VizCluster and its Application on Classifying Gene Expression Data
Distributed and Parallel Databases
A Computational Approach to Gene Expression Data Extraction and Analysis
Journal of VLSI Signal Processing Systems
Visual Analysis of Gel-Free Proteome Data
IEEE Transactions on Visualization and Computer Graphics
| Hi-index | 0.00 |
HCDMG03Currently, the cDNA and genomic sequence projects are processing at such a rapid rate that more and more gene data become available. New methods are needed to efficiently and effectively analyze and visualize this data. In this paper, we present a visualization method which maps the samples' n-dimensional gene vectors into 2-dimensional points. This mapping is effective in keeping correlation coefficient similarity which is the most suitable similarity measure for analyzing the gene expression data. Our analysis method first removes noise genes from the gene expression