Abstract
Basically, one of the most important issues for identifying biological sequences is accuracy; however, since the exponential growth and excessive diversity of biological data, the requirement to compute within considerably appropriate time usually compromises with accuracy. We propose novel approaches for accurately identifying DNA sequences in shorter time by discovering sequence patterns – signatures, which are enough distinctive information for the sequence identification. The approaches are to find the best combination of n-gram patterns and six statistical scoring algorithms, which are regularly used in the research of Information Retrieval, and then employ the signatures to create a similarity scoring model for identifying the DNA. We generate two approaches to discover the signatures. For the first one, we use only statistical information extracted directly from the sequences to discover the signatures. For the second one, we use prior knowledge of the DNA in the signature discovery process. From our experiments on influenza virus, we found that: 1) our technique can identify the influenza virus at the accuracy of up to 99.69% when 11-gram is used and the prior knowledge is applied; 2) the use of too short or too long signatures produces lower efficiency; and 3) most scoring algorithms are good for identification except the “Rocchio algorithm” where its results are approximately 9% lower than the others. Moreover, this technique can be applied for identifying other organisms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Aalbersberg, I.: A Document Retrieval Model Based on Term Frequency Ranks. In: Proceedings of the 7th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 163–172 (1994)
Benson, D.A., Karsch-Mizrachi, I., Lipman, D.J., Ostell, J., Rapp, B.A., Wheeler, D.L.: GenBank. Nucleic Acids Research 28(1), 15–18 (2000)
Brown, P.F., de Souza, P.V., Della Pietra, V.J., Mercer, R.L.: Class-Based N-Gram Models of Natural Language. Computational Linguistics 18(4), 467–479 (1992)
Chuzhanova, N.A., Jones, A.J., Margetts, S.: Feature Selection for Genetic Sequence Classification. Bioinformatics Journal 14(2), 139–143 (1998)
Joachims, T.: A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. In: Proceedings of the 14th International Conference on Machine Learning, pp. 143–151 (1997)
Keinduangjun, J., Piamsa-nga, P., Poovorawan, Y.: Models for Discovering Signatures in DNA Sequences. In: Proceedings of the 3rd IASTED International Conference on Biomedical Engineering, Innsbruck, Austria, pp. 548–553 (2005)
Krauthammer, M., Rzhetsky, A., Morozov, P., Friedman, C.: Using BLAST for Identifying Gene and Protein Names in Journal Articles. Gene 259(1-2), 245–252 (2000)
Mladenic, D., Grobelnik, M.: Feature Selection for Classification Based on Text Hierarchy. In: Working Notes of Learning from Text and the Web. Conference on Automated Learning and Discovery. Carnegie Mellon University, Pittsburgh (1998)
Mladenic, D., Grobelnik, M.: Feature Selection for Unbalanced Class Distribution and Naïve Bayes. In: Proceedings of the 16th International Conference on Machine Learning, pp. 258–267 (1999)
Pearson, W.R.: Using the FASTA Program to Search Protein and DNA Sequence Databases. Methods Molecular Biology 25, 365–389 (1994)
Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34(1), 1–47 (2002)
Spitters, M.: Comparing Feature Sets for Learning Text Categorization. In: Proceedings on RIAO (2000)
Wang, J.T.L., Rozen, S., Shapiro, B.A., Shasha, D., Wang, Z., Yin, M.: New Techniques for DNA Sequence Classification. Journal of Computational Biology 6(2), 209–218 (1999)
Xu, Y., Mural, R., Einstein, J., Shah, M., Uberbacher, E.: Grail: A Multiagent Neural Network System for Gene Identification. Proceedings of IEEE 84(10), 1544–1552 (1996)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Keinduangjun, J., Piamsa-nga, P., Poovorawan, Y. (2005). Signature Recognition Methods for Identifying Influenza Sequences. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds) Artificial Intelligence in Medicine. AIME 2005. Lecture Notes in Computer Science(), vol 3581. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527770_67
Download citation
DOI: https://doi.org/10.1007/11527770_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27831-3
Online ISBN: 978-3-540-31884-2
eBook Packages: Computer ScienceComputer Science (R0)