Bioinformatics: the machine learning approach
Bioinformatics: the machine learning approach
Microarray data mining: facing the challenges
ACM SIGKDD Explorations Newsletter
A sequential feature extraction approach for naïve bayes classification of microarray data
Expert Systems with Applications: An International Journal
CBR System with Reinforce in the Revision Phase for the Classification of CLL Leukemia
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Feature selection via Boolean independent component analysis
Information Sciences: an International Journal
Partition-conditional ICA for Bayesian classification of microarray data
Expert Systems with Applications: An International Journal
OWA-based linkage method in hierarchical clustering: Application on phylogenetic trees
Expert Systems with Applications: An International Journal
MicroCBR: A case-based reasoning architecture for the classification of microarray data
Applied Soft Computing
Computer Aided Diagnosis tool for Alzheimer's Disease based on Mann-Whitney-Wilcoxon U-Test
Expert Systems with Applications: An International Journal
On the empirical mode decomposition applied to the analysis of brain SPECT images
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Bio-chip data that consists of high-dimensional attributes have more attributes than specimens. Thus, it is difficult to obtain covariance matrix from tens thousands of genes within a number of samples. Feature selection and extraction is critical to remove noisy features and reduce the dimensionality in microarray analysis. This study aims to fill the gap by developing a data mining framework with a proposed algorithm for cluster analysis of gene expression data, in which coefficient correlation is employed to arrange genes. Indeed, cluster analysis of microarray data can find coherent patterns of gene expression. The output is displayed as table list for convenient survey. We adopt the breast cancer microarray dataset to demonstrate practical viability of this approach.