Butte and his Harvard co-author, Isaac Kohane, MD, PhD, used computer programs to automatically categorize the tens of thousands of microarray experiments in a single database based on the terms, or concepts, used by the submitter to describe the experiment. They then looked for findings shared by several experiments with similar concepts, such as tissue type, for example. Comparing results from many similar experiments allowed them to identify correlations that may not be statistically significant in just one experiment.
Butte and Kohane identified several previously unknown correlations: nine genes whose expression increased or decreased significantly with aging, two genes that are highly expressed in response to injury, and another gene in which the expression drops significantly in leukemic cells. They also confirmed these relationships by studying genes known to be associated with muscle tissue in both humans and mice.
Their classification system was stymied, however, when scientists included too much or too little information in the text annotations, or used imprecise words such as "pool," which can mean either a body of water or the action of combining the contents of two or more tubes.
"As a community, we've standardized the way the data itself is represented," said Butte, "but there are no formal requirements for the accompanying textual descriptions of this data. Sometimes people seem to almost copy and paste their entire scientific paper into the text box. We need to clean up our annotations because now we're showing that they have value."
Butte and Kohane favor using the existing Unified Medical Language System, which consists of more than 1 million biomedical concepts, to vastly simplify the computerized sorting of the thousands of microarray exper
Source:Stanford University Medical Center