Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A novel ensemble machine learning for robust microarray data classification
Computers in Biology and Medicine
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Comparing ensembles of learners: detecting prostate cancer from high resolution MRI
CVAMIA'06 Proceedings of the Second ECCV international conference on Computer Vision Approaches to Medical Image Analysis
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
An effective double-bounded tree-connected Isomap algorithm for microarray data classification
Pattern Recognition Letters
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The recent explosion in availability of gene and protein expression data for cancer detection has necessitated the development of sophisticated machine learning tools for high dimensional data analysis. Previous attempts at gene expression analysis have typically used a linear dimensionality reduction method such as Principal Components Analysis (PCA). Linear dimensionality reduction methods do not however account for the inherent nonlinearity within the data. The motivation behind this work is to demonstrate that nonlinear dimensionality reduction methods are more adept at capturing the nonlinearity within the data compared to linear methods, and hence would result in better classification and potentially aid in the visualization and identification of new data classes. Consequently, in this paper, we empirically compare the performance of 3 commonly used linear versus 3 nonlinear dimensionality reduction techniques from the perspective of (a) distinguishing objects belonging to cancer and non-cancer classes and (b) new class discovery in high dimensional gene and protein expression studies for different types of cancer. Quantitative evaluation using a support vector machine and a decision tree classifier revealed statistically significant improvement in classification accuracy by using nonlinear dimensionality reduction methods compared to linear methods.