2014/01/01 by Tobias Kuhn, Mate Nagy, Mate Levente Nagy +2
Biochemistry, Genetics and Molecular Biology · Computer Science · #Biomedical Text Mining and Ontologies #Cell Image Analysis Techniques #Identification (biology) #Image (mathematics) #Image Retrieval and Classification Techniques #Image processing #Pattern recognition (psychology) #Prime (order theory) #Workflow #cs.IR
paper · pdf · doi:10.1186/2041-1480-5-10
published as Journal of Biomedical Semantics 2014, 5:10 · arXiv admin note: substantial text overlap with arXiv:1209.1481
openalex publication_date 2014/01/01 · arxiv created 2014/02/10 · arxiv updated 2014/03/06 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
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.