Abstract
HBV (Hepatitis B Virus) infection is a severe global health problem. In recent years, the single point mutation as an essential element in the HBV evolution has been extensively studied, however, only the limited mutation loci were reported. In this paper, we proposed a new method to apply MORE (Mining Optimal Risk PattErn sets) and RPSW (Risk and Preventive Sets with Weights) algorithms to study the single point mutation loci in the HBV sequences. Experimental results show that the proposed approach is efficient to mine mutation loci, such as the reported mutation loci at positions ntT1753C, ntA1762T, ntG1764A, nt1896, and the new found mutation loci at positions ntA1436G, ntG1629A, ntA1383C, ntA1573T, and the risky of positive mutation loci at positions nt1726, nt1657, nt1463, nt1658, nt1498, nt1386. Furthermore, the proposed method is also able to find out highly relevant association rules or patterns based on the feature mutation loci.
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Zhang, Q. et al. (2012). A Study of the Single Point Mutation Loci in the Hepatitis B Virus Sequences via Optimal Risk and Preventive Sets with Weights. In: Sheng, Q.Z., Wang, G., Jensen, C.S., Xu, G. (eds) Web Technologies and Applications. APWeb 2012. Lecture Notes in Computer Science, vol 7235. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29253-8_39
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DOI: https://doi.org/10.1007/978-3-642-29253-8_39
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