Image Mining from Gel Diagrams in Biomedical Publications

TL;DR

This paper presents an automated method for detecting and analyzing gel images in biomedical publications, enabling extraction of experimental data like gene names from these images.

Contribution

It introduces a novel workflow for gel image detection and initial gene name identification, advancing image mining in biomedical literature.

Findings

High accuracy in gel segment and panel detection

Feasibility demonstrated for gene name identification in gel images

Initial results support further development of image mining techniques

Abstract

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.