Mining images in biomedical publications: Detection and analysis of gel diagrams
Identifieur interne : 000096 ( Main/Exploration ); précédent : 000095; suivant : 000097Mining images in biomedical publications: Detection and analysis of gel diagrams
Auteurs : Tobias Kuhn [Suisse] ; Mate Levente Nagy [États-Unis] ; Thaibinh Luong [États-Unis] ; Michael Krauthammer [États-Unis]Source :
- Journal of Biomedical Semantics [ 2041-1480 ] ; 2014.
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.
Url:
DOI: 10.1186/2041-1480-5-10
PubMed: 24568573
PubMed Central: 4190668
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en"><p>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.</p>
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<tree><country name="Suisse"><region name="Canton de Zurich"><name sortKey="Kuhn, Tobias" sort="Kuhn, Tobias" uniqKey="Kuhn T" first="Tobias" last="Kuhn">Tobias Kuhn</name>
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<country name="États-Unis"><region name="Connecticut"><name sortKey="Nagy, Mate Levente" sort="Nagy, Mate Levente" uniqKey="Nagy M" first="Mate Levente" last="Nagy">Mate Levente Nagy</name>
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<name sortKey="Krauthammer, Michael" sort="Krauthammer, Michael" uniqKey="Krauthammer M" first="Michael" last="Krauthammer">Michael Krauthammer</name>
<name sortKey="Krauthammer, Michael" sort="Krauthammer, Michael" uniqKey="Krauthammer M" first="Michael" last="Krauthammer">Michael Krauthammer</name>
<name sortKey="Luong, Thaibinh" sort="Luong, Thaibinh" uniqKey="Luong T" first="Thaibinh" last="Luong">Thaibinh Luong</name>
</country>
</tree>
</affiliations>
</record>
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