IJCATR Volume 5 Issue 8

A Gene Structure Prediction Model using Bayesian Algorithm and the Nearest Neighbor

Elham Naseh Ali Asghar Safaee
10.7753/IJCATR0508.1002
keywords : Gene structure prediction, nearest neighbor algorithm, Bayesian algorithm, blended learning methods, genetic disease diagnosis.

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Basically genetic disorders include general problems and issues that are caused by the failure of one or more of the genome, and usually appear at birth; although they sometimes occur later. Genetic diseases may not be inherited and they may be caused as new mutations in the genome of embryos. Like many other diseases, diagnosis, treatment, and prognosis of genetic diseases is very important and sometimes complex. One of the best ways of treating genetic diseases is its diagnosis in a fetus. All gene structures of a fetus should be available in order to diagnose genetic disease. This structure can be achieved when the fetus is seven months and there are just 5% to 30% of gene sequence structure before seven months. To solve this shortcoming and fix the obstacle in the diagnosis of diseases, 5 to 30% of the gene sequence structure of the whole structure of the fetus is predicted with the help of parents’ gene structure. In previous studies, gene structure prediction using machine learning algorithms has achieved a maximum accuracy of 95%.
@artical{e582016ijcatr05081002,
Title = "A Gene Structure Prediction Model using Bayesian Algorithm and the Nearest Neighbor",
Journal ="International Journal of Computer Applications Technology and Research(IJCATR)",
Volume = "5",
Issue ="8",
Pages ="509 - 515",
Year = "2016",
Authors ="Elham Naseh Ali Asghar Safaee"}
  • In this research in A Gene Structure Prediction.
  • Data mining, decision trees and neural network techniques have been investigated.
  • In the simulation, a performance evaluation of the algorithm is carried out.
  • In this research the criteria used to assess the accuracy.