Gene Ontology Analysis of 3D Microarray Gene Expression Data using Hybrid PSO Optimization
N. Narmadha1, R. Rathipriya2

1N. Narmadha, P.hD Research Scholar, Department of Computer Science, Periyar University, Salem -636 011, Tamil Nadu, India.
2Dr. R.Rathipriya, Assistant Professor , Department of Computer Science, Periyar University, Salem -636 011, Tamil Nadu, India.
Manuscript received on 24 August 2019. | Revised Manuscript received on 03 September 2019. | Manuscript published on 30 September 2019. | PP: 3890-3896 | Volume-8 Issue-11, September 2019. | Retrieval Number: K12610981119/2019©BEIESP | DOI: 10.35940/ijitee.K1261.0981119
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: At present, triclustering is the well known data mining technique for analysis of 3D gene expression data (GST). Triclustering is a simultaneously clustering of subset of Gene (G), subset of Sample (S), and over a subset of Time point (T). Triclustering approach identifies a coherent pattern in the 3D gene expression data using Mean Correlation Value (MCV). In this chapter, Hybrid PSO based algorithm is developed for triclustering of 3D gene expression data. This algorithm can effectively find the coherent pattern with high volume of a tricluster. The experimental study is conducted on yeast cycle dataset to study the biological significance of the coherent tricluster using gene ontology tool.
Keywords: Gene Ontology, Hybrid Particle Swarm Optimization (Hybrid PSO), Gene Expression Data, Triclustering, Yeast Cell Cycle data.
Scope of the Article: Discrete Optimization