Clustering microarray gene expression data using enhanced harmony search. (2015) - Pandi, M.; Premalatha, K.
The DNA microarray technology concurrently monitors the expression levels of thousands of genes during significant biological processes and across the related samples. The better understanding of functional genomics is obtained by extracting the patterns hidden in gene expression data. It is handled by clustering which reveals natural structures and identify interesting patterns in the underlying data. In the proposed work clustering gene expression data is done through an enhanced harmony search (EHS) algorithm. Harmony search (HS) was inspired by the musical improvisation process where musicians improvise their instruments' pitches searching for a perfect state of harmony. In EHS the intensification and diversification process is incorporated in HS by smoothing the pitch values and replacing a fraction of instruments with new instruments. The experiment results are analysed with optimisation benchmark test functions and gene expression benchmark datasets. The results show that EHS outperforms HS in both benchmarks. Also this work determines the biological validation of the clusters with gene ontology in terms of function, process and component.