A semi-supervised hierarchical approach: two-dimensional clustering of microarray gene expression data

Authors:
R. Priscilla;S. Swamynathan
Affiliations:
Department of Information and Science and Technology, The College of Engineering, Guindy Campus, Anna University, Chennai, India 600025;Department of Information and Science and Technology, The College of Engineering, Guindy Campus, Anna University, Chennai, India 600025
Venue:
Frontiers of Computer Science: Selected Publications from Chinese Universities
Year:
2013

Citing 5
Cited 0

Fast and effective text mining using linear-time document clustering

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering analysis of microarray gene expression data by splitting algorithm

Journal of Parallel and Distributed Computing - High-performance computational biology
Clustering microarray gene expression data using weighted Chinese restaurant process

Bioinformatics
Semi-supervised clustering with metric learning: An adaptive kernel method

Pattern Recognition
Defining an informativeness metric for clustering gene expression data

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Micro array technologies have become a widespread research technique for biomedical researchers to assess tens of thousands of gene expression values simultaneously in a single experiment. Micro array data analysis for biological discovery requires computational tools. In this research a novel two-dimensional hierarchical clustering is presented. From the review, it is evident that the previous research works have used clustering which have been applied in gene expression data to create only one cluster for a gene that leads to biological complexity. This is mainly because of the nature of proteins and their interactions. Since proteins normally interact with different groups of proteins in order to serve different biological roles, the genes that produce these proteins are therefore expected to co express with more than one group of genes. This constructs that in micro array gene expression data, a gene may makes its presence in more than one cluster. In this research, multi-level micro array clustering, performed in two dimensions by the proposed two-dimensional hierarchical clustering technique can be used to represent the existence of genes in one or more clusters consistent with the nature of the gene and its attributes and prevent biological complexities.