Abstract
The Genome of the Potyviridae virus family is usually expressed as a polyprotein which can be divided into ten proteins through the action of enzymes or proteases which cut the chain in specific places called cleavage sites. Three different techniques were employed to model each cleavage site: Hidden Markov Models (HMM), grammatical inference OIL algorithm (OIL), and Artificial Neural Networks (ANN).
Based on experimentation, the Hidden Markov Model has the best classification performance as well as a high robustness in relation to class imbalance. However, the Order Independent Language (OIL) algorithm is found to exhibit the ability to improve when models are trained using a greater number of samples without regard to their huge imbalance.
The translation for publication in English was done by John Field Palencia Roth, assistant professor in the Department of Communication and Language of the Faculty of Humanities and Social Sciences at the Pontificia Universidad Javeriana Cali. This work is funded by the Departamento Administrativo de Ciencia, Tecnología e Innovación de Colombia ( COLCIENCIAS) under the grant project code 1251-521-28290.
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Alvarez, G.I., Bravo, E., Linares, D., Vargas, J.F., Velasco, J.A. (2013). Machine Learning Techniques Applied to the Cleavage Site Prediction Problem. In: Castro, F., Gelbukh, A., González, M. (eds) Advances in Artificial Intelligence and Its Applications. MICAI 2013. Lecture Notes in Computer Science(), vol 8265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45114-0_39
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DOI: https://doi.org/10.1007/978-3-642-45114-0_39
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