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Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on as a courtesy and upon agreement with the previous journal publisher.
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Classification of Helicobacter Pylori according to National Strains using Bayesian Learning

Fatih University, Department of Computer Engineering, Istanbul, Turkey
Authors to whom correspondence should be addressed.
Math. Comput. Appl. 2009, 14(3), 241-251;
Published: 1 December 2009
There is many studies about Helicobacter pylori genome and many instances of national strains are sequenced completely. To make a successful classification, the same functional portions have to be used a classifier like Bayesian Learning. Thus suitable genes will be used for classification since genes are portions that work functionally same. The cagA and vacA genes are selected for classification. cagA gene stands for ‘cytotoxin-associated protein A’ gene and vacA gene stands for ‘vacuolating cytotoxin precursor’ gene and these genes have a role of coding of these proteins. The reasons for selecting these genes are that these genes are the genes which affect the bacteria being a pathogen, Nucleotide numbers of these genes are higher than the most of other genes of bacteria, and these genes are the most popular genes of Helicobacter pylori. There are some instances of these genes which are classified with respect to national strains. The national strains are based on the nation of the host human. The cause of the difference on national strains is the difference of the cultural activities. If the national strain of a random Helicobacter pylori bacterium is known, the host human's nation can also be known approximately. The aim of this study is to classify Helicobacter pylori according to National strain using a well-know classification technique named Bayesian Learning.
Keywords: Helicobacter pylori; Bayesian Learning; National strain Helicobacter pylori; Bayesian Learning; National strain
MDPI and ACS Style

Karlık, B.; Avcı, A.; Yabanıgül, A.T. Classification of Helicobacter Pylori according to National Strains using Bayesian Learning. Math. Comput. Appl. 2009, 14, 241-251.

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