Mining Images in Biomedical Publications: Detection and Analysis of Gel Diagrams
This work addresses the need for automated image mining in biomedical research to extract experimental results from gel images, which are abundant and often not fully discussed in text, though it is incremental as it does not provide a complete solution.
The paper tackled the problem of automatically detecting and analyzing gel diagrams in biomedical publications, achieving high accuracy in detecting gel segments and panels and presenting preliminary results for identifying gene names in these images.
Authors of biomedical publications use gel images to report experimental results such as protein-protein interactions or protein expressions under different conditions. Gel images offer a concise way to 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 automated image mining and parsing. We introduce an approach for the detection of gel images, and present a workflow to analyze them. We are able to detect gel segments and panels at high accuracy, and present preliminary 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.