This kind of “persistent” vector is known in mathematics as an eigenvector (eigen being the German word for “characteristic”). Eigenvectors have appeared in numerous contexts over the centuries. The concept (though not the terminology) first arose in the work of the 18th century mathematician Leonhard Euler on the rotation of solid bodies. Because any rotation in space must have an axis—a line that persists in the same direction throughout the rotation—Euler recognized that the axis and angle of rotation characterize the rotation (which justifies the term “characteristic,” at least in this context).

Fast forward a century or so, and you find eigenvectors used again in quantum physics. The motion of electrons is described by Schrödinger’s equation, formulated in 1926 by Austrian physicist Erwin Schrödinger. They do not orbit atomic nuclei in circles or ellipses in the way that planets orbit the Sun. Instead, their orbits form complicated three-dimensional shapes that are determined by the eigenvectors of Schrödinger’s equation. By counting the number of these solutions, you can tell how many electrons fit in each energy level or orbital of an atom, and in this way you can start to explain the patterns and periodicities of the periodic table.

Fast forward again to the present, and you can find the same concept used in genomics. Imagine that you have a large array of data; for example, the level of activity of 3,000 genes in a cell at 20 different times. Although the cell has thousands of genes, it does not have that many biologically meaningful processes. Some of the genes may work together to repel an invader. Other genes may be involved in cell division or metabolism. But the rest may not be doing much of anything, at least while you are watching them; their activity just amounts to random noise. The eigenvectors of the data set correspond to the most relevant patterns in the data, those which persist through the noise of chance variation. Figure 2 (on page 10) shows networks of genes found using eigenvectors. One eigenvector (the term “eigengene” has even been coined here) might correspond to genes that control metabolism. Another might consist of genes activated during cell division. The mathematics identifies the gene networks that appear most tied to biological activity, but it cannot tell what the networks do. That is up to the biologist.

Singular value decomposition (SVD) is a purely mathematical technique to pick out characteristic features in a giant array of data by finding eigenvectors. The idea is something like this: First you look for the one vector that most closely matches all of the rows of data in the array; that is the first eigenvector. Then you look for a second vector that most closely matches the residual variations after the first eigenvector has been subtracted out. This is the second eigenvector. The process can, of course, be repeated. For the PageRank example, only the first eigenvector is used. But in other applications, such as genomics, more than one eigenvector may be biologically significant.

Given the general applicability of eigenvector approaches, perhaps it is not too surprising that Google’s PageRank—an algorithm that involves no actual understanding of your search query—could rank Web sites better than algorithms that attempted to



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