Published in

BioMed Central, Journal of Biomedical Semantics, 1(5), p. 10

DOI: 10.1186/2041-1480-5-10

Links

Tools

Export citation

Search in Google Scholar

Mining images in biomedical publications: Detection and analysis of gel diagrams

Journal article published in 2014 by Tobias Kuhn ORCID, Mate Levente Nagy, ThaiBinh Luong, Michael Krauthammer
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Upload
Green circle
Postprint: archiving allowed
Upload
Green circle
Published version: archiving allowed
Upload
Policy details
Data provided by SHERPA/RoMEO

Abstract

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.