Home > Archive > 2012 > Volume 2 Number 3 (Jun. 2012) >
IJML 2012 Vol.2(3): 339-344
doi: 10.7763/IJMLC.2012.V2.141

Personalized Cancer Treatment by Using Naive Bayes Classifier

Bekir Karlık1 , Emre öztoprak2

  • 1Department of Computer Engineering, Engineering Faulty, Mevlana University, 42003, Selçuklu-Konya, Turkey.
  • 2Medicine Faculty, Mevlana University, 42003, Selçuklu-Konya, Turkey.

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.

Keywords

  • Personalized medicine
  • pharmacogenetics; TPMT polymorphism; Naïve Bayes classifier; cancer treatment
141-L0078

How to Cite

Copied

Bekir Karlık and Emre öztoprak, "Personalized Cancer Treatment by Using Naive Bayes Classifier," International Journal of Machine Learning, vol. 2, no. 3, pp. 339-344, 2012. https://doi.org/10.7763/IJMLC.2012.V2.141

Copyright & License

Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Article Metrics in Dimensions

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • DOI: 10.18178/IJML
  • Frequency: Quarterly
  • Average Days to Accept: 68 days
  • Acceptance Rate: 27%
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • DOI: 10.18178/IJML
  • Abstracing/Indexing: Google Scholar, Crossref, ProQuest, Electronic Journals Library, CNKI.
  • E-mail: editor@ijml.org
  • APC: 500USD