Name
Abstract | ||
|---|---|---|
Motivation: Unsupervised analysis of microarray gene expression data attempts to find biologically significant patterns within a given collection of expression measurements. For example, hierarchical clustering can be applied to expression profiles of genes across multiple experiments, identifying groups of genes that share similiar expression profiles. Previous work using the support vector machine supervised learning algorithm with microarray data suggests that higher-order features, such as pairwise and tertiary correlations across multiple experiments, may provide significant benefit in learning to recognize classes of co-expressed genes. Results: We describe a generalization of the hierarchical clustering algorithm that efficiently incorporates these higher-order features by using a kernel function to map the data into a high-dimensional feature space. We then evaluate the utility of the kernel hierarchical clustering algorithm using both internal and external validation. The experiments demonstrate that the kernel representation itself is insufficient to provide improved clustering performance. We conclude that mapping gene expression data into a high-dimensional feature space is only a good idea when combined with a learning algorithm, such as the support vector machine that does not suffer from the curse of dimensionality. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1093/bioinformatics/btg288 | BIOINFORMATICS |
Keywords | Field | DocType |
kernel function,microarray data,supervised learning,gene cluster,feature space,curse of dimensionality,higher order,hierarchical clustering,support vector machine,source code,external validity | Data mining,CURE data clustering algorithm,Computer science,Artificial intelligence,Conceptual clustering,Cluster analysis,Hierarchical clustering,Canopy clustering algorithm,Clustering high-dimensional data,Pattern recognition,Correlation clustering,Bioinformatics,Kernel method | Journal |
Volume | Issue | ISSN |
19 | 16.0 | 1367-4803 |
Citations | PageRank | References |
20 | 1.49 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Qin | 1 | 20 | 3.18 |
| Darrin P. Lewis | 2 | 132 | 15.26 |
| William Stafford Noble | 3 | 2907 | 203.56 |