Title :
Predictive genotype based on phenotype using the association rules mining
Author :
Ranny ; Wasito, Ito ; Sadikin, M. ; Handhayani, Teny
Author_Institution :
Fac. of Comput. Sci., Univ. Indonesia, Depok, Indonesia
Abstract :
Genotype data is very important for many purposes since it contains such information of living organism. Unfortunately it is very hard to get the data. To get the data, it is needed a long and difficult chemical process. The purpose of this research is to develop a predictive gene sequence scheme in order to minimize the producing of the data. The scheme is made by finding the pattern of data correlation to perform a rule. The association rule mining method is used to build the rules andit can be used for genotype prediction. The prediction will be evaluated by the training and non training data on the experiment. The result of the experiment is an accuracy score of the predictive genotype. The accuracy of the training data is about 90% and the non training data is about 40%.
Keywords :
biology computing; data mining; genetics; genomics; association rules mining; data correlation pattern; genotype prediction; living organism; phenotype; predictive gene sequence scheme; predictive genotype; Accuracy; Association rules; Mice; Testing; Training; Training data;
Conference_Titel :
Advanced Computer Science and Information Systems (ICACSIS), 2013 International Conference on
Conference_Location :
Bali
DOI :
10.1109/ICACSIS.2013.6761573