Automatic Teaching–Learning-Based Optimization: A Novel Clustering Method for Gene Functional Enrichments - Computational Intelligence Techniques for Comparative Genomics - page 16

Automatic Teaching - Learning-Based
Optimization: A Novel Clustering Method
for Gene Functional Enrichments

Ramachandra Rao Kurada, K. Karteeka Pavan and Allam Appa Rao

Abstract Multi-objective optimization emerged as a signi cant 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 pro

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les,
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 signi cant 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.