  Authors of biomedical publications often use gel images to report
experimental results such as protein-protein interactions or protein
expressions under different conditions. Gel images offer a way to concisely
communicate such findings, not all of which need to be explicitly discussed in
the article text. This fact together with the abundance of gel images and their
shared common patterns makes them prime candidates for image mining endeavors.
We introduce an approach for the detection of gel images, and present an
automatic workflow to analyze them. We are able to detect gel segments and
panels at high accuracy, and present first results for the identification of
gene names in these images. While we cannot provide a complete solution at this
point, we present evidence that this kind of image mining is feasible.
