Abstract
The selection of the distance measure to separate the objects of the knowledge space is critical in many classification algorithms. In this paper, we analyze the distance measures reported in the literature for the problem of HIV prediction. We propose a new distance for HIV viral sequences, based on the mutations with regard to the HXB2 reference sequence. In a first step, we reduce data dimensionality in order to subsequently analyze the distance measure’s performance in terms of its ability to separate classes.
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
Wilson, D.R., Martinez, T.R.: Improved Heterogeneous Distance Functions. Journal of Artificial Intelligence Research 6, 1–34 (1997)
Jolliffe, I.T.: Principal Component Analysis. Springer, New York (1986)
Cox, T., Cox, M.: Multidimensional Scaling. Chapman and Hall, Boca Raton (1994)
McQueen, J.: Some methods for classification and analysis of multivariate observations. In: Fifth Berkeley Symposium on Mathematical Statistics and Probability, pp. 182–297 (1967)
Kohonen, T.: Self-Organization and Associative Memory. Springer, New York (1989)
Cover, P.E., Hart, P.E.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory 13(1), 21–27 (1967)
Muller, K.-R., Mika, S., Ratsch, G., Tsuda, K., Scholkopf, B.: An introduction to kernel-based learning algorithms. IEEE Transactions on Neural Networks 12(2), 181–201 (2001)
Dayhoff, M.O., Schwartz, R., Orcutt, B.C.: A model of evolutionary change in proteins. Atlas of Protein Sequence and Structure 5, 345–352 (1978)
Henikoff, S., Henikoff, J.: Amino acid substitution matrices from protein blocks. Proccedings of the National Academy of Sciences of the United States of America. PNAS 89, 10915–10919 (1992)
Baldi, P., Soren, B.: Bioinformatics: The Machine Learning Approach. MIT Press, Cambridge (2001)
Bhaskar, H., Hoyle, D.C., Singh, S.: Machine learning in bioinformatics: A brief survey and recommendations for practitioners. Computers in Biology and Medicine 36(10), 1104–1125 (2006)
Stanford HIV Resistance Database Protease, http://hivdb.stanford.edu//cgi-bin/GenoPhenoDS.cgi
Miyazawa, S., Jernigan, R.L.: Residue Potentials with a Favorable Contact Pair Term and an Unfavorable High Packing Density Term, for Simulation and Threading. J. Mol. Biol. 256, 623–644 (1996)
James, R.: Predicting Human Immunodeficiency Virus Type 1 Drug Resistance from Genotype Using Machine Learning. University of Edinburgh (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bonet, I., Rodríguez, A., Grau, R., García, M.M., Saez, Y., Nowé, A. (2008). Comparing Distance Measures with Visual Methods. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_8
Download citation
DOI: https://doi.org/10.1007/978-3-540-88636-5_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88635-8
Online ISBN: 978-3-540-88636-5
eBook Packages: Computer ScienceComputer Science (R0)