Knowledge-based analysis of microarray gene expression data by using support vector machines

We introduce a method of functionally classifying genes by using gene expression data from DNA microarray hybridization experiments. The method is based on the theory of support vector machines (SVMs). SVMs are considered a supervised computer learning method because they exploit prior knowledge of gene function to identify unknown genes of similar function from expression data. SVMs avoid several problems associated with unsupervised clustering methods, such as hierarchical clustering and self-organizing maps. SVMs have many mathematical features that make them attractive for gene expression analysis, including their flexibility in choosing a similarity function, sparseness of solution when dealing with large data sets, the ability to handle large feature spaces, and the ability to identify outliers. We test several SVMs that use different similarity metrics, as well as some other supervised learning methods, and find that the SVMs best identify sets of genes with a common function using expression data. Finally, we use SVMs to predict functional roles for uncharacterized yeast ORFs based on their expression data.

The extraordinarily rapid disappearance of entoptic images.

It has been known for more than 40 years that images fade from perception when they are kept at the same position on the retina by abrogating eye movements. Although aspects of this phenomenon were described earlier, the use of close-fitting contact lenses in the 1950s made possible a series of detailed observations on eye movements and visual continuity. In the intervening decades, many investigators have studied the role of image motion on visual perception. Although several controversies remain, it is clear that images deteriorate and in some cases disappear following stabilization; eye movements are, therefore, essential to sustained exoptic vision. The time course of image degradation has generally been reported to be a few seconds to a minute or more, depending upon the conditions. Here we show that images of entoptic vascular shadows can disappear in less than 80 msec. The rapid vanishing of these images implies an active mechanism of image erasure and creation as the basis of normal visual processing.