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Reseach Article

Creation and Use of Ontology Related to Genes, Syndromes, Diseases and Symptoms for the Classification of Biomedical Texts

by C. Perez De Celis, Fatima Ronquillo, Emilio Salceda
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 54 - Number 15
Year of Publication: 2012
Authors: C. Perez De Celis, Fatima Ronquillo, Emilio Salceda
10.5120/8644-2537

C. Perez De Celis, Fatima Ronquillo, Emilio Salceda . Creation and Use of Ontology Related to Genes, Syndromes, Diseases and Symptoms for the Classification of Biomedical Texts. International Journal of Computer Applications. 54, 15 ( September 2012), 32-37. DOI=10.5120/8644-2537

@article{ 10.5120/8644-2537,
author = { C. Perez De Celis, Fatima Ronquillo, Emilio Salceda },
title = { Creation and Use of Ontology Related to Genes, Syndromes, Diseases and Symptoms for the Classification of Biomedical Texts },
journal = { International Journal of Computer Applications },
issue_date = { September 2012 },
volume = { 54 },
number = { 15 },
month = { September },
year = { 2012 },
issn = { 0975-8887 },
pages = { 32-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume54/number15/8644-2537/ },
doi = { 10.5120/8644-2537 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:55:47.056314+05:30
%A C. Perez De Celis
%A Fatima Ronquillo
%A Emilio Salceda
%T Creation and Use of Ontology Related to Genes, Syndromes, Diseases and Symptoms for the Classification of Biomedical Texts
%J International Journal of Computer Applications
%@ 0975-8887
%V 54
%N 15
%P 32-37
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This research focuses on analyzing and classifying biomedical articles in the field of neuroscience, with a particular emphasis on scientific articles related to hearing loss. To carry out this task in a more efficient manner, resources as the elimination of stopwords were used. As well, it was implemented the n-gram-based text categorization system along with the use of a domain ontology related with genes, diseases and syndromes, obtaining promising results.

References
  1. Ronquillo, Fátima-Itzel; Pérez de Celis, Concepción; Sierra , Gerardo; da Cunha , Iria; Torres-Moreno, Juan-Manuel (2011). «Automatic classification of biomedical texts: experiments with a hearing loss corpus». En Ding, Yongsheng; Peng, Yonghong; Shi, Riyi; Hao, Kuangrong; Wang, Lipo (eds. ). 4th International Conference on Biomedical Engineering and Informatics,BMEI 2011. 1674-1679. Shanghai, China: IEEE. ISBN 978-1-4244-9351-7
  2. Amari S. , N. Murata, K. R. Muller, M. Finke y H. H. Yang. 1997. Asymptotic statistical theory of overtraining and cross-validation. IEEE Transactions on Neural Networks, 8(5):985-996.
  3. Hall. M. , Eibe F. , Holmes G. , Pfahringer B. , Reutemann P. y Witten I. H. 2009. The WEKA Data Mining Software: An Up-date. SIGKDD Explorations, 11(1):10-18.
  4. Hmway Hmway Tar, Thi Thi Soe Nyunt. 2011. Enhancing Traditional Text Documents Clustering based on Ontology, International Journal of Computer Applications (0975 – 8887) Volume 33– No. 10, November 2011, pp. 38-42.
  5. G. Bharathi, D. Venkatesan. 2012. Study of Ontology or Thesaurus based Document Clustering and Information Retrieval, Journal of Theoretical and Applied Information Technology, 15 June 2012. Vol. 40 No. 1, Pags. 55-61.
  6. Stephan Bloehdorn, Philipp Cimiano, and Andreas Hotho. 2006. Learning Ontologies to Improve Text Clustering and Classification, From Data and Information Analysis to Knowledge Engineering Studies in Classification, Data Analysis, and Knowledge Organization 2006, pp 334-341.
  7. Irena Spasic, Sophia Ananiadou, John McNaught and Anand Kumar. 2005. Text mining and ontologies in biomedicine: Making sense of raw text, Henry Stewart Publications 1467-5463. Briefings in Bioinformatics. Vol 6. No 3. 239–251. September 2005, pp. . 239-251.
  8. Alexander Maedche and Ste_en Staab. 2000. Mining Ontologies from Text, R. Dieng and O. Corby (Eds. ): EKAW 2000, LNAI 1937, pp. 189-202, Springer-Verlag Berlin Heidelberg 2000.
  9. N. Dragu, F. Elkhoury, T. Miyazaki, R. A. Morelli, and N. D. Tada, 2010. Ontology-Based Text Mining for Predicting Disease Outbreaks. ;In Proceedings of FLAIRS Conference. 2010.
  10. Protégé, http://protege. stanford. edu/
  11. S. Bloehdorn and P. Cimiano and A. Hotho and S. Staab. 2005. An Ontology-based Framework for Text Mining, LDV Forum - GLDV Journal for Computational Linguistics and Language Technology, Vol. 20, Nr. 1 (May 2005) , pp. 87-112.
  12. Genetics Home Reference, http://ghr. nlm. nih. gov/
  13. HUGO Gene Nomenclature Committee (HGNC), http://www. genenames. org/
Index Terms

Computer Science
Information Sciences

Keywords

Multi-cataloguing n-grams of letters ontologies hearing loss genes