster validations into TLBO. The objectives of the algorithm were thoroughly tested over microarray datasets. The investigation results that endorse AutoTLBO were impeccable in obtaining optimal number of clusters, co-expressed cluster profiles, and gene patterns. The work was further extended by inputting the AutoTLBO algorithm outcomes into benchmarked bioinformatics tools to attain optimal gene functional enrichment scores. The concessions from these tools indicate excellent implications and significant results, justifying that the outcomes of AutoTLBO were incredible. Thus, both these rendezvous investigations give a lasting impression that AutoTLBO arises as an impending colonizer in this hybrid approach.