Name
Abstract | ||
|---|---|---|
Cluster structure of gene expression data obtained from DNA microarrays is analyzed and visualized with the Self-Organizing Map (SOM) algorithm. The SOM forms a nonlinear mapping of the data to a two-dimensional map grid that can be used as an exploratory data analysis tool for generating hypotheses on the relationships, and ultimately of the function of the genes. Similarity relationships within the data and cluster structures can be visualized and interpreted. The methods are demonstrated by... |
Year | Venue | DocType |
|---|---|---|
2002 | Neural Networks | Journal |
Volume | Issue | Citations |
15 | 8-9 | 11 |
PageRank | References | Authors |
1.66 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samuel Kaski | 1 | 2755 | 245.52 |
| Janne Nikkilä | 2 | 200 | 16.65 |
| Petri Törönen | 3 | 254 | 16.32 |
| E. Castrén | 4 | 11 | 1.66 |
| Garry Wong | 5 | 89 | 9.62 |