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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(3): 339-344 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.141

Personalized Cancer Treatment by Using Naive Bayes Classifier

Bekir Karlık and Emre Öztoprak

Abstract—Pharmacogenomics is the application of genomic technologies to drug discovery and development, as well as for the elucidation of the mechanisms of drug action on cells and organisms. DNA microarrays measure genome-wide gene expression patterns and are an important tool for pharmacogenomics applications, such as the identification of molecular targets for drugs, toxicological studies and molecular diagnostics. DNA based classification is difficult because the sequence of DNA differs from among all individuals. Genome-wide investigations generate vast amounts of data and there is a need for soft computational methods such as artificial intelligence and expert systems to manage and analyze this information. This study presents a Naive Bayes classifier to a novel approach on application of pharmacogenetics to personalized cancer treatment using data of TPMT polymorphisms.

Index Terms—Personalized medicine, pharmacogenetics; TPMT polymorphism; Naïve Bayes classifier; cancer treatment.

B. Karlık is with the Department of Computer Engineering, Engineering Faulty, Mevlana University, 42003, Selçuklu-Konya, Turkey (e-mail: bkarlik@mevlana.edu.tr).
E. Öztoprak is with the Medicine Faculty, Mevlana University, 42003, Selçuklu-Konya, Turkey (e-mail: eoztoprak@mevlana.edu.tr).

[PDF]

Cite: Bekir Karlık and Emre Öztoprak, "Personalized Cancer Treatment by Using Naive Bayes Classifier," International Journal of Machine Learning and Computing vol. 2, no. 3, pp. 339-344, 2012.

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