Abstract
Human papillomavirus (HPV) is considered to be the most common sexually transmitted disease and the infection of HPV is known as the major factor for cervical cancer. There are more than 100 types in HPV and each HPV has two risk types, low and high. In particular, high risk type HPV is known to the most important factors in medical judgment. Thus, the classifying the risk type of HPV is very important to the treat of cervical cancer. In this paper, we present a machine learning approach to mine the structure of HPV DNA sequence for effective classification of the HPV risk types. We learn the most informative subsequence segment sets and its weights with genetic algorithm to classify the risk types of each HPV. To resolve the problem of computational complexity of genetic algorithm we use distributed intelligent data engineering platform based on active grid concept called “IDEA@Home.” The proposed genetic mining method, with the described platform, shows about 85.6% classification accuracy with relatively fast mining speed.
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Eom, JH., Park, SB., Zhang, BT. (2004). Genetic Mining of DNA Sequence Structures for Effective Classification of the Risk Types of Human Papillomavirus (HPV). In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_208
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DOI: https://doi.org/10.1007/978-3-540-30499-9_208
Publisher Name: Springer, Berlin, Heidelberg
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