Hybrid Cuckoo Search with Clonal Selection for Triclustering Gene Expression Data of Breast Cancer

Abstract

Triclustering techniques are applied to analyze three-dimensional gene expression microarray data to retrieve group of genes under the tested samples over certain time points based on a similarity measure. The real-life three-dimensional dataset chosen is estrogen induced breast cancer dataset. In such datasets, identifying the variations in mining the gene expressions across samples and time points is a difficult task. In this work, the hybrid cuckoo search with clonal selection is applied to extract co-expressed genes over samples and times with triclustering solution. The cuckoo eggs are procreated based on the clonal selection theory. Clonal selection generates the variability required for the solution without any substantial modification. The findings are then used to determine the biological significance of genes in the resulting cluster using gene ontology, functional annotation, and transcription factor binding sites. The proposed work identifies the top genes SRY, SOX5, PAX4, NKX61, SP1, OCT, CDP, POU3F2 and PAX4 present in the tricluster which are associated with the breast cancer. To learn the performance of the proposed work, the experiment results are analyzed with conventional cuckoo search. The hybrid cuckoo search with clonal selection outperforms both the conventional cuckoo search and the other existing triclustering algorithms.

KEYWORDS:

• Breast cancer
• Clonal selection
• Cuckoo search
• Gene ontology
• Microarray gene expression data
• Multi-objective optimization
• TFBS analysis
• Tricluster

Additional information

Notes on contributors

P. Swathypriyadharsini

P Swathypriyadharsini is currently working as an assistant professor in the Department of Computer Science Engineering at Bannari Amman Institute of Technology, Erode, Tamil Nadu, India. She completed her Master of Engineering in computer science engineering (CSE) at Bannari Amman Institute of Technology, Erode, Tamil Nadu, India and Bachelor of Engineering in CSE at Avinashilingam University, Coimbatore, Tamil Nadu, India. Her research interests include data mining, soft computing and artificial intelligence.

K. Premalatha

K Premalatha is currently working as a professor and head in the Department of Computer Science Engineering at Bannari Amman Institute of Technology, Erode, Tamil Nadu, India. She completed her PhD in CSE at Anna University, Chennai, India. She did her Master of Engineering in CSE and Bachelor of Engineering in CSE at Bharathiar University, Coimbatore, Tamil Nadu, India. Her research interests include data mining, networking, information retrieval and soft computing. Email: [email protected]