Abstract
A multi-layered biomedical literature mining approach is presented aiming to the discovery of gene-gene correlations and the construction of respective gene networks. Utilization of the Trie-memory data structure enables efficient manipulation of different gene nomenclatures. The whole approach is coupled with a texts (biomedical abstracts) classification method. Experimental validation and evaluation results show the rationality, efficiency and reliability of the approach.
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Antonakaki, D., Kanterakis, A., Potamias, G. (2006). Biomedical Literature Mining for Text Classification and Construction of Gene Networks. In: Antoniou, G., Potamias, G., Spyropoulos, C., Plexousakis, D. (eds) Advances in Artificial Intelligence. SETN 2006. Lecture Notes in Computer Science(), vol 3955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752912_47
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DOI: https://doi.org/10.1007/11752912_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34117-8
Online ISBN: 978-3-540-34118-5
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