Abstract
The amount of information about the genome, transcriptome and proteome, forms a problem for the scientific community: how to find the right information in a reasonable amount of time. Most research aiming to solve this problem, however, concentrate on a certain organism or a very limited dataset. Complementary to those algorithms, we developed CONAN, a system which provides a full-scale approach, tailored to experimentalists, designed to combine several information extraction methods and connect the outcome of these methods to gather novel information. Its methods include tagging of gene/protein names, finding interaction and mutation data, tagging of biological concepts, linking to MeSH and Gene Ontology terms, which can all be found back by querying the system. We present a full-scale approach that will ultimately cover all of PubMed/MEDLINE. We show that this universality has no effect on quality: our system performs as well as existing systems.
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Malik, R., Siebes, A. (2005). CONAN: An Integrative System for Biomedical Literature Mining. In: Bento, C., Cardoso, A., Dias, G. (eds) Progress in Artificial Intelligence. EPIA 2005. Lecture Notes in Computer Science(), vol 3808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11595014_25
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DOI: https://doi.org/10.1007/11595014_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30737-2
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