Abstract
The increasing pace of biotechnological advances produced an unprecedented amount of both experimental data and biological information mostly diffused on the web. However, the heterogeneity of the data organization and the different knowledge representations open the ways to new challenges in the integration and the extraction of biological information fundamental for correctly interpreter experimental results.
In the present work we introduce a new methodology for quantitatively scoring the degree of biological correlation among biological terms occurring in biomedical abstracts. The proposed flow is based on the latent semantic analysis of biomedical literature coupled with the UMLS Metathesarurs and PubMed literature information. The results demonstrate that the structured and consolidated knowledge in the UMLS and pathway database efficiently improves the accuracy of the latent semantic analysis of biomedical literature.
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Abate, F., Ficarra, E., Acquaviva, A., Macii, E. (2013). Improving Latent Semantic Analysis of Biomedical Literature Integrating UMLS Metathesaurus and Biomedical Pathways Databases. In: Fred, A., Filipe, J., Gamboa, H. (eds) Biomedical Engineering Systems and Technologies. BIOSTEC 2011. Communications in Computer and Information Science, vol 273. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29752-6_13
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DOI: https://doi.org/10.1007/978-3-642-29752-6_13
Publisher Name: Springer, Berlin, Heidelberg
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