Image Mining from Gel Diagrams in Biomedical Publications
This work addresses the challenge of mining data from abundant gel images in biomedical research, though it is incremental as it provides a proof-of-concept rather than a complete solution.
The paper tackled the problem of extracting information from gel images in biomedical publications by developing an approach for detecting gel images and analyzing them automatically, achieving high accuracy in detecting gel segments and panels and presenting initial results for identifying gene names.
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.