| Publisher | Universidade de Sao Paulo | ||
|---|---|---|---|
| Format | 631.2KB PDF | Date added | 21 Sep 2004 |
| Topics | Parallel Processing, Software Engineering | ||
| Downloads | 7 | ||
GCLASS is an algorithm which explores small samples of two distinct biological states for finding small sets of genes, which form a feature vector that is enough to separate these two states. A typical sample is a set of 60 microarrays, 30 for each biological state, with several thousand genes. The technique consists of the following: a spreading model defined in the space of small sets of genes studied and centered in each feature vector considered; the designing of optimal linear classifiers under this spreading model; and ranking the designed classifiers, based on their error and robustness relative to the spreading. The feature vectors used in the best classifiers are considered the best feature vectors.
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