SUPERVISED MACHINE LEARNING MODEL FOR MICRORNA EXPRESSION DATA IN CANCER
Abstract: The cancer cell gene
expression data in general has a very large feature and requires analysis to
find out which genes are strongly influencing the specific disease for
diagnosis and drug discovery. In this paper several methods of supervised
learning (decisien tree, naïve bayes, neural network, and deep learning) are
used to classify cancer cells based on the expression of the microRNA gene to
obtain the best method that can be used for gene analysis. In this study there
is no optimization and tuning of the algorithm to test the ability of general
algorithms. There are 1881 features of microRNA gene epresi on 25 cancer
classes based on tissue location. A simple feature selection method is used to
test the comparison of the algorithm. Expreriments were conducted with various
scenarios to test the accuracy of the classification.
Keywords: Cancer, MicroRNA,
classification, Decesion Tree, Naïve Bayes, Neural Network, Deep Learning
Author: Indra Waspada, Adi
Wibowo, Noel Segura Meraz
Journal Code: jptkomputergg170014
