Robust PCA and classification in biosciences
Bioinformatics
Clustering of time-course gene expression data using functional data analysis
Computational Biology and Chemistry
Computational Biology and Chemistry
Computational Biology and Chemistry
Computational Statistics & Data Analysis
Comparative analysis of classification methods for protein interaction verification system
ADVIS'06 Proceedings of the 4th international conference on Advances in Information Systems
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High-throughput screening technologies recently developed allow scientists to conduct millions of biological and medical tests simultaneously and rapidly. A major bottleneck for the analysis is to reduce the inherent high dimensionality for subsequent analysis. Principal Component Analysis PCA is a popular tool for dimensionality reduction by selecting typically a few Principal Components PCs ranked by their variances, eigenvalues. Since this selection approach is not always effective in reducing dimensionality, we consider a different ranking criterion, the canonical variate criterion. To further enhance the classification performance, we propose an integrated classification framework to combine the criterion and two hybrid classification methods and compare with several popular classification methods using leave-one-out cross-validation. For illustration, three real high-throughput data sets are considered and analysed to illustrate the methods.